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Top 10 Hadoop Commands [With Usages]
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In this era, with huge chunks of data, it becomes essential to deal with them. The data springing from organizations with growing customers is way larger than any traditional data management tool can store. It leaves us with the question of managing larger sets of data, which could range from gigabytes to petabytes, without using a single large computer or traditional data management tool. This is where the Apache Hadoop framework grabs the spotlight. Before diving into Hadoop command implementation, let’s briefly comprehend the Hadoop framework and its importance. What is Hadoop? Hadoop is commonly used by major business organizations to solve various problems, from storing large GBs (Gigabytes) of data every day to computing operations on the data. Traditionally defined as an open-source software framework used to store data and processing applications, Hadoop stands out quite heavily from the majority of traditional data management tools. It improves the computing power and extends the data storage limit by adding a few nodes in the framework, making it highly scalable. Besides, your data and application processes are protected against various hardware failures. Hadoop follows a master-slave architecture to distribute and store data using MapReduce and HDFS. As depicted in the figure below, the architecture is tailored in a defined manner to perform data management operations using four primary nodes, namely Name, Data, Master, and Slave. The core components of Hadoop are built directly on top of the framework. Other components integrate directly with the segments. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Source Hadoop Commands Major features of the Hadoop framework show a coherent nature, and it becomes more user-friendly when it comes to managing big data with learning Hadoop Commands. Below are some convenient Hadoop Commands that allow performing various operations, such as management and HDFS clusters file processing. This list of commands is frequently required to achieve certain process outcomes. Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses 1. Hadoop Touchz hadoop fs -touchz /directory/filename This command allows the user to create a new file in the HDFS cluster. The “directory” in the command refers to the directory name where the user wishes to create the new file, and the “filename” signifies the name of the new file which will be created upon the completion of the command. 2. Hadoop Test Command  hadoop fs -test -[defsz] <path> This particular command fulfills the purpose of testing the existence of a file in the HDFS cluster. The characters from “[defsz]” in the command have to be modified as needed. Here is a brief description of these characters: d -> Checks if it is a directory or not e -> Checks if it is a path or not f -> Checks if it is a file or not s -> Checks if it is an empty path or not r -> Checks the path existence and read permission w -> Checks the path existence and write permission z -> Checks the file size In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses 3. Hadoop Text Command hadoop fs -text <src> The text command is particularly useful to display the allocated zip file in text format. It operates by processing source files and providing its content into a plain decoded text format. 4. Hadoop Find Command hadoop fs -find <path> … <expression> This command is generally used for the purpose to search for files in the HDFS cluster. It scans the given expression in the command with all the files in the cluster, and displays the files that match the defined expression. Read: Top Hadoop Tools 5. Hadoop Getmerge Command hadoop fs -getmerge <src> <localdest> Getmerge command allows merging one or multiple files in a designated directory on the HDFS filesystem cluster. It accumulates the files into one single file located in the local filesystem. The “src” and “localdest” represents the meaning of source-destination and local destination. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? 6. Hadoop Count Command hadoop fs -count [options] <path> As obvious as its name, the Hadoop count command counts the number of files and bytes in a given directory. There are various options available that modify the output as per the requirement. These are as follows: q -> quota shows the limit on the total number of names and usage of space u -> displays only quota and usage h -> gives the size of a file v -> displays header 7. Hadoop AppendToFile Command hadoop fs -appendToFile <localsrc> <dest> It allows the user to append the content of one or many files into a single file on the specified destination file in the HDFS filesystem cluster. On execution of this command, the given source files are appended into the destination source as per the given filename in the command. 8. Hadoop ls Command hadoop fs -ls /path The ls command in Hadoop shows the list of files/contents in a specified directory, i.e., path. On adding “R” before /path, the output will show details of the content, such as names, size, owner, and so on for each file specified in the given directory. 9. Hadoop mkdir Command hadoop fs -mkdir /path/directory_name This command’s unique feature is the creation of a directory in the HDFS filesystem cluster if the directory does not exist. Besides, if the specified directory is present, then the output message will show an error signifying the directory’s existence. 10. Hadoop chmod Command hadoop fs -chmod [-R] <mode> <path> This command is used when there is a need to change the permissions to accessing a particular file. On giving the chmod command, the permission of the specified file is changed. However, it is important to remember that the permission will be modified when the file owner executes this command. Hadoop Developer Salary Insights Salary Based on Location City Average Annual Salary Bangalore ₹8 Lakhs New Delhi ₹7 Lakhs Mumbai ₹8.2 Lakhs Hyderabad ₹7.8 Lakhs Pune ₹7.9 Lakhs Chennai ₹8.1 Lakhs Kolkata ₹7.5 Lakhs Salary Based on Experience Experience(Years) Average Annual Salary 0-2 ₹4.5 Lakhs 3 ₹6 Lakhs 4 ₹7.4 Lakhs 5 ₹8.5 Lakhs 6 ₹9.9 Lakhs Salary Based on Company Type Company Type Average Annual Salary Forbes Global 2000 ₹10.7 Lakhs Public ₹10.6 Lakhs Fortune India 500 ₹9.3 Lakhs MNCs ₹ 5.8 Lakhs – ₹ 7.4 Lakhs Startups ₹ 6.3 Lakhs – ₹ 8.1 Lakhs Also Read: Impala Hadoop Tutorial Conclusion Beginning with the important issue of data storage faced by the major organizations in today’s world, this article discussed the solution for limited data storage by introducing Hadoop and its impact on carrying out data management operations by using Hadoop commands. For beginners in Hadoop, an overview of the framework is described along with its components and architecture. After reading this article, one can easily feel confident about their knowledge in the aspect of the Hadoop framework and its applied commands. upGrad’s Exclusive PG Certification in Big Data: upGrad offers an industry-specific 7.5 months program for PG Certification in Big Data where you will organize, analyze, and interpret Big Data with IIIT-Bangalore. Designed carefully for working professionals, it will help the students gain practical knowledge and foster their entry into Big Data roles. Program Highlights: Learning relevant languages and tools Learning advanced concepts of Distributed Programming, Big Data Platforms, Database, Algorithms, and Web Mining An accredited certificate from IIIT Bangalore Placement assistance to get absorbed in top MNCs 1:1 mentorship to track your progress & assisting you at every point Working on Live projects and assignments Eligibility: Math/Software Engineering/Statistics/Analytics background Check our other Software Engineering Courses at upGrad.
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by Rohit Sharma

12 Apr 2024

Characteristics of Big Data: Types & 5V’s
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Introduction The world around is changing rapidly, we live a data-driven age now. Data is everywhere, from your social media comments, posts, and likes to your order and purchase data on the e-commerce websites that you visit daily. Your search data is used by the search engines to enhance your search results. For large organizations, this data is in the form of customer data, sales figures, financial data, and much more. You can imagine how much data is produced every second! Huge amounts of data are referred to as Big Data.  Check out our free courses to get an edge over the competition. Let us start with the basics concepts of Big Data and further proceed to list out and discuss the characteristics of big data. Read: Big data career path What is Big Data? Big Data refers to the huge collections of data that are structured and unstructured. This data may be sourced from servers, customer profile information, order and purchase data, financial transactions, ledgers, search history, and employee records. In large companies, this data collection is continuously growing with time. But the amount of data a company has is not important, but what it is doing with that data. Companies aim to analyze these huge collections of data properly to gain insights. The analysis helps them in understanding patterns in the data that eventually lead to better business decisions. All this helps in reducing time, efforts, and costs. But this humongous amount of data cannot be stored, processed, and studied using traditional methods of data analysis. Hence companies hire data analysts and data scientists who write programs and develop modern tools. Learn more about big data skills one needs to develop. Characteristics of Big data with examples will help you understand the various characteristics properly. Many Big Data characteristics have been discussed below precisely: Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses Types of Big Data Big Data is present in three basic forms. They are –  1. Structured data As the name suggests, this kind of data is structured and is well-defined. It has a consistent order that can be easily understood by a computer or a human. This data can be stored, analyzed, and processed using a fixed format. Usually, this kind of data has its own data model. You will find this kind of data in databases, where it is neatly stored in columns and rows. Two sources of structured data are: Machine-generated data – This data is produced by machines such as sensors, network servers, weblogs, GPS, etc.  Human-generated data – This type of data is entered by the user in their system, such as personal details, passwords, documents, etc. A search made by the user, items browsed online, and games played are all human-generated information. For example, a database consisting of all the details of employees of a company is a type of structured data set. Learn: Mapreduce in big data 2. Unstructured data Any set of data that is not structured or well-defined is called unstructured data. This kind of data is unorganized and difficult to handle, understand and analyze. It does not follow a consistent format and may vary at different points of time. Most of the data you encounter comes under this category. For example, unstructured data are your comments, tweets, shares, posts, and likes on social media. The videos you watch on YouTube and text messages you send via WhatsApp all pile up as a huge heap of unstructured data. 3. Semi-structured data This kind of data is somewhat structured but not completely. This may seem to be unstructured at first and does not obey any formal structures of data models such as RDBMS. For example, NoSQL documents have keywords that are used to process the document. CSV files are also considered semi-structured data. After learning the basics and the characteristics of Big data with examples, now let us understand the features of Big Data. Read: Why to Become a Big Data Developer? Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Characteristics of Big Data There are several characteristics of Big Data with example. The primary characteristics of Big Data are – 1. Volume Volume refers to the huge amounts of data that is collected and generated every second in large organizations. This data is generated from different sources such as IoT devices, social media, videos, financial transactions, and customer logs. Storing and processing this huge amount of data was a problem earlier. But now distributed systems such as Hadoop are used for organizing data collected from all these sources. The size of the data is crucial for understanding its value. Also, the volume is useful in determining whether a collection of data is Big Data or not. Data volume can vary. For example, a text file is a few kilobytes whereas a video file is a few megabytes. In fact, Facebook from Meta itself can produce an enormous proportion of data in a single day. Billions of messages, likes, and posts each day contribute to generating such huge data. The global mobile traffic was tallied to be around 6.2 ExaBytes( 6.2 billion GB) per month in the year 2016. Also read: Difference Between Big Data and Hadoop 2. Variety Another one of the most important Big Data characteristics is its variety. It refers to the different sources of data and their nature. The sources of data have changed over the years. Earlier, it was only available in spreadsheets and databases. Nowadays, data is present in photos, audio files, videos, text files, and PDFs. The variety of data is crucial for its storage and analysis.  A variety of data can be classified into three distinct parts: Structured data Semi-Structured data Unstructured data 3. Velocity This term refers to the speed at which the data is created or generated. This speed of data producing is also related to how fast this data is going to be processed. This is because only after analysis and processing, the data can meet the demands of the clients/users. Massive amounts of data are produced from sensors, social media sites, and application logs – and all of it is continuous. If the data flow is not continuous, there is no point in investing time or effort on it. As an example, per day, people generate more than 3.5 billion searches on Google. Check out big data certifications at upGrad 4. Value Among the characteristics of Big Data, value is perhaps the most important. No matter how fast the data is produced or its amount, it has to be reliable and useful. Otherwise, the data is not good enough for processing or analysis. Research says that poor quality data can lead to almost a 20% loss in a company’s revenue.  Data scientists first convert raw data into information. Then this data set is cleaned to retrieve the most useful data. Analysis and pattern identification is done on this data set. If the process is a success, the data can be considered to be valuable. Knowledge Read: Big data jobs & Career planning 5. Veracity This feature of Big Data is connected to the previous one. It defines the degree of trustworthiness of the data. As most of the data you encounter is unstructured, it is important to filter out the unnecessary information and use the rest for processing. Read: Big data jobs and its career opportunities Veracity is one of the characteristics of big data analytics that denotes data inconsistency as well as data uncertainty. As an example, a huge amount of data can create much confusion on the other hand, when there is a fewer amount of data, that creates inadequate information. Other than these five traits of big data in data science, there are a few more characteristics of big data analytics that have been discussed down below: 1. Volatility  One of the big data characteristics is Volatility. Volatility means rapid change. And Big data is in continuous change. Like data collected from a particular source change within a span of a few days or so. This characteristic of Big Data hampers data homogenization. This process is also known as the variability of data. 2. Visualization  Visualization is one more characteristic of big data analytics. Visualization is the method of representing that big data that has been generated in the form of graphs and charts. Big data professionals have to share their big data insights with non-technical audiences on a daily basis. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Fundamental fragments of Big Data Let’s discuss the diverse traits of big data in data science a bit more in detail! Ingestion- In this step, data is gathered as well as processed. The process further extends when data is collected in batches or streams, and thereafter it is cleansed and organized to be finally prepared. Storage- After the collection of the required data, it is needed to be stored. Data is mainly stored in a data warehouse or data lake. Analysis- In this process, big data is processed to abstract valuable insights. There are four types of big data analytics: prescriptive, descriptive, predictive, and diagnostic. Consumption – This is the last stage of the big data process. The data insights are shared with non-technical audiences in the form of visualization or data storytelling. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Advantages and Attributes of Big Data  Big Data has emerged as a critical component of modern enterprises and sectors, providing several benefits and distinguishing itself from traditional data processing methods. The capacity to gather and interpret massive volumes of data has profound effects on businesses, allowing them to prosper in an increasingly data-driven environment.  Big Data characteristics come with several advantages. Here we have elucidated some of the advantages that explain the characteristics of Big Data with real-life examples:-  Informed Decision-Making: Big Data allows firms to make data-driven decisions. It helps businesses analyse huge amounts of data and can get important insights into consumer behaviour, market trends, and operational efficiency. This educated decision-making can result in better outcomes and a competitive advantage in the market. Improved Customer Experience: Big Data and its characteristics help in understanding customer data enabling companies to better understand consumer preferences, predict requirements, and personalise services. This results in better client experiences, increased satisfaction, and higher customer retention. Enhanced Operational Efficiency: The different features of Big Data analytics assist firms in optimizing their operations by finding inefficiencies and bottlenecks. This results in cheaper operations, lower costs, and improved overall efficiency. Product Development and Innovation: The 7 characteristics of Big Data offer insights that help stimulate both of these processes. Understanding market demands and customer preferences enables firms to produce new goods or improve existing ones in order to remain competitive. Risk Management: Various attributes of Big Data help by analysing massive databases, firms can identify possible hazards and reduce them proactively. Whether in financial markets, cybersecurity, or supply chain management, Big Data analytics aids in the effective prediction and control of risks. Personalised Marketing: By evaluating consumer behaviour and preferences, Big Data characteristics allow for personalised marketing techniques. This enables firms to design targeted marketing efforts, which increases the likelihood of turning leads into consumers with the help of Big Data and its characteristics.  Healthcare Advancements: Attributes of Big Data are being employed to examine patient information, medical history, and treatment outcomes. This contributes to customised therapy, early illness identification, and overall advances in healthcare delivery. Scientific Research and Discovery: Big Data is essential in scientific research because it allows researchers to evaluate massive datasets for patterns, correlations and discoveries. This is very useful in areas such as genetics, astronomy, and climate study. Real-time Analytics: Big Data characteristics and technologies enable businesses to evaluate and react to data in real-time. This is especially useful in areas such as banking, where real-time analytics may be used to detect fraud and anticipate stock market trends. Competitive Advantage: Businesses that properly use Big Data have a competitive advantage. Those who can quickly and efficiently assess and act on data insights have a higher chance of adapting to market changes and outperforming the competition. Application of Big Data in the Real World  The use of Big Data in the real world has become more widespread across sectors, affecting how businesses operate, make decisions, and engage with their consumers. Here, we look at some of the most famous Big Data applications in several industries. Healthcare  Predictive Analysis: Predictive analytics in healthcare uses Big Data to forecast disease outbreaks, optimise resource allocation, and enhance patient outcomes. Large datasets can be analysed to assist in uncovering trends and forecast future health hazards, allowing for proactive and preventative treatments. Personalised Medicine: Healthcare practitioners may adapt therapy to each patient by examining genetic and clinical data. Big Data facilitates the detection of genetic markers, allowing physicians to prescribe drugs and therapies tailored to a patient’s genetic composition. Electronic Health Records (EHR): The use of electronic health records has resulted in a massive volume of healthcare data. Big Data analytics is critical for processing and analyzing this information in order to improve patient care, spot patterns, and manage healthcare more efficiently. Finance Financial Fraud Detection: Big Data is essential to financial business’s attempts to identify and stop fraud. Real-time transaction data analysis identifies anomalous patterns or behaviours, enabling timely intervention to limit possible losses. Algorithmic Trading: Big Data is employed in financial markets to evaluate market patterns, news, and social media sentiment. Algorithmic trading systems use this information to make quick and educated investment decisions while optimizing trading methods. Credit Scoring and Risk Management: Big Data enables banks to more properly assess creditworthiness. Lenders can make more educated loan approval choices and manage risks by examining a wide variety of data, including transaction history, social behaviour, and internet activity. Retail  Customer Analytics: Retailers leverage Big Data to study customer behaviour, preferences, and purchasing history. This data is useful for establishing tailored marketing strategies, boosting inventory management, and improving the overall customer experience. Supply Chain Optimisation: Big Data analytics is used to improve supply chain operations by anticipating demand, enhancing logistics, and reducing delays. This ensures effective inventory management and lowers costs across the supply chain. Price Optimisation: Retailers use Big Data to dynamically modify prices depending on demand, rival pricing, and market trends. This allows firms to determine optimal pricing that maximises earnings while maintaining competition. Manufacturing  Predictive Maintenance: Big data is used in manufacturing to make predictions about the maintenance of machinery and equipment. Organisations can mitigate downtime by proactively scheduling maintenance actions based on sensor data and previous performance. Quality Control: Analysing data from the manufacturing process enables producers to maintain and enhance product quality. Big Data technologies understand patterns and abnormalities, enabling the early discovery and rectification of errors throughout the production process. Supplier Chain Visibility: Big Data gives firms complete visibility into their supplier chains. This insight aids in optimum utilisation of inventory, improved supplier collaboration, and on-time manufacturing and delivery. Telecommunications  Network Optimisation: Telecommunications businesses employ Big Data analytics to improve network performance. This involves examining data on call patterns, network traffic, and user behaviour to improve service quality and find opportunities for infrastructure enhancement. Customer Churn Prediction: By examining customer data, telecom companies can forecast which customers are likely to churn. This enables focused retention measures, such as tailored incentives or enhanced customer service, to help lessen turnover. Fraud Prevention: Big Data can help detect and prevent fraudulent activity in telecommunications, such as SIM card cloning and subscription fraud. Analysing trends and finding abnormalities aids in real-time fraud detection. Job Opportunities with Big Data  The Big Data employment market is varied, with possibilities for those with talents ranging from data analysis and machine learning to database administration and cloud computing. As companies continue to understand the potential of Big Data, the need for qualified people in these jobs is projected to remain high, making it an interesting and dynamic industry for anyone seeking a career in technology and analytics. Data Scientist: Data scientists use big data to uncover patterns and insights that are significant. They create and execute algorithms, analyse large databases, and present results to help guide decision-making. Data Engineer: The primary responsibility of a data engineer is to plan, build, and manage the infrastructure (such as warehouses and data pipelines) required for the effective processing and storing of massive amounts of data. Big Data Analysts: They interpret data to assist businesses in making educated decisions. They employ statistical approaches, data visualisation, and analytical tools to generate meaningful insights from large datasets. Machine Learning Engineer: By analysing large amounts of data using models and algorithms, machine learning engineers can build systems that are capable of learning and making judgments without the need for explicit programming. Database Administrator: Database administrators look after and administer databases, making sure they are scalable, secure, and function well. Administrators that work with Big Data often rely on distributed databases envisioned to manage large volumes of data. Business Intelligence (BI) Developer: BI developers construct tools and systems for collecting, interpreting, and presenting business information. They play an important role in converting raw data into usable insights for decision-makers. Data Architect: Data architects create the general architecture and structure of data systems, making sure that they satisfy the requirements of the company and follow industry best practices. Hadoop Developer: Hadoop developers work with tools such as HDFS, MapReduce, and Apache Spark. They create and execute solutions for processing and analyzing huge data collections. Data Privacy Analyst: With the growing significance of data privacy, analysts in this profession are responsible for ensuring that firms follow data protection legislation and apply appropriate privacy safeguards. IoT Data Analyst: Internet of Things (IoT) data analysts work with and analyse data created by IoT devices, deriving insights from massive volumes of sensor data collected in a variety of businesses. Cloud Solutions Architect: As enterprises transition to cloud platforms, cloud solutions architects develop and deploy Big Data solutions on cloud infrastructure to ensure scalability, dependability, and cost efficiency. Cybersecurity Analyst (Big Data): Experts in Big Data analyse enormous amounts of data to identify and address security issues. They employ advanced analytics to detect patterns suggestive of cyberattacks. Conclusion Big Data is the driving force behind major sectors such as business, marketing, sales, analytics, and research. It has changed the business strategies of customer-based and product-based companies worldwide. Thus, all the Big Data characteristics have to be given equal importance when it comes to analysis and decision-making. In this blog, we tried to list out and discuss the characteristics of big data, which, if grasped accurately, can fuel you to do wonders in the field of big data! If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career.
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by Rohit Sharma

04 Mar 2024

50 Must Know Big Data Interview Questions and Answers 2024: For Freshers & Experienced
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Introduction The demand for potential candidates is increasing rapidly in the big data technologies field. There are plenty of opportunities in this field if you aspire to be part of this domain. The most fruitful domains under big data technologies are data analytics, data science, big data engineering, and so on. For getting success in admission in big data, it is crucial to understand what kind of questions are asked in the interviews and how to answer them. This article will help you to find a direction for the preparation of big data testing interview questions and will increase your chances of selection. Attending a big data interview and wondering what are all the questions and discussions you will go through? Before attending a big data interview, it’s better to have an idea of the type of big data interview questions so that you can mentally prepare answers for them. To help you out, I have created the top big data interview questions and answers guide to understand the depth and real-intend of big data interview questions. Check out our free courses to get an edge over the competition. You won’t belive how this Program Changed the Career of Students Check out the scope of a career in big data. We’re in the era of Big Data and analytics. With data powering everything around us, there has been a sudden surge in demand for skilled data professionals. Organizations are always on the lookout for upskilled individuals who can help them make sense of their heaps of data. The number of jobs in data science is predicted to grow by 30% by 2026. This means there will be many more employment opportunities for people working with data. To make things easier for applicants and candidates, we have compiled a comprehensive list of big data interview questions.  The keyword here is ‘upskilled’ and hence Big Data interviews are not really a cakewalk. There are some essential Big Data interview questions that you must know before you attend one. These will help you find your way through. The questions have been arranged in an order that will help you pick up from the basics and reach a somewhat advanced level. How To Prepare for Big Data Interview Before we proceed further and understand the big data analytics interview questions directly, let us first understand the basic points for the preparation of this interview – Draft a Compelling Resume – A resume is a piece of paper that reflects your accomplishments. However, you must modify your resume based on the role or position you are applying for. Your resume must reflect and compel the employer that you have gone thoroughly with the industry’s standards, history, vision, and culture. You must also mention your soft skills that are relevant to your role.  Interview is a Two-sided Interaction – Apart from giving correct and accurate answers to the interview questions, you must not ignore the importance of asking your questions. Prepare a list of suitable questions in advance and ask them at favorable opportunities. Research and Rehearse – You must research the most commonly asked questions which are asked in the big data analytics interviews. Prepare their answers in advance and rehearse these answers before you appear for the interview. Big Data Interview Questions & Answers  Here is a list of some of the most common big data interview questions to help you prepare beforehand. This list can also apply to big data viva questions, especially if you are looking to prepare for a practical viva exam.  1. Define Big Data and explain the Vs of Big Data. This is one of the most introductory yet important Big Data interview questions. It also doubles as one of the most common big data practical viva questions.  The answer to this is quite straightforward: Big Data can be defined as a collection of complex unstructured or semi-structured data sets which have the potential to deliver actionable insights. The four Vs of Big Data are – Volume – Talks about the amount of data. In other words, the sheer amount of data generated, collected, and stored by organizations.  Variety – Talks about the various formats of data. Data comes in various forms, such as structured data (like databases), semi-structured data (XML, JSON), unstructured data (text, images, videos), and more.  Velocity – Talks about the ever increasing speed at which the data is growing. Moreover, it denotes the speed of data generation processed in real time. Veracity – Talks about the degree of accuracy of data available. Big Data often involves data from multiple sources, which might be incomplete, inconsistent, or contain errors. Ensuring data quality and reliability is essential for making informed decisions. Verifying and maintaining data integrity through cleansing, validation, and quality checks become imperative to derive meaningful insights. Big Data Tutorial for Beginners: All You Need to Know Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses 2. How is Hadoop related to Big Data? When we talk about Big Data, we talk about Hadoop. So, this is another Big Data interview question that you will definitely face in an interview. This one also doubles as one of the most commonly asked BDA viva questions. Hadoop is an open-source framework for storing, processing, and analyzing complex unstructured data sets for deriving insights and intelligence. Hadoop is closely linked to Big Data because it’s a tool specifically designed to handle massive and varied types of data that are typically challenging for regular systems to manage. Hadoop’s main job is to store this huge amount of data across many computers (HDFS) and process it in a way that makes it easier to understand and use (MapReduce).  Essentially, Hadoop is a key player in the Big Data world, helping organizations deal with their large and complex data more easily for analysis and decision-making. 3. Define HDFS and YARN, and talk about their respective components. Now that we’re in the zone of Hadoop, the next Big Data interview question you might face will revolve around the same. This is also among the commonly asked big data interview questions for experienced professionals. Hence, even if you have expert knowledge in this field, make sure that you prepare this question thoroughly. The HDFS is Hadoop’s default storage unit and is responsible for storing different types of data in a distributed environment. HDFS has the following two components: NameNode – This is the master node that has the metadata information for all the data blocks in the HDFS. The NameNode is like the manager of the Hadoop system. It keeps track of where all the files are stored in the Hadoop cluster and manages the file system’s structure and organization. The NameNode is like the manager of the Hadoop system. It keeps track of where all the files are stored in the Hadoop cluster and manages the file system’s structure and organization. DataNode – These are the nodes that act as slave nodes and are responsible for storing the data. DataNodes are like storage units in the Hadoop cluster. They store the actual data blocks that make up the files. They follow instructions from the NameNode and store, retrieve, and replicate data as needed. YARN, short for Yet Another Resource Negotiator, is responsible for managing resources and providing an execution environment for the said processes. The two main components of YARN are – ResourceManager – Responsible for allocating resources to respective NodeManagers based on the needs. It oversees allocation to various applications through the Scheduler, ensuring fair distribution based on policies like FIFO or fair sharing. Additionally, the ApplicationManager is responsible for coordinating and monitoring application execution, handling job submissions, and managing ApplicationMasters. NodeManager – Executes tasks on every DataNode. 7 Interesting Big Data Projects You Need To Watch Out It operates on individual nodes by managing resources, executing tasks within containers, and reporting container statuses to the ResourceManager. It efficiently monitors and allocates resources to tasks, ensuring optimal resource utilization while managing task execution and failure at the node level. 4. What do you mean by commodity hardware? This is yet another Big Data interview question you’re most likely to come across in any interview you sit for. As one of the most common big data questions, make sure you are prepared to answer the same. Commodity Hardware refers to the minimal hardware resources needed to run the Apache Hadoop framework. Any hardware that supports Hadoop’s minimum requirements is known as ‘Commodity Hardware.’ It meets Hadoop’s basic needs and is cost-effective and scalable, making it accessible for setting up Hadoop clusters without requiring pricey specialized gear. This approach lets many businesses use regular, affordable hardware to tap into Hadoop’s powerful data processing abilities. 5. Define and describe the term FSCK. When you are preparing big data testing interview questions, make sure that you cover FSCK. This question can be asked if the interviewer is covering HADOOP questions.  FSCK stands for Filesystem Check. It is a command used to run a Hadoop summary report that describes the state of HDFS. It only checks for errors and does not correct them. This command can be executed on either the whole system or a subset of files. File System (HDFS), FSCK, is a utility that verifies the health and integrity of the HDFS file system by examining its structure and metadata information. It identifies missing, corrupt, or misplaced data blocks and provides information about the overall file system’s status, including the number of data blocks, their locations, and replication status.  Read: Big data jobs and its career opportunities 6. What is the purpose of the JPS command in Hadoop? This is one of the big data engineer interview questions that might be included in interviews focused on the Hadoop ecosystem and tools.  The JPS command is used for testing the working of all the Hadoop daemons. It specifically tests daemons like NameNode, DataNode, ResourceManager, NodeManager and more. (In any Big Data interview, you’re likely to find one question on JPS and its importance.) Big Data: Must Know Tools and Technologies This command is especially useful in verifying whether the different components of a Hadoop cluster, including the core services and auxiliary processes, are up and running. 7. Name the different commands for starting up and shutting down Hadoop Daemons. This is one of the most important Big Data interview questions to help the interviewer gauge your knowledge of commands. To start all the daemons: ./sbin/start-all.sh To shut down all the daemons: ./sbin/stop-all.sh 8. Why do we need Hadoop for Big Data Analytics? This is one of the most anticipated big data Hadoop interview questions.  This Hadoop interview questions test your awareness regarding the practical aspects of Big Data and Analytics. In most cases, Hadoop helps in exploring and analyzing large and unstructured data sets. Hadoop offers storage, processing and data collection capabilities that help in analytics. Knowledge Read: Big data jobs & Career planning These capabilities make Hadoop a fundamental tool in handling the scale and complexity of Big Data, empowering organizations to derive valuable insights for informed decision-making and strategic planning. 9. Explain the different features of Hadoop. Listed in many Big Data Interview Questions and Answers, the best answer to this is – Open-Source – Hadoop is an open-sourced platform. It allows the code to be rewritten or modified according to user and analytics requirements. Scalability – Hadoop supports the addition of hardware resources to the new nodes. Data Recovery – Hadoop follows replication which allows the recovery of data in the case of any failure. Data Locality – This means that Hadoop moves the computation to the data and not the other way round. This way, the whole process speeds up. 10. Define the Port Numbers for NameNode, Task Tracker and Job Tracker. Understanding port numbers, configurations, and components within Hadoop clusters is a common part of big data scenario based interview questions for roles handling Hadoop administration.  NameNode – Port 50070.  This port allows users/administrators to access the Hadoop Distributed File System (HDFS) information and its status through a web browser. Task Tracker – Port 50060.  This port corresponds to TaskTracker’s web UI, providing information about the tasks it handles and allowing monitoring and management through a web browser.Job Tracker – Port 50030.  This port is associated with the JobTracker’s web UI. It allows users to monitor and track the progress of MapReduce jobs, view job history, and manage job-related information through a web browser. 11. What do you mean by indexing in HDFS? HDFS indexes data blocks based on their sizes. The end of a data block points to the address of where the next chunk of data blocks get stored. The DataNodes store the blocks of data while NameNode stores these data blocks. Big Data Applications in Pop-Culture Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript 12. What are Edge Nodes in Hadoop? This is one of the top big data analytics important questions which can also be asked as data engineer interview questions. Edge nodes refer to the gateway nodes which act as an interface between Hadoop cluster and the external network. These nodes run client applications and cluster management tools and are used as staging areas as well. Enterprise-class storage capabilities are required for Edge Nodes, and a single edge node usually suffices for multiple Hadoop clusters. 13. What are some of the data management tools used with Edge Nodes in Hadoop? This Big Data interview question aims to test your awareness regarding various tools and frameworks. Oozie, Ambari, Pig and Flume are the most common data management tools that work with Edge Nodes in Hadoop. 14. Explain the core methods of a Reducer. This is also among the most commonly asked big data analytics interview questions.  There are three core methods of a reducer. They are- setup() – This is used to configure different parameters like heap size, distributed cache and input data. This method is invoked once at the beginning of each reducer task before processing any keys or values. It allows developers to perform initialization tasks and configuration settings specific to the reducer task.  reduce() – A parameter that is called once per key with the concerned reduce task. It is where the actual data processing and reduction take place. The method takes in a key and an iterable collection of values corresponding to that key.  cleanup() – Clears all temporary files and called only at the end of a reducer task. The cleanup() method is called once at the end of each reducer task after all the keys have been processed and the reduce() method has completed execution.  15. Talk about the different tombstone markers used for deletion purposes in HBase. This Big Data interview question dives into your knowledge of HBase and its working. There are three main tombstone markers used for deletion in HBase. They are- Family Delete Marker – For marking all the columns of a column family. When applied, it signifies the intent to delete all versions of all columns within that column family. This marker acts at the column family level, allowing users to delete an entire family of columns across all rows in an HBase table. Version Delete Marker – For marking a single version of a single column. It targets and indicates the deletion of only one version of a specific column. This delete marker permits users to remove a specific version of a column value while retaining other versions associated with the same column. Column Delete Marker – For marking all the versions of a single column. This delete marker operates at the column level, signifying the removal of all versions of a specific column across different timestamps or versions within an individual row in the HBase table. Big Data Engineers: Myths vs. Realities 16. How can Big Data add value to businesses? One of the most common big data interview question. In the present scenario, Big Data is everything. If you have data, you have the most powerful tool at your disposal. Big Data Analytics helps businesses to transform raw data into meaningful and actionable insights that can shape their business strategies. The most important contribution of Big Data to business is data-driven business decisions. Big Data makes it possible for organizations to base their decisions on tangible information and insights. Furthermore, Predictive Analytics allows companies to craft customized recommendations and marketing strategies for different buyer personas. Together, Big Data tools and technologies help boost revenue, streamline business operations, increase productivity, and enhance customer satisfaction. In fact, anyone who’s not leveraging Big Data today is losing out on an ocean of opportunities.  Check out the best big x`data courses at upGrad 17. How do you deploy a Big Data solution? This question also falls under one of the highest anticipated big data analytics viva questions. You can deploy a Big Data solution in three steps: Data Ingestion – This is the first step in the deployment of a Big Data solution. You begin by collecting data from multiple sources, be it social media platforms, log files, business documents, anything relevant to your business. Data can either be extracted through real-time streaming or in batch jobs. Data Storage – Once the data is extracted, you must store the data in a database. It can be HDFS or HBase. While HDFS storage is perfect for sequential access, HBase is ideal for random read/write access. Data Processing – The last step in the deployment of the solution is data processing. Usually, data processing is done via frameworks like Hadoop, Spark, MapReduce, Flink, and Pig, to name a few. 18. How is NFS different from HDFS? This question also qualifies as one of the big data scenario based questions that you may be asked in an interview. The Network File System (NFS) is one of the oldest distributed file storage systems, while Hadoop Distributed File System (HDFS) came to the spotlight only recently after the upsurge of Big Data.  The table below highlights some of the most notable differences between NFS and HDFS: NFS HDFS It can both store and process small volumes of data.  It is explicitly designed to store and process Big Data. The data is stored in dedicated hardware. Data is divided into data blocks that are distributed on the local drives of the hardware.  In the case of system failure, you cannot access the data.  Data can be accessed even in the case of a system failure. Since NFS runs on a single machine, there’s no chance for data redundancy. HDFS runs on a cluster of machines, and hence, the replication protocol may lead to redundant data. 19. List the different file permissions in HDFS for files or directory levels. One of the common big data interview questions. The Hadoop distributed file system (HDFS) has specific permissions for files and directories. There are three user levels in HDFS – Owner, Group, and Others. For each of the user levels, there are three available permissions: read (r) write (w) execute(x). These three permissions work uniquely for files and directories. For files – The r permission is for reading a file The w permission is for writing a file. Although there’s an execute(x) permission, you cannot execute HDFS files. For directories – The r permission lists the contents of a specific directory. The w permission creates or deletes a directory. The X permission is for accessing a child directory. 20. Elaborate on the processes that overwrite the replication factors in HDFS. In HDFS, there are two ways to overwrite the replication factors – on file basis and on directory basis. On File Basis In this method, the replication factor changes according to the file using Hadoop FS shell. The following command is used for this: $hadoop fs – setrep –w2/my/test_file Here, test_file refers to the filename whose replication factor will be set to 2. On Directory Basis This method changes the replication factor according to the directory, as such, the replication factor for all the files under a particular directory, changes. The following command is used for this: $hadoop fs –setrep –w5/my/test_dir Here, test_dir refers to the name of the directory for which the replication factor and all the files contained within will be set to 5. 21. Name the three modes in which you can run Hadoop. One of the most common question in any big data interview. The three modes are: Standalone mode – This is Hadoop’s default mode that uses the local file system for both input and output operations. The main purpose of the standalone mode is debugging. It does not support HDFS and also lacks custom configuration required for mapred-site.xml, core-site.xml, and hdfs-site.xml files.  Pseudo-distributed mode – Also known as the single-node cluster, the pseudo-distributed mode includes both NameNode and DataNode within the same machine. In this mode, all the Hadoop daemons will run on a single node, and hence, the Master and Slave nodes are the same. Fully distributed mode – This mode is known as the multi-node cluster wherein multiple nodes function simultaneously to execute Hadoop jobs. Here, all the Hadoop daemons run on different nodes. So, the Master and Slave nodes run separately. 22. Explain “Overfitting.” This is one of the most common and easy big data interview questions you should not skip. It can also be a part of big data practical viva questions if you are a student in this stream. Hence, make sure you are thoroughly familiar with the answer below. Overfitting refers to a modeling error that occurs when a function is tightly fit (influenced) by a limited set of data points. Overfitting results in an overly complex model that makes it further difficult to explain the peculiarities or idiosyncrasies in the data at hand. As it adversely affects the generalization ability of the model, it becomes challenging to determine the predictive quotient of overfitted models. These models fail to perform when applied to external data (data that is not part of the sample data) or new datasets.  Overfitting is one of the most common problems in Machine Learning. A model is considered to be overfitted when it performs better on the training set but fails miserably on the test set. However, there are many methods to prevent the problem of overfitting, such as cross-validation, pruning, early stopping, regularization, and assembling. 23. What is Feature Selection? This is one of the popular Big Data analytics important questions which is also often featured as data engineer interview questions. Feature selection refers to the process of extracting only the required features from a specific dataset. When data is extracted from disparate sources, not all data is useful at all times – different business needs call for different data insights. This is where feature selection comes in to identify and select only those features that are relevant for a particular business requirement or stage of data processing. The main goal of feature selection is to simplify ML models to make their analysis and interpretation easier. Feature selection enhances the generalization abilities of a model and eliminates the problems of dimensionality, thereby, preventing the possibilities of overfitting. Thus, feature selection provides a better understanding of the data under study, improves the prediction performance of the model, and reduces the computation time significantly.  Feature selection can be done via three techniques: Filters method In this method, the features selected are not dependent on the designated classifiers. A variable ranking technique is used to select variables for ordering purposes. During the classification process, the variable ranking technique takes into consideration the importance and usefulness of a feature. The Chi-Square Test, Variance Threshold, and Information Gain are some examples of the filters method. Wrappers method In this method, the algorithm used for feature subset selection exists as a ‘wrapper’ around the induction algorithm. The induction algorithm functions like a ‘Black Box’ that produces a classifier that will be further used in the classification of features. The major drawback or limitation of the wrappers method is that to obtain the feature subset, you need to perform heavy computation work. Genetic Algorithms, Sequential Feature Selection, and Recursive Feature Elimination are examples of the wrappers method. Embedded method  The embedded method combines the best of both worlds – it includes the best features of the filters and wrappers methods. In this method, the variable selection is done during the training process, thereby allowing you to identify the features that are the most accurate for a given model. L1 Regularisation Technique and Ridge Regression are two popular examples of the embedded method. 24. Define “Outliers.” As one of the most commonly asked big data viva questions and interview questions, ensure that you are thoroughly prepared to answer the following. An outlier refers to a data point or an observation that lies at an abnormal distance from other values in a random sample. In other words, outliers are the values that are far removed from the group; they do not belong to any specific cluster or group in the dataset. The presence of outliers usually affects the behavior of the model – they can mislead the training process of ML algorithms. Some of the adverse impacts of outliers include longer training time, inaccurate models, and poor outcomes.  However, outliers may sometimes contain valuable information. This is why they must be investigated thoroughly and treated accordingly. 25. Name some outlier detection techniques. Again, one of the most important big data interview questions. Here are six outlier detection methods: Extreme Value Analysis – This method determines the statistical tails of the data distribution. Statistical methods like ‘z-scores’ on univariate data are a perfect example of extreme value analysis. Probabilistic and Statistical Models – This method determines the ‘unlikely instances’ from a ‘probabilistic model’ of data. A good example is the optimization of Gaussian mixture models using ‘expectation-maximization’. Linear Models – This method models the data into lower dimensions. Proximity-based Models – In this approach, the data instances that are isolated from the data group are determined by Cluster, Density, or by the Nearest Neighbor Analysis. Information-Theoretic Models – This approach seeks to detect outliers as the bad data instances that increase the complexity of the dataset. High-Dimensional Outlier Detection – This method identifies the subspaces for the outliers according to the distance measures in higher dimensions. 26. Explain Rack Awareness in Hadoop. If you are a student preparing for your practical exam, make sure that you prepare Rack Awareness in Hadoop. This can also be asked as one of the BDA viva questions. Rack Awareness is one of the popular big data interview questions. Rach awareness is an algorithm that identifies and selects DataNodes closer to the NameNode based on their rack information. It is applied to the NameNode to determine how data blocks and their replicas will be placed. During the installation process, the default assumption is that all nodes belong to the same rack.   Rack awareness helps to: Improve data reliability and accessibility. Improve cluster performance. Improve network bandwidth.  Keep the bulk flow in-rack as and when possible. Prevent data loss in case of a complete rack failure. 27. Can you recover a NameNode when it is down? If so, how? This is one of the most common big data interview questions for experienced professionals. Yes, it is possible to recover a NameNode when it is down. Here’s how you can do it: Use the FsImage (the file system metadata replica) to launch a new NameNode.  Configure DataNodes along with the clients so that they can acknowledge and refer to newly started NameNode. When the newly created NameNode completes loading the last checkpoint of the FsImage (that has now received enough block reports from the DataNodes) loading process, it will be ready to start serving the client.  However, the recovery process of a NameNode is feasible only for smaller clusters. For large Hadoop clusters, the recovery process usually consumes a substantial amount of time, thereby making it quite a challenging task.  28. Name the configuration parameters of a MapReduce framework. The configuration parameters in the MapReduce framework include: The input format of data. The output format of data. The input location of jobs in the distributed file system. The output location of jobs in the distributed file system. The class containing the map function The class containing the reduce function The JAR file containing the mapper, reducer, and driver classes. Learn: Mapreduce in big data Learn Online Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career. 29. What is a Distributed Cache? What are its benefits? Any Big Data Interview Question and Answers guide won’t complete without this question. Distributed cache in Hadoop is a service offered by the MapReduce framework used for caching files. If a file is cached for a specific job, Hadoop makes it available on individual DataNodes both in memory and in system where the map and reduce tasks are simultaneously executing. This allows you to quickly access and read cached files to populate any collection (like arrays, hashmaps, etc.) in a code. Distributed cache offers the following benefits: It distributes simple, read-only text/data files and other complex types like jars, archives, etc.  It tracks the modification timestamps of cache files which highlight the files that should not be modified until a job is executed successfully. 30. What is a SequenceFile in Hadoop? In Hadoop, a SequenceFile is a flat-file that contains binary key-value pairs. It is most commonly used in MapReduce I/O formats. The map outputs are stored internally as a SequenceFile which provides the reader, writer, and sorter classes.  There are three SequenceFile formats: Uncompressed key-value records Record compressed key-value records (only ‘values’ are compressed). Block compressed key-value records (here, both keys and values are collected in ‘blocks’ separately and then compressed).  In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses 31. Explain the role of a JobTracker. One of the common big data interview questions. The primary function of the JobTracker is resource management, which essentially means managing the TaskTrackers. Apart from this, JobTracker also tracks resource availability and handles task life cycle management (track the progress of tasks and their fault tolerance). Some crucial features of the JobTracker are: It is a process that runs on a separate node (not on a DataNode). It communicates with the NameNode to identify data location. It tracks the execution of MapReduce workloads. It allocates TaskTracker nodes based on the available slots. It monitors each TaskTracker and submits the overall job report to the client. It finds the best TaskTracker nodes to execute specific tasks on particular nodes. 32. Name the common input formats in Hadoop. Hadoop has three common input formats: Text Input Format – This is the default input format in Hadoop. Sequence File Input Format – This input format is used to read files in a sequence. Key-Value Input Format – This input format is used for plain text files (files broken into lines). 33. What is the need for Data Locality in Hadoop? One of the important big data interview questions. In HDFS, datasets are stored as blocks in DataNodes in the Hadoop cluster. When a  MapReduce job is executing, the individual Mapper processes the data blocks (Input Splits). If the data does is not present in the same node where the Mapper executes the job, the data must be copied from the DataNode where it resides over the network to the Mapper DataNode. When a MapReduce job has over a hundred Mappers and each Mapper DataNode tries to copy the data from another DataNode in the cluster simultaneously, it will lead to network congestion, thereby having a negative impact on the system’s overall performance. This is where Data Locality enters the scenario. Instead of moving a large chunk of data to the computation, Data Locality moves the data computation close to where the actual data resides on the DataNode. This helps improve the overall performance of the system, without causing unnecessary delay. 34. What are the steps to achieve security in Hadoop? In Hadoop, Kerberos – a network authentication protocol – is used to achieve security. Kerberos is designed to offer robust authentication for client/server applications via secret-key cryptography.  When you use Kerberos to access a service, you have to undergo three steps, each of which involves a message exchange with a server. The steps are as follows: Authentication – This is the first step wherein the client is authenticated via the authentication server, after which a time-stamped TGT (Ticket Granting Ticket) is given to the client. Authorization – In the second step, the client uses the TGT for requesting a service ticket from the TGS (Ticket Granting Server). Service Request – In the final step, the client uses the service ticket to authenticate themselves to the server.  35. How can you handle missing values in Big Data? Final question in our big data interview questions and answers guide. Missing values refer to the values that are not present in a column. It occurs when there’s is no data value for a variable in an observation. If missing values are not handled properly, it is bound to lead to erroneous data which in turn will generate incorrect outcomes. Thus, it is highly recommended to treat missing values correctly before processing the datasets. Usually, if the number of missing values is small, the data is dropped, but if there’s a bulk of missing values, data imputation is the preferred course of action.  In Statistics, there are different ways to estimate the missing values. These include regression, multiple data imputation, listwise/pairwise deletion, maximum likelihood estimation, and approximate Bayesian bootstrap. What command should I use to format the NameNode? This also falls under the umbrella of big data analytics important questions. Here’s the answer: The command to format the NameNode is “$ hdfs namenode -format” Do you like good data or good models more? Why? You may face these big data scenario based interview questions in interviews. Although it is a difficult topic, it is frequently asked in big data interviews. You are asked to choose between good data or good models. You should attempt to respond to it from your experience as a candidate. Many businesses have already chosen their data models because they want to adhere to a rigid evaluation process. Good data can change the game in this situation. The opposite is also true as long as a model is selected based on reliable facts. Answer it based on your own experience. Though it is challenging to have both in real-world projects, don’t say that having good data and good models is vital. Will you speed up the code or algorithms you use? One of the top big data analytics important questions is undoubtedly this one. Always respond “Yes” when asked this question. Performance in the real world is important and is independent of the data or model you are utilizing in your project. If you have any prior experience with code or algorithm optimization, the interviewer may be very curious to hear about it. It definitely relies on the previous tasks a newbie worked on. Candidates with experience can also discuss their experiences appropriately. However, be truthful about your efforts; it’s okay if you haven’t previously optimized any code. You can succeed in the big data interview if you just share your actual experience with the interviewer. What methodology do you use for data preparation? One of the most important phases in big data projects is data preparation. There may be at least one question focused on data preparation in a big data interview. This question is intended to elicit information from you on the steps or safety measures you employ when preparing data. As you are already aware, data preparation is crucial to obtain the information needed for further modeling. The interviewer should hear this from you. Additionally, be sure to highlight the kind of model you’ll be using and the factors that went into your decision. Last but not least, you should also go over keywords related to data preparation, such as variables that need to be transformed, outlier values, unstructured data, etc. Tell us about data engineering. Big data is also referred to as data engineering. It focuses on how data collection and research are applied. The data produced by different sources is raw data. Data engineering assists in transforming this raw data into informative and valuable insights.  This is one of the top motadata interview questions asked by the interviewer. Make sure to practice it among other motadata interview questions to strengthen your preparation. How well-versed are you in collaborative filtering? A group of technologies known as collaborative filtering predict which products a specific consumer will like based on the preferences of a large number of people. It is merely the technical term for asking others for advice. Ensure you do not skip this because it can be one of the big data questions asked in your interview. What does a block in the Hadoop Distributed File System (HDFS) mean? When a file is placed in HDFS, the entire file system is broken down into a collection of blocks, and HDFS is completely unaware of the contents of the file. Hadoop requires blocks to be 128MB in size. Individual files may have a different value for this. Give examples of active and passive Namenodes. The answer is that Active NameNodes operate and function within a cluster, whilst Passive NameNodes have similar data to Active NameNodes. What criteria will you use to define checkpoints? A checkpoint is a key component of keeping the HDFS filesystem metadata up to date. By combining fsimage and the edit log, it provides file system metadata checkpoints. Checkpoint is the name of the newest iteration of fsimage. What is the primary distinction between Sqoop and distCP? DistCP is used for data transfers between clusters, whereas Sqoop is solely used for data transfers between Hadoop and RDBMS. Sqoop and DistCP serve different data transfer needs within the Hadoop ecosystem. Sqoop specializes in bidirectional data transfers between Hadoop and relational databases (RDBMS), allowing seamless import and export of structured data. DistCP works at the Hadoop Distributed File System (HDFS) level, breaking data into chunks and performing parallel data transfers across nodes or clusters, ensuring high-speed, fault-tolerant data movement. You need not elaborate so much when asked one of these big data testing interview questions, but make sure that you stay one step ahead in case your interviewer asks you to. How can unstructured data be converted into structured data? Big Data changed the field of data science for many reasons, one of which is the organizing of unstructured data. The unstructured data is converted into structured data to enable accurate data analysis. You should first describe the differences between these two categories of data in your response to such big data interview questions before going into detail about the techniques you employ to convert one form of data into another. Share your personal experience while highlighting the importance of machine learning in data transformation. How much data is required to produce a reliable result? This can also be one of the big data engineer interview questions if you are applying for a similar job position. Ans. Every company is unique, and every firm is evaluated differently. Therefore, there will never be enough data and no correct response. The amount of data needed relies on the techniques you employ to have a great chance of obtaining important results. A strong data collection strategy greatly influences result accuracy and reliability. Additionally, leveraging advanced analytics and machine learning techniques can boost insights from smaller datasets, highlighting the importance of using appropriate analysis methods. Do other parallel computing systems and Hadoop differ from one another? How? Yes, they do. Hadoop is a distributed file system. It enables us to control data redundancy while storing and managing massive volumes of data in a cloud of computers. The key advantage of this is that it is preferable to handle the data in a distributed manner because it is stored across numerous nodes. Instead of wasting time sending data across the network, each node may process the data that is stored there. In comparison, a relational database computer system allows for real-time data querying but storing large amounts of data in tables, records, and columns is inefficient. What is a Backup Node? As one of the common big data analytics interview questions, prepare the answer to this well. The Backup Node is an expanded Checkpoint Node that supports both Checkpointing and Online Streaming of File System Edits. It forces synchronization with Namenode and functions similarly to Checkpoint. The file system namespace is kept up to date in memory by the Backup Node. The backup node must store the current state from memory to generate a new checkpoint in an image file. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? 50. What do you mean by Google BigQuery? This can be categorized under uncommon but nevertheless important big query interview questions. Familiarize yourself with the answer given below. Google BigQuery is a fully managed, serverless cloud-based data warehouse provided by Google Cloud Platform. It’s designed for high-speed querying and analyzing large datasets using SQL-like queries. BigQuery offers scalable and cost-effective data storage and processing without requiring infrastructure management, making it suitable for real-time analytics and data-driven decision-making. Are you willing to gain an advancement in your learning which can help you to make your career better with us? This question is often asked in the last part of the interview stage. The answer to this question varies from person to person. It depends on your current skills and qualifications and also your responsibilities towards your family. But this question is a great opportunity to show your enthusiasm and spark for learning new things. You must try to answer this question honestly and straightforwardly. At this point, you can also ask the company about its mentoring and coaching policies for its employees. You must also keep in mind that there are various programs readily available online and answer this question accordingly. Do you have any questions for us? As discussed earlier, the interview is a two-way process. You are also open to asking questions. But, it is essential to understand what to ask and when to ask. Usually, it is advised to keep your questions for the last. However, it also depends on the flow of your interview. You must keep a note of the time that your question can take and also track how your overall discussion has gone. Accordingly, you can ask questions from the interviewer and must not hesitate to seek any clarification. Conclusion We hope our Big Data Questions and Answers guide is helpful. We will be updating the guide regularly to keep you updated. If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.
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by Mohit Soni

What is Big Data – Characteristics, Types, Benefits & Examples
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Lately the term ‘Big Data’ has been under the limelight, but not many people know what is big data. Businesses, governmental institutions, HCPs (Health Care Providers), and financial as well as academic institutions, are all leveraging the power of Big Data to enhance business prospects along with improved customer experience. Simply Stating, What Is Big Data? Simply stating, big data is a larger, complex set of data acquired from diverse, new, and old sources of data. The data sets are so voluminous that traditional software for data processing cannot manage it. Such massive volumes of data are generally used to address problems in business you might not be able to handle. IBM maintains that businesses around the world generate nearly 2.5 quintillion bytes of data daily! Almost 90% of the global data has been produced in the last 2 years alone. So we know for sure that the best way to answer ‘what is big data’ is mentioning that it has penetrated almost every industry today and is a dominant driving force behind the success of enterprises and organizations across the globe. But, at this point, it is important to know what is big data? Lets talk about big data, characteristics of big data, types of big data and a lot more. Check out our free courses to get an edge over the competition. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript You won’t belive how this Program Changed the Career of Students What is Big Data? Gartner Definition  According to Gartner, the definition of Big Data –  “Big data” is high-volume, velocity, and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.” This definition clearly answers the “What is Big Data?” question – Big Data refers to complex and large data sets that have to be processed and analyzed to uncover valuable information that can benefit businesses and organizations. However, there are certain basic tenets of Big Data that will make it even simpler to answer what is Big Data: It refers to a massive amount of data that keeps on growing exponentially with time. It is so voluminous that it cannot be processed or analyzed using conventional data processing techniques. It includes data mining, data storage, data analysis, data sharing, and data visualization. The term is an all-comprehensive one including data, data frameworks, along with the tools and techniques used to process and analyze the data. Big Data Applications That Surround You Types of Big Data Now that we are on track with what is big data, let’s have a look at the types of big data: Structured Structured is one of the types of big data and By structured data, we mean data that can be processed, stored, and retrieved in a fixed format. It refers to highly organized information that can be readily and seamlessly stored and accessed from a database by simple search engine algorithms. For instance, the employee table in a company database will be structured as the employee details, their job positions, their salaries, etc., will be present in an organized manner.  Read: Big data engineering jobs and its career opportunities What is big data technology and its types? Structured one of the types of big data is easy to input, store, query and analyze thanks to its predefined data model and schema. Most traditional databases and spreadsheets hold structured data like tables, rows, and columns. This makes it simple for analysts to run SQL queries and extract insights using familiar BI tools. However, structuring data requires effort and expertise during the design phase. As data volumes grow to petabyte scale, rigid schemas become impractical and limit the flexibility needed for emerging use cases. Also some data like text, images, video etc. cannot be neatly organized in tabular formats. Therefore, while structured data brings efficiency, scale and variety of big data necessitates semi-structured and unstructured types of digital data in big data to overcome these limitations. The value lies in consolidating these multiple types rather than relying solely on structured data for modern analytics. Unstructured Unstructured data refers to the data that lacks any specific form or structure whatsoever. This makes it very difficult and time-consuming to process and analyze unstructured data. Email is an example of unstructured data. Structured and unstructured are two important types of big data. Unstructured types of big data constitutes over 80% of data generated today and continues to grow exponentially from sources like social posts, digital images, videos, audio files, emails, and more. It does not conform to any data model, so conventional tools cannot give meaningful insights from it. However, unstructured data tends to be more subjective, rich in meaning, and reflective of human communication compared to tabular transaction data.  With immense business value hidden inside, specialized analytics techniques involving NLP, ML, and AI are essential to process high volumes of unstructured content. For instance, sentiment analysis of customer social media rants can alert companies to issues before mainstream notice. Text mining of maintenance logs and field technician reports can improve future product designs. And computer vision techniques on image data from manufacturing floors can automate quality checks. While analysis requires advanced skill, the unstructured data’s scale, variety, and information density deliver new opportunities for competitive advantage across industries. Check out the big data courses at upGrad Semi-structured Semi structured is the third type of big data. Semi-structured data pertains to the data containing both the formats mentioned above, that is, structured and unstructured data. To be precise, it refers to the data that although has not been classified under a particular repository (database), yet contains vital information or tags that segregate individual elements within the data. Thus we come to the end of types of data. Lets discuss the characteristics of data. Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses Semi-structured variety in big data includes elements of both structured and unstructured data. For example, XML, JSON documents contain tags or markers to separate semantic elements, but the data is unstructured free flowing text, media, etc. Clickstream data from website visits have structured components like timestamps and pages visited, but the path a user takes is unpredictable. Sensor data with timestamped values is semi-structured. This hybrid data abstraction effortlessly incorporates the variety and volume of big data across system interfaces.  For analytic applications, semi-structured data poses technical and business-level complexities for processing, governance, and insight generation. However, flexible schemas and object-oriented access methods are better equipped to handle velocity and variety in semi-structured types of digital data in big data at scale. With rich contextual information encapsulated, established databases have expanded native JSON, XML, and Graph support for semi-structured data to serve modern real-time analytics needs. Characteristics of Big Data Back in 2001, Gartner analyst Doug Laney listed the 3 ‘V’s of Big Data – Variety, Velocity, and Volume. Let’s discuss the characteristics of big data. These characteristics, isolatedly, are enough to know what is big data. Let’s look at them in depth: 1) Variety Variety of Big Data refers to structured, unstructured, and semistructured data that is gathered from multiple sources. While in the past, data could only be collected from spreadsheets and databases, today data comes in an array of forms such as emails, PDFs, photos, videos, audios, SM posts, and so much more. Variety is one of the important characteristics of big data. The traditional types of data are structured and also fit well in relational databases. With the rise of big data, the data now comes in the form of new unstructured types. These unstructured, as well as semi-structured data types, need additional pre-processing for deriving meaning and support of metadata. 2) Velocity Velocity essentially refers to the speed at which data is being created in real-time. In a broader prospect, it comprises the rate of change, linking of incoming data sets at varying speeds, and activity bursts. The speed of data receipt and action is simply known as velocity. The highest velocity for data will stream directly into the memory against being written to the disk. Few internet-based smart products do operate in real-time or around real-time. This mostly requires evaluation as well as in real-time. Learn: Mapreduce in big data The velocity of variety in big data is crucial because it allows companies to make quick, data-driven decisions based on real-time insights. As data streams in at high speeds from sources like social media, sensors, mobile devices, etc., companies can spot trends, detect patterns, and derive meaning from that data more rapidly. High velocity characteristics of big data combined with advanced analytics enables faster planning, problem detection, and decision optimization. For example, a company monitoring social media chatter around its brand can quickly respond to emerging issues before they spiral out of control. 3) Volume Volume is one of the characteristics of big data. We already know that Big Data indicates huge ‘volumes’ of data that is being generated on a daily basis from various sources like social media platforms, business processes, machines, networks, human interactions, etc. Such a large amount of data are stored in data warehouses. Thus comes to the end of characteristics of big data. The data volume matters when you discuss the big data characteristics. In the context of big data, you will need to process a very high volume of low-density or unstructured data. This will be data related to an unknown value. Example data feeds on Twitter, clickstreams on web pages or mobile apps, or even sensor-based equipment. For a few organizations, it means ten times a few terabytes of data. For some others, it could mean hundreds of times petabytes. Big Data Roles and Salaries in the Finance Industry Advantages of Big Data (Features) One of the biggest advantages of Big Data is predictive analysis. Big Data analytics tools can predict outcomes accurately, thereby, allowing businesses and organizations to make better decisions, while simultaneously optimizing their operational efficiencies and reducing risks. By harnessing data from social media platforms using Big Data analytics tools, businesses around the world are streamlining their digital marketing strategies to enhance the overall consumer experience. Big Data provides insights into the customer pain points and allows companies to improve upon their products and services. Being accurate, Big Data combines relevant data from multiple sources to produce highly actionable insights. Almost 43% of companies lack the necessary tools to filter out irrelevant data, which eventually costs them millions of dollars to hash out useful data from the bulk. Big Data tools can help reduce this, saving you both time and money. Big Data analytics could help companies generate more sales leads which would naturally mean a boost in revenue. Businesses are using Big Data analytics tools to understand how well their products/services are doing in the market and how the customers are responding to them. Thus, the can understand better where to invest their time and money. With Big Data insights, you can always stay a step ahead of your competitors. You can screen the market to know what kind of promotions and offers your rivals are providing, and then you can come up with better offers for your customers. Also, Big Data insights allow you to learn customer behaviour to understand the customer trends and provide a highly ‘personalized’ experience to them. Read: Career Scope for big data jobs. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Who is using Big Data? 5 Applications The people who’re using Big Data know better that, what is Big Data. Let’s look at some such industries: 1) Healthcare Big Data has already started to create a huge difference in the healthcare sector. With the help of predictive analytics, medical professionals and HCPs are now able to provide personalized healthcare services to individual patients. Apart from that, fitness wearables, telemedicine, remote monitoring – all powered by Big Data and AI – are helping change lives for the better. The healthcare industry is harnessing big data in various innovative ways – from detecting diseases faster to providing better treatment plans and preventing medication errors. By analyzing patient history, clinical data, claims data, and more, healthcare providers can better understand patient risks, genetic factors, environmental factors to customize treatments rather than follow a one-size-fits-all approach. Population health analytics on aggregated EMR data also allows hospitals to reduce readmission rates and unnecessary costs. Pharmaceutical companies are leveraging big data to improve drug formulation, identify new molecules, and reduce time-to-market by analyzing years of research data. The insights from medical imaging data combined with genomic data analysis enables precision diagnosis at early stages. 2) Academia Big Data is also helping enhance education today. Education is no more limited to the physical bounds of the classroom – there are numerous online educational courses to learn from. Academic institutions are investing in digital courses powered by Big Data technologies to aid the all-round development of budding learners. Educational institutions are leveraging big data in dbms in multifaceted ways to elevate learning experiences and optimize student outcomes. By analyzing volumes of student academic and behavioral data, predictive models identify at-risk students early to recommend timely interventions. Tailored feedback is provided based on individual progress monitoring. Curriculum design and teaching practices are refined by assessing performance patterns in past course data. Self-paced personalized learning platforms powered by AI recommend customized study paths catering to unique learner needs and competency levels. Academic corpus and publications data aids cutting-edge research and discovery through knowledge graph mining and natural language queries. Knowledge Read: Big data jobs & Career planning 3) Banking The banking sector relies on Big Data for fraud detection. Big Data tools can efficiently detect fraudulent acts in real-time such as misuse of credit/debit cards, archival of inspection tracks, faulty alteration in customer stats, etc. Banks and financial institutions depend heavily on big data in dbms and analytics to operate services, reduce risks, retain customers, and increase profitability. Predictive models flag probable fraudulent transactions in seconds before completion by scrutinizing volumes of past transactional data, customer information, credit history, investments, and third-party data. Connecting analytics to the transaction processing pipeline has immensely reduced false declines and improved fraud detection rates. Client analytics helps banks precisely segment customers, contextualise engagement through the right communication channels, and accurately anticipate their evolving needs to recommend the best financial products. Processing volumes of documentation and loan application big data types faster using intelligent algorithms and automation enables faster disbursal with optimized risks. Trading firms leverage big data analytics on historical market data, economic trends, and news insights to support profitable investment decisions. Thus, big data radically enhances banking experiences by minimizing customer risks and maximizing personalisation through every engagement. 4) Manufacturing According to TCS Global Trend Study, the most significant benefit of Big Data in manufacturing is improving the supply strategies and product quality. In the manufacturing sector, Big data helps create a transparent infrastructure, thereby, predicting uncertainties and incompetencies that can affect the business adversely. Manufacturing industries are optimizing end-to-end value chains using volumes of operational data generated from sensors, equipment logs, inventory flows, supplier networks, and customer transactions. By combining this real-time structured and unstructured big data types with enterprise data across siloed sources, manufacturers gain comprehensive visibility into operational performance, production quality, supply-demand dynamics, and fulfillment. Advanced analytics transforms this data into meaningful business insights around minimizing process inefficiencies, improving inventory turns, reducing machine failures, shortening production cycle times, and meeting dynamic customer demands continually. Overall, equipment effectiveness is improved with predictive maintenance programs. Data-based simulation, scheduling, and control automation increases speed, accuracy, and compliance. Real-time synchronization of operations planning with execution enabled by big data analytics creates the responsive and intelligent factory of the future. 5) IT One of the largest users of Big Data, IT companies around the world are using Big Data to optimize their functioning, enhance employee productivity, and minimize risks in business operations. By combining Big Data technologies with ML and AI, the IT sector is continually powering innovation to find solutions even for the most complex of problems. Planning a Big Data Career? Know All Skills, Roles & Transition Tactics! The technology and IT sectors pioneer big data-enabled transformations across other industries, though the first application starts from within. IT infrastructure performance, application usage telemetry, network traffic data, security events, and business KPIs provide technology teams with comprehensive observability into systems health, utilization, gaps and dependencies. This drives data-based capacity planning, proactive anomaly detection and accurate root cause analysis to optimize IT service quality and employee productivity. User behavior analytics identifies the most valued features and pain points to prioritize software enhancements aligned to business needs. For product companies, big data analytics features of big data logs, sensor data, and customer usage patterns enhances user experiences by detecting issues and churn faster. Mining years of structured and unstructured data aids context-aware conversational AI feeding into chatbots and virtual assistants. However, robust information management and governance practices remain vital as the scale and complexity of technology data environments continue to expand massively. With positive business outcomes realized internally, IT domain expertise coupled with analytics and AI skillsets power data transformation initiatives across external customer landscapes. 6. Retail Big Data has changed the way of working in traditional brick and mortar retail stores. Over the years, retailers have collected vast amounts of data from local demographic surveys, POS scanners, RFID, customer loyalty cards, store inventory, and so on. Now, they’ve started to leverage this data to create personalized customer experiences, boost sales, increase revenue, and deliver outstanding customer service. Retailers are even using smart sensors and Wi-Fi to track the movement of customers, the most frequented aisles, for how long customers linger in the aisles, among other things. They also gather social media data to understand what customers are saying about their brand, their services, and tweak their product design and marketing strategies accordingly.  7. Transportation  Big Data Analytics holds immense value for the transportation industry. In countries across the world, both private and government-run transportation companies use Big Data technologies to optimize route planning, control traffic, manage road congestion, and improve services. Additionally, transportation services even use Big Data to revenue management, drive technological innovation, enhance logistics, and of course, to gain the upper hand in the market. The transportation sector is adopting big data and IoT technologies to monitor, analyse, and optimize end-to-end transit operations intelligently. Transport authorities can dynamically control traffic flows, mitigating congestion, optimising tolls, and identifying incidents faster by processing high-velocity telemetry data streams from vehicles, roads, signals, weather systems, and rider mobile devices. Journey reliability and operational efficiency are improved through data-based travel demand prediction, dynamic route assignment, and AI-enabled dispatch. Predictive maintenance reduces equipment downtime. Riders benefit from real-time tracking, estimated arrivals, and personalized alerts, minimising wait times. Logistics players leverage big data for streamlined warehouse management, load planning, and shipment route optimisation, driving growth and customer satisfaction. However, key challenges around data quality, privacy, integration, and skills shortage persist. They need coordinated efforts from policymakers and technology partners before their sustainable value is fully realised across an integrated transportation ecosystem. Big Data Case studies 1. Walmart  Walmart leverages Big Data and Data Mining to create personalized product recommendations for its customers. With the help of these two emerging technologies, Walmart can uncover valuable patterns showing the most frequently bought products, most popular products, and even the most popular product bundles (products that complement each other and are usually purchased together). Based on these insights, Walmart creates attractive and customized recommendations for individual users. By effectively implementing Data Mining techniques, the retail giant has successfully increased the conversion rates and improved its customer service substantially. Furthermore, Walmart uses Hadoop and NoSQL technologies to allow customers to access real-time data accumulated from disparate sources.  2. American Express The credit card giant leverages enormous volumes of customer data to identify indicators that could depict user loyalty. It also uses Big Data to build advanced predictive models for analyzing historical transactions along with 115 different variables to predict potential customer churn. Thanks to Big Data solutions and tools, American Express can identify 24% of the accounts that are highly likely to close in the upcoming four to five months.  3. General Electric In the words of Jeff Immelt, Chairman of General Electric, in the past few years, GE has been successful in bringing together the best of both worlds – “the physical and analytical worlds.” GE thoroughly utilizes Big Data. Every machine operating under General Electric generates data on how they work. The GE analytics team then crunches these colossal amounts of data to extract relevant insights from it and redesign the machines and their operations accordingly. Today, the company has realized that even minor improvements, no matter how small, play a crucial role in their company infrastructure. According to GE stats, Big Data has the potential to boost productivity by 1.5% in the US, which compiled over a span of 20 years could increase the average national income by a staggering 30%! 4. Uber  Uber is one of the major cab service providers in the world. It leverages customer data to track and identify the most popular and most used services by the users. Once this data is collected, Uber uses data analytics to analyze the usage patterns of customers and determine which services should be given more emphasis and importance. Apart from this, Uber uses Big Data in another unique way. Uber closely studies the demand and supply of its services and changes the cab fares accordingly. It is the surge pricing mechanism that works something like this – suppose when you are in a hurry, and you have to book a cab from a crowded location, Uber will charge you double the normal amount!   5. Netflix Netflix is one of the most popular on-demand online video content streaming platform used by people around the world. Netflix is a major proponent of the recommendation engine. It collects customer data to understand the specific needs, preferences, and taste patterns of users. Then it uses this data to predict what individual users will like and create personalized content recommendation lists for them. Today, Netflix has become so vast that it is even creating unique content for users. Data is the secret ingredient that fuels both its recommendation engines and new content decisions. The most pivotal data points used by Netflix include titles that users watch, user ratings, genres preferred, and how often users stop the playback, to name a few. Hadoop, Hive, and Pig are the three core components of the data structure used by Netflix.  6. Procter & Gamble Procter & Gamble has been around us for ages now. However, despite being an “old” company, P&G is nowhere close to old in its ways. Recognizing the potential of Big Data, P&G started implementing Big Data tools and technologies in each of its business units all over the world. The company’s primary focus behind using Big Data was to utilize real-time insights to drive smarter decision making. To accomplish this goal, P&G started collecting vast amounts of structured and unstructured data across R&D, supply chain, customer-facing operations, and customer interactions, both from company repositories and online sources. The global brand has even developed Big Data systems and processes to allow managers to access the latest industry data and analytics. 7. IRS Yes, even government agencies are not shying away from using Big Data. The US Internal Revenue Service actively uses Big Data to prevent identity theft, fraud, and untimely payments (people who should pay taxes but don’t pay them in due time). The IRS even harnesses the power of Big Data to ensure and enforce compliance with tax rules and laws. As of now, the IRS has successfully averted fraud and scams involving billions of dollars, especially in the case of identity theft. In the past three years, it has also recovered over US$ 2 billion. Careers In Big Data Big data characteristics are seemingly transforming the way businesses work while also driving growth through the economy globally. Businesses are observing immense benefits using the characteristics of big data for protecting their database, aggregating huge volumes of information, as well as making informed decisions to benefit organizations. No wonder it is clear that big data has a huge range across a number of sectors. For instance, in the financial industry, big data comes across as a vital tool that helps make profitable decisions. Similarly, some data organizations might look at big data as a means for fraud protection and pattern detection in large-sized datasets. Nearly every large-scale organization currently seeks talent in big data, and hopefully, the demand is prone to a significant rise in the future as well. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Wrapping Up We hope we were able to answer the “What is Big Data?” question clearly enough. We hope you understood about the types of big data, characteristics of big data, use cases, etc.  Organizations actually mine both unstructured as well structured data sets. This helps in leveraging machine learning as well as framing predictive modeling techniques. The latter helps extract meaningful insights. With such findings, a data manager will be able to make data-driven decisions and solve a plethora of main business problems. A number of significant technical skills help individuals succeed in the field of big data. Such skills include-       Data mining       Programming       Data visualization       Analytics If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career.
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by Abhinav Rai

18 Feb 2024

Cassandra vs MongoDB: Difference Between Cassandra & MongoDB [2023]
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Introduction Cassandra and MongoDB are among the most famous NoSQL databases used by large to small enterprises and can be relied upon for scalability. Cassandra was launched in late 2008, and after a year, MongoDB was also launched. Apart from the fact that both are open-source, there are multiple contrasting factors between them. Let’s have a look at them one by one. Structuring Of Data  This data architecture supports flexible column addition and updates without needing schema modifications. Cassandra also supports collections, letting users save sophisticated data types such as lists, sets, and maps. Including these aspects would offer a complete picture of Cassandra’s data modelling versatility. Cassandra is more like RDBMS if we talk about the way it stores information. It stores data structured in tables and follows columns based arrangement. However, in contrast to RDBMS, you can make columns and tables very quickly. Also, every line in Cassandra doesn’t need to have a similar column. The database depends on the essential key to bring information.  Whereas, MongoDB can be considered as an object-oriented database. It utilises BSON (Binary JSON) to store information. MongoDB can uphold different object structures, and even gives the option of creating nested structures. If we compare with Cassandra, MongoDB is much more flexible as the user must not have a JSON schema. It also gives the option to deal with schemas if necessary in some cases. Query Language Cassandra utilises Cassandra Query Language (CQL) for getting the required data. CQL is fundamentally the same as SQL. CQL is very easy to learn for any data professional who is well acquainted with SQL. MongoDB gives significantly better alternatives in this scenario majorly because it stores information in JSON-like records. Overseers can request MongoDB data via the Mongo shell, PHP, Perl, Python, Node.js, Java, Compass, and Ruby. While comparing Cassandra vs MongoDB, don’t forget that MongoDB’s query language offers a comprehensive collection of operators and functions that are particularly built for JSON-like documents. These enhancements provide greater functionality and eloquent queries, allowing developers to alter and retrieve information contained in MongoDB more easily. Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses Secondary Indexes Secondary Indexes are valuable for getting the data that is primarily a non-key attribute. Cassandra doesn’t ultimately uphold secondary indexes. It depends on the primary keys to fetch data.  MongoDB leans towards indexes for fetching the required data. The compatibility with secondary indexes helps MongoDB in improving the inquiry speeds. It is conceivable to inquiry any property of an item, including nested objects, that too with few moments.   Cassandra does not intrinsically give secondary indexes, it does offer an alternative known as “materialized views”. By building indexes on individual columns, materialized views encourage the denormalization of information and fast querying of non-key properties. All this additional data would help distinguish the indexing features of both databases. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Scalability To enhances the write-scalability of system, Cassandra enables the administrator to have multiple master nodes. An administrator can define the total number of nodes that will be required in a cluster. One can judge the level of scalability of a database on the number of full nodes. On the other hand, MongoDB has enabled only one master node. Rest of the nodes act as slaves within the cluster. Even though data will be defined in the master node, the slave nodes are configured as read-only. The scalability of MongoDB gets hit as compared to Cassandra, primarily because of this master-slave architecture. One can enhance the scalability of MongoDB through sharding techniques. Learn about Cassandra vs hadoop. The underlying contrasting feature in the interpretation process between the two is the way they handle fault tolerance. Cassandra can define a cluster even when a particular node fails because it allows multiple masters. On the other hand, MongoDB forces the administrator to wait 10 to 40 seconds if a node fails and wants to enter some information. It is because of the single master behavior of MongoDB. All in all, Cassandra is much better than MongoDB in terms of availability. It is crucial to note that while MongoDB defaults to a master-slave design, it also allows sharding. Sharding shares data over numerous servers, enabling horizontal scalability, bigger collections of data, and higher performance. This feature demonstrates how, despite maintaining a single master node, MongoDB can achieve significant scalability using sharding. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Aggregation To run complex queries, most of the users use aggregation these days. In Cassandra, there is no built-in support for any aggregation system. Most of the users have to find a workaround for utilising the benefits of aggregation. For this, multiple third-party tools are used by administrators like Hadoop and Spark. Unlike Cassandra, MongoDB comes packed with an aggregation framework. To aggregate stored data, it can make use of the ETL pipeline and give out results. Although it is an easy way to do this, the built-in aggregation method runs only on low to medium traffic. Hence, it becomes challenging to scale as the aggregation framework keeps on becoming complex. Cassandra supports complicated aggregations utilizing third-party tools like Apache Spark or Apache Flink. These technologies work well with Cassandra and offer substantial backing for the large-scale processing of information and analytics. This knowledge contributes to a deeper comprehension of Cassandra’s aggregation features. Performance Performance evaluation of these requires the analysis of a lot of factors. Everything is taken into account from the type of schema you use (which directly affects the query speeds) to the input and output load characteristics (responsible for a database’s performance).  Cassandra was a clear winner in write-oriented operations as per a 2018 benchmark report on Cassandra vs MongoDB. Licensing Licensing is not a significant issue in both databases as they open as open-source, free software. If someone wants to opt for enterprise-grade Cassandra, third-party vendors like Datastax offer individual plans. Whereas, its namesake software company usually overlooks MongoDB. Subscription plans are available for both of them at different levels. On top of it, anyone can also use AWS to host the database on public clouds as AWS gives built-in support. Learn More About Top 5 Big Data Tools  Popular Courses & Articles on Software Engineering Popular Programs Executive PG Program in Software Development - IIIT B Blockchain Certificate Program - PURDUE Cybersecurity Certificate Program - PURDUE MSC in Computer Science - IIIT B Other Popular Articles Cloud Engineer Salary in the US AWS Solution Architect Salary in US Backend Developer Salary in the US Front End Developer Salary in US Web developer Salary in USA Scrum Master Interview Questions How to Start a Career in Cyber Security Career Options in the US for Engineering Students .wpdt-fs-000020 { font-size: 20px !important;} Conclusion Organisations are continuously searching for new and creative innovations to chip away at, and databases like MongoDB and Cassandra is one of them. These new-age abilities are valuable to flourish in a competitive environment where needs change with a new technology’s arrival. It is important to mention that while comparing the difference between Cassandra and MongoDB, both are emerging databases with merits and shortcomings. Mentioning that organizations should carefully analyze their specific needs and consider variables such as data modelling demands, query patterns, adaptability, fault tolerance, and environment interoperability would offer readers a well-rounded view. upGrad has been offering these forefront skills in different areas, for example, a Machine Learning Course, Data Science from IIITB, provided in a joint effort with industry accomplices like Flipkart and Indian Institute of Information Technology.  Your future must be secure if you devote your time and exertion to seek after your goals. We at upGrad are here to assist you with accomplishing that potential and form your abilities into resources for future organisations and associations by giving ongoing sources of info and broad arrangement. Make your future secure with us, and don’t let these difficulties deny you had always wanted. Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career.
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by Rohit Sharma

31 Jan 2024

13 Ultimate Big Data Project Ideas & Topics for Beginners [2024]
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Big Data Project Ideas Big Data is an exciting subject. It helps you find patterns and results you wouldn’t have noticed otherwise. This skill highly in demand, and you can quickly advance your career by learning it. So, if you are a big data beginner, the best thing you can do is work on some big data project ideas. But it can be difficult for a beginner to find suitable big data topics as they aren’t very familiar with the subject.  We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting big data project ideas which beginners can work on to put their big data knowledge to test. In this article, you will find top big data project ideas for beginners to get hands-on experience on big data Check out our free courses to get an edge over the competition. However, knowing the theory of big data alone won’t help you much. You’ll need to practice what you’ve learned. But how would you do that? You can practice your big data skills on big data projects. Projects are a great way to test your skills. They are also great for your CV. Especially big data research projects and data processing projects are something that will help you understand the whole of the subject most efficiently.  Read: Big data career path You won’t belive how this Program Changed the Career of Students Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses What are the areas where big data analytics is used? Before jumping into the list of  big data topics that you can try out as a beginner, you need to understand the areas of application of the subject. This will help you invent your own topics for data processing projects once you complete a few from the list. Hence, let’s see what are the areas where big data analytics is used the most. This will help you navigate how to identify issues in certain industries and how they can be resolved with the help of big data as big data research projects. Banking and Safety: The banking industry often deals with cases of card fraud, security fraud, ticks and such other issues that greatly hamper their functioning as well as market reputation. Hence to tackle that, the securities exchange commission aka SEC takes the help of big data and monitors the financial market activity.  This has further helped them manage a safer environment for highly valuable customers like retail traders, hedge funds, big banks and other eminent individuals in the financial market. Big data has helped this industry in the cases like anti-money laundering, fraud mitigation, demand enterprise risk management and other cases of risk analytics.  Media and Entertainment industry It is needless to say that the media and entertainment industry heavily depends on the verdict of the consumers and this is why they are always required to put up their best game. For that, they require to understand the current trends and demands of the public, which is also something that changes rapidly these days. To get an in-depth understanding of consumer behaviour and their needs, the media and entertainment industry collects, analyses and utilises customer insights. They leverage mobile and social media content to understand the patterns at a real-time speed.  The industry leverages Big data to run detailed sentiment analysis to pitch the perfect content to the users. Some of the biggest names in the entertainment industry such as Spotify and Amazon Prime are known for using big data to provide accurate content recommendations to their users, which helps them improve their customer satisfaction and, therefore, increases customer retention.  Healthcare Industry Even though the healthcare industry generates huge volumes of data on a daily basis which can be ustilised in many ways to improve the healthcare industry, it fails to utilise it completely due to issues of usability of it. Yet there is a significant number of areas where the healthcare industry is continuously utilising Big Data. The main area where the healthcare industry is actively leveraging big data is to improve hospital administration so that patients can revoke best-in-class clinical support. Apart from that, Big Data is also used in fighting lethal diseases like cancer. Big Data has also helped the industry to save itself from potential frauds and committing usual man-made errors like providing the wrong dosage, medicine etc.  Education Similar to the society that we live in, the education system is also evolving. Especially after the pandemic hit hard, the change became even more rapid. With the introduction of remote learning, the education system transformed drastically, and so did its problems. On that note, Big Data significantly came in handy, as it helped educational institutions to get the insights that can be used to take the right decisions suitable for the circumstances. Big Data helped educators to understand the importance of creating a unique and customised curriculum to fight issues like students not being able to retain attention.  It not only helped improve the educational system but to identify the student’s strengths and channeled them right.  Government and Public Services Likewise the field of government and public services itself, the applications of Big Data by them are also extensive and diverse. Government leverages big data mostly in areas like financial market analysis, fraud detection, energy resource exploration, environment protection, public-health-related research and so forth.  The Food and Drug Administration (FDA) actively uses Big Data to study food-related illnesses and disease patterns.  Retail and Wholesale Industry In spite of having tons of data available online in form of reviews, customer loyalty cards, RFID etc. the retail and wholesale industry is still lacking in making complete use of it. These insights hold great potential to change the game of customer experience and customer loyalty.  Especially after the emergence of e-commerce, big data is used by companies to create custom recommendations based on their previous purchasing behaviour or even from their search history.  In the case of brick-and-mortar stores as well, big data is used for monitoring store-level demand in real-time so that it can be ensured that the best-selling items remain in stock. Along with that, in the case of this industry, data is also helpful in improving the entire value chain to increase profits.   Manufacturing and Resources Industry The demand for resources of every kind and manufactured product is only increasing with time which is making it difficult for industries to cope. However, there are large volumes of data from these industries that are untapped and hold the potential to make both industries more efficient, profitable and manageable.  By integrating large volumes of geospatial and geographical data available online, better predictive analysis can be done to find the best areas for natural resource explorations. Similarly, in the case of the manufacturing industry, Big Data can help solve several issues regarding the supply chain and provide companies with a competitive edge.  Insurance Industry  The insurance industry is anticipated to be the highest profit-making industry but its vast and diverse customer base makes it difficult for it to incorporate state-of-the-art requirements like personalized services, personalised prices and targeted services. To tackle these prime challenges Big Data plays a huge part. Big data helps this industry to gain customer insights that further help in curating simple and transparent products that match the recruitment of the customers. Along with that, big data also helps the industry analyse and predict customer behaviours and results in the best decision-making for insurance companies. Apart from predictive analytics, big data is also utilised in fraud detection.  How do you create a big data project? Creating a big data project involves several key steps and considerations. Here’s a general outline to guide you through the process: Define Objectives: Clearly define the objectives and goals of your big data project. Identify the business problems you want to solve or the insights you aim to gain from the data. Data Collection: Determine the sources of data you need for your project. It could be structured data from databases, unstructured data from social media or text documents, or semi-structured data from log files or XML. Plan how you will collect and store this data. Data Storage: Choose a suitable storage solution for your data. Depending on the volume and variety of data, you may consider traditional databases, data lakes, or distributed file systems like Hadoop HDFS. Data Processing: Determine how you will process and manage your big data. This step usually involves data cleaning, transformation, and integration. Technologies like Apache Spark or Apache Hadoop MapReduce are commonly used for large-scale data processing. Data Analysis: Perform exploratory data analysis to gain insights and understand patterns within the data. Use data visualization tools to present the findings. Implement Algorithms: If your project involves machine learning or advanced analytics, implement relevant algorithms to extract meaningful information from the data. Performance Optimization: Big data projects often face performance challenges. Optimize your data processing pipelines, algorithms, and infrastructure for efficiency and scalability. Data Security and Privacy: Ensure that your project adheres to data security and privacy regulations. Implement proper data access controls and anonymization techniques if needed. Deploy and Monitor: Deploy your big data project in a production environment and set up monitoring to track its performance and identify any issues. Evaluate Results: Continuously evaluate the results of your big data project against the defined objectives. Refine and improve your approach based on feedback and insights gained from the project. Documentation: Thoroughly document each step of the project, including data sources, data processing steps, analysis methodologies, and algorithms used. This documentation will be valuable for future reference and for collaborating with others. Team Collaboration: Big data projects often involve collaboration between various teams, such as data engineers, data scientists, domain experts, and IT professionals. Effective communication and collaboration are crucial for the success of the project. The Key Elements of a Good Big Data Project Before you learn about different big data projects, you should understand the criteria for evaluating them: Quality Over Quantity In the field of big data, it is a common tendency to prioritize quantity. However, quality should be a major focus while selecting data to analyze. The ultimate goal of big data analysis is nothing different from other analytical tasks. It involves driving important insights to fulfill business objectives and make major decisions. So, it’s extremely crucial to collect data from the right sources for analysis. You can explore different resources before finding the absolute best for collecting data. Additionally, you will have to find the right algorithms for processing data and interpreting everything accurately. Concentrate on Outcome and Impact The purpose of big data projects is to meet several business objectives. So, your focus shouldn’t be on using more data or utilizing more tools to perform big data analysis. Instead, you should improve the impact of big data projects to allow organizations to develop better strategies. Clean Code and Analysis This aspect of big data projects will depend on your work mechanism as an individual or a team. It’s extremely vital to generate clean code. Therefore, your code should be formatted in the right way and contain comments in the necessary places. A clean code makes it easy to proceed with big data projects. Even your colleagues will find it easier to proceed with the project at a later point when you might not be available. While writing code for data analysis, rely on fair and goal-oriented methodologies. Emotions and biases can easily mess with the accuracy of your data analysis. So, you should stay away from these mistakes while writing code for big data projects. What problems you might face in doing Big Data Projects Big data is present in numerous industries. So you’ll find a wide variety of big data project topics to work on too. Apart from the wide variety of project ideas, there are a bunch of challenges a big data analyst faces while working on such projects. They are the following: Limited Monitoring Solutions You can face problems while monitoring real-time environments because there aren’t many solutions available for this purpose. That’s why you should be familiar with the technologies you’ll need to use in big data analysis before you begin working on a project. Timing Issues A common problem among data analysis is of output latency during data virtualization. Most of these tools require high-level performance, which leads to these latency problems. Due to the latency in output generation, timing issues arise with the virtualization of data. The requirement of High-level Scripting When working on big data analytics projects, you might encounter tools or problems which require higher-level scripting than you’re familiar with. In that case, you should try to learn more about the problem and ask others about the same. Data Privacy and Security While working on the data available to you, you have to ensure that all the data remains secure and private. Leakage of data can wreak havoc to your project as well as your work. Sometimes users leak data too, so you have to keep that in mind. Knowledge Read: Big data jobs & Career planning Unavailability of Tools You can’t do end-to-end testing with just one tool. You should figure out which tools you will need to use to complete a specific project. When you don’t have the right tool at a specific device, it can waste a lot of time and cause a lot of frustration. That is why you should have the required tools before you start the project. Check out big data certifications at upGrad Too Big Datasets You can come across a dataset which is too big for you to handle. Or, you might need to verify more data to complete the project as well. Make sure that you update your data regularly to solve this problem. It’s also possible that your data has duplicates, so you should remove them, as well. While working on big data projects, keep in mind the following points to solve these challenges:         Use the right combination of hardware as well as software tools to make sure your work doesn’t get hampered later on due to the lack of the same.         Check your data thoroughly and get rid of any duplicates.         Follow Machine Learning approaches for better efficiency and results.         What are the technologies you’ll need to use in Big Data Analytics Projects: We recommend the following technologies for beginner-level big data projects:         Open-source databases         C++, Python         Cloud solutions (such as Azure and AWS)         SAS         R (programming language)         Tableau         PHP and Javascript Each of these technologies will help you with a different sector. For example, you will need to use cloud solutions for data storage and access. On the other hand, you will need to use R for using data science tools. These are all the problems you need to face and fix when you work on big data project ideas.  If you are not familiar with any of the technologies we mentioned above, you should learn about the same before working on a project. The more big data project ideas you try, the more experience you gain. Otherwise, you’d be prone to making a lot of mistakes which you could’ve easily avoided. So, here are a few Big Data Project ideas which beginners can work on: Read: Career in big data and its scope. Big Data Project Ideas: Beginners Level This list of big data project ideas for students is suited for beginners, and those just starting out with big data. These big data project ideas will get you going with all the practicalities you need to succeed in your career as a big data developer. Further, if you’re looking for big data project ideas for final year, this list should get you going. So, without further ado, let’s jump straight into some big data project ideas that will strengthen your base and allow you to climb up the ladder. We know how challenging it is to find the right project ideas as a beginner. You don’t know what you should be working on, and you don’t see how it will benefit you. That’s why we have prepared the following list of big data projects so you can start working on them: Let’s start with big data project ideas. Fun Big Data Project Ideas Social Media Trend Analysis: Gather data from various platforms and analyze trends, topics, and sentiment. Music Recommender System: Build a personalized music recommendation engine based on user preferences. Video Game Analytics: Analyze gaming data to identify patterns and player behavior. Real-time Traffic Analysis: Use data to create visualizations and optimize traffic routes. Energy Consumption Optimization: Analyze energy usage data to propose energy-saving strategies. Predicting Box Office Success: Develop a model to predict movie success based on various factors. Food Recipe Recommendation: Recommend recipes based on dietary preferences and history. Wildlife Tracking and Conservation: Use big data to track and monitor wildlife for conservation efforts. Fashion Trend Analysis: Analyze fashion data to identify trends and popular styles. Online Gaming Community Analysis: Understand player behavior and social interactions in gaming communities. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript 1. Classify 1994 Census Income Data One of the best ideas to start experimenting you hands-on big data projects for students is working on this project. You will have to build a model to predict if the income of an individual in the US is more or less than $50,000 based on the data available. A person’s income depends on a lot of factors, and you’ll have to take into account every one of them. You can find the data for this project here. 2. Analyze Crime Rates in Chicago Law enforcement agencies take the help of big data to find patterns in the crimes taking place. Doing this helps the agencies in predicting future events and helps them in mitigating the crime rates. You will have to find patterns, create models, and then validate your model. You can get the data for this project here. 3. Text Mining Project This is one of the excellent deep learning project ideas for beginners. Text mining is in high demand, and it will help you a lot in showcasing your strengths as a data scientist. In this project, you will have to perform text analysis and visualization of the provided documents.   You will have to use Natural Language Process Techniques for this task. You can get the data here. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Big Data Project Ideas: Advanced Level 4. Big Data for cybersecurity This project will investigate the long-term and time-invariant dependence relationships in large volumes of data. The main aim of this Big Data project is to combat real-world cybersecurity problems by exploiting vulnerability disclosure trends with complex multivariate time series data. This cybersecurity project seeks to establish an innovative and robust statistical framework to help you gain an in-depth understanding of the disclosure dynamics and their intriguing dependence structures. 5. Health status prediction This is one of the interesting big data project ideas. This Big Data project is designed to predict the health status based on massive datasets. It will involve the creation of a machine learning model that can accurately classify users according to their health attributes to qualify them as having or not having heart diseases. Decision trees are the best machine learning method for classification, and hence, it is the ideal prediction tool for this project. The feature selection approach will help enhance the classification accuracy of the ML model. 6. Anomaly detection in cloud servers In this project, an anomaly detection approach will be implemented for streaming large datasets. The proposed project will detect anomalies in cloud servers by leveraging two core algorithms – state summarization and novel nested-arc hidden semi-Markov model (NAHSMM). While state summarization will extract usage behaviour reflective states from raw sequences, NAHSMM will create an anomaly detection algorithm with a forensic module to obtain the normal behaviour threshold in the training phase. 7. Recruitment for Big Data job profiles Recruitment is a challenging job responsibility of the HR department of any company. Here, we’ll create a Big Data project that can analyze vast amounts of data gathered from real-world job posts published online. The project involves three steps: Identify four Big Data job families in the given dataset. Identify nine homogeneous groups of Big Data skills that are highly valued by companies.  Characterize each Big Data job family according to the level of competence required for each Big Data skill set. The goal of this project is to help the HR department find better recruitments for Big Data job roles. 8. Malicious user detection in Big Data collection This is one of the trending deep learning project ideas. When talking about Big Data collections, the trustworthiness (reliability) of users is of supreme importance. In this project, we will calculate the reliability factor of users in a given Big Data collection. To achieve this, the project will divide the trustworthiness into familiarity and similarity trustworthiness. Furthermore, it will divide all the participants into small groups according to the similarity trustworthiness factor and then calculate the trustworthiness of each group separately to reduce the computational complexity. This grouping strategy allows the project to represent the trust level of a particular group as a whole.  9. Tourist behaviour analysis This is one of the excellent big data project ideas. This Big Data project is designed to analyze the tourist behaviour to identify tourists’ interests and most visited locations and accordingly, predict future tourism demands. The project involves four steps:  Textual metadata processing to extract a list of interest candidates from geotagged pictures.  Geographical data clustering to identify popular tourist locations for each of the identified tourist interests.  Representative photo identification for each tourist interest.  Time series modelling to construct a time series data by counting the number of tourists on a monthly basis.  10. Credit Scoring This project seeks to explore the value of Big Data for credit scoring. The primary idea behind this project is to investigate the performance of both statistical and economic models. To do so, it will use a unique combination of datasets that contains call-detail records along with the credit and debit account information of customers for creating appropriate scorecards for credit card applicants. This will help to predict the creditworthiness of credit card applicants. 11. Electricity price forecasting This is one of the interesting big data project ideas. This project is explicitly designed to forecast electricity prices by leveraging Big Data sets. The model exploits the SVM classifier to predict the electricity price. However, during the training phase in SVM classification, the model will include even the irrelevant and redundant features which reduce its forecasting accuracy. To address this problem, we will use two methods – Grey Correlation Analysis (GCA) and Principle Component Analysis. These methods help select important features while eliminating all the unnecessary elements, thereby improving the classification accuracy of the model. 12. BusBeat BusBeat is an early event detection system that utilizes GPS trajectories of periodic-cars travelling routinely in an urban area. This project proposes data interpolation and the network-based event detection techniques to implement early event detection with GPS trajectory data successfully. The data interpolation technique helps to recover missing values in the GPS data using the primary feature of the periodic-cars, and the network analysis estimates an event venue location. 13. Yandex.Traffic Yandex.Traffic was born when Yandex decided to use its advanced data analysis skills to develop an app that can analyze information collected from multiple sources and display a real-time map of traffic conditions in a city. After collecting large volumes of data from disparate sources, Yandex.Traffic analyses the data to map accurate results on a particular city’s map via Yandex.Maps, Yandex’s web-based mapping service. Not just that, Yandex.Traffic can also calculate the average level of congestion on a scale of 0 to 10 for large cities with serious traffic jam issues. Yandex.Traffic sources information directly from those who create traffic to paint an accurate picture of traffic congestion in a city, thereby allowing drivers to help one another. Additional Topics         Predicting effective missing data by using Multivariable Time Series on Apache Spark         Confidentially preserving big data paradigm and detecting collaborative spam         Predict mixed type multi-outcome by using the paradigm in healthcare application         Use an innovative MapReduce mechanism and scale Big HDT Semantic Data Compression         Model medical texts for Distributed Representation (Skip Gram Approach based) Learn: Mapreduce in big data More Fun Big Data Projects Some more exciting big data projects to develop your skills include: Traffic Control Using Big Data Traffic issues are a common burden for many major cities, especially during peak hours. To address this problem, regularly monitoring popular and alternate routes for traffic may provide some relief. Leveraging the power of big data projects with real-time traffic simulation and predictions offers numerous advantages. In fact, this cutting-edge technology has already demonstrated success in effectively modeling traffic patterns. Take, for example, the Lambda Architecture program designed to tackle traffic challenges in Chicago. By tracking over 1,250 city roads, this program provides up-to-date information on traffic flow and traffic violations. Search Engines Search engines manage trillions of network objects and track online user movements to decode their search requests. But how do search engines make sense of all this information? They do so by transforming the vast amount of website content into measurable data. This presents an exciting opportunity for curious newbies looking to delve into the world of big data projects and Hadoop. Specifically, they can hone their skills in querying and analyzing data with the help of Apache Hive. With its SQL-like interface, Hive offers a user-friendly way to access data from a variety of Hadoop-based databases. Anyone already familiar with SQL will find this project easy to complete. Medical Insurance Fraud Detection Medical insurance fraud detection is quite easy with cutting-edge data science methodologies. By leveraging real-time analysis and advanced classification algorithms, this approach can promote trust in the medical insurance industry. It is one of the big data projects that address the issue of healthcare costs alongside preventing fraud. This project harnesses the power of data analytics to uncover critical links between healthcare professionals. Data Warehouse Design If you are interested in big data projects related to e-commerce sites, this one is recommended for you. Your task will be to construct a data warehouse for a retail enterprise. This project has a particular focus on optimizing pricing and inventory allocation. This project will help identify whether certain markets have an inclination toward high-priced products. Moreover, it will help you understand whether price adjustment or inventory redistribution is necessary according to locations. Get ready to harness the power of big data to uncover valuable insights in these areas. Recommendation System The vast world of online services offers access to an endless array of items. You will find music, video clips, and more. Big data can help create recommendation systems that will provide you with tailored suggestions. All big data projects analyze user data to effectively offer recommendations. They will consider browsing history and other metrics to come up with the right suggestions. In this specific big data project, you will have to leverage different recommendation models available on the Hadoop Framework. This will ensure that you understand which model will deliver optimal outcomes. Wikipedia Trend Visualization Human brains get exposed to different formats of data. But our brains are programmed to understand visual data better than anything else. In fact, the brain can comprehend visual data within only 13 milliseconds. Wikipedia is a go-to destination for a vast number of individuals all over the world for research purposes or general knowledge. At times, people visit these pages out of pure curiosity. The endless amount of data within its pages can be harnessed and refined through the use of Hadoop. By utilizing Zeppelin notebooks, this data can then be transformed into visually appealing insights. This will enable a deeper understanding of trends and patterns across different demographics and parameters. Therefore, it is one of the best big data projects to understand the potential of visualization. Website Clickstream Data Visualization Clickstream data analysis is about understanding the web pages visited by a specific user. This type of analysis helps with web page marketing and product management. Moreover, clickstream data analysis can help with creating targeted advertisements. Users will always visit websites according to their interests and needs. So, clickstream analysis is all about figuring out what a user is on the lookout for. It is one of the big data projects that need the Hadoop framework. Image Caption Generation The growing influence of social media requires businesses to produce engaging content. Catchy images are definitely important on social media profiles. But businesses also need to add attractive captions to describe the images. With captions and useful hashtags, businesses are able to reach the intended target audience more easily. Producing relevant captions for images requires dealing with large datasets. Therefore, image caption generation can be one of the most interesting big data projects. This project involves processing images with the help of deep learning techniques. It helps in understanding the image and creating appealing captions with AI. Python is often the source code behind these big data projects. So, it is better to proceed with this project after working on something with Python as the source code. GIS Analytics for Effective Waste Management Large amounts of waste pose a threat to the environment and our well-being. Proper waste management is necessary for addressing this issue. Waste management is not just about collecting unwanted items and their disposal. It also involves the transportation and recycling of waste. Waste management can be one of the most interesting big data projects by leveraging the power of GIS modeling. These models can help create a strategic path for collecting waste. Moreover, data experts can create routes to dispose of waste at designated areas like landfills or recycling centers. Additionally, these big data projects can help find ideal locations for landfills. These projects can also help with the proper placement of garbage bins all over the city. Network Traffic and Call Data Analysis The telecommunication industry produces heaps of data every day. But only a small amount of this data can be useful for improving business practices. The real challenge is in dealing with such vast volumes of data in real time. One of the most interesting big data projects is analyzing the data available in the telecommunications sector. It will help the telecom industry to undertake decisions regarding the improvement of customer experience. This big data project will involve analyzing the network traffic. As a result, it will become easier to address issues like network interruptions and call drops. By assessing the usage patterns of customers, telecom companies will be able to create better service plans. As a result, customers will be satisfied with plans that fulfill their overall needs. The tools used for this kind of big data project will depend on its complexity. Fruit Image Classification This can be one of the most interesting big data projects with professionals working on a mobile application. It will be a mobile app capable of providing insights about fruit harvesting by analyzing different pictures. This project will involve leveraging AWS cloud tools to develop a data processing chain. Some steps in this chain will include dimensionality reduction and operating a fruit image classification engine. While working on this big data project, you will have to generate PySpark scripts. Your task will become easier with a big data architecture created on an EC2 Linux server. Due to its compatibility with AWS, DataBricks is also ideal for this project. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Conclusion In this article, we have covered top big data project ideas. We started with some beginner projects which you can solve with ease. Once you finish with these simple projects, I suggest you go back, learn a few more concepts and then try the intermediate projects. When you feel confident, you can then tackle the advanced projects. If you wish to improve your big data skills, you need to get your hands on these big data project ideas. Working on big data projects will help you find your strong and weak points. Completing these projects will give you real-life experience of working as a data scientist. If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career.
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by upGrad

16 Jan 2024

Be A Big Data Analyst – Skills, Salary & Job Description
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In an era dominated by Big Data, one cannot imagine that the skill set and expertise of traditional Data Analysts are enough to handle the complexities of Big Data. So, who then jumps in to save the day? Essentially, Big Data Analysts are data analysts in the truest sense of the term, but they have a significant point of difference – unlike traditional Data Analysts who mostly deal with structured data, Big Data Analysts deal with Big Data that primarily consists of raw unstructured and semi-structured data. Naturally, the job responsibilities and skill set of Big Data Analysts differs from traditional Data Analysts. To help you understand the job profile of a Big Data Analyst, we’ve created this guide containing a detailed description of job responsibilities, skill set, salary, and the career path to becoming a Big Data Analyst. Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses Who is a Big Data Analyst? The job description of a Data Scientist and Big Data Analyst often overlap, since they both deal in Big Data. Just as Data Scientists are engaged in gathering, cleaning, processing, and analyzing Big Data to uncover meaningful insights and patterns, Big Data Analysts are responsible for understanding those insights and identifying ways to transform them into actionable business strategies. Big Data Analysts have one fundamental aim – to help businesses realize the true potential of Big Data in positively influencing business decisions. Big Data Analysts gather data from multiple sources and company data warehouses and analyze and interpret the data to extract such information that can be beneficial for businesses. They must visualize and report their findings by preparing comprehensive reports, graphs, charts, etc. Visual representation of the data findings helps all the stakeholders (both technical and non-technical) to understand it better. Once everyone can visualize the idea clearly, the entire IT and business team can brainstorm as to how to use the insights in improving business decisions, boosting revenues, influencing customer decisions, enhancing customer satisfaction, and much more. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Big Data Analysts are also summoned by businesses to perform competitive market analysis tasks to identify key industry trends. To analyze and interpret data, Big Data Analysts spend a lot of time working with a host of business, Big Data, and analytics tools like Microsoft Excel, MS Office, SAS, Tableau, QlikView, Hadoop, Spark, MongoDB, Cassandra, Hive, Pig, R, Python, SQL, to name a few. What are the job responsibilities of a Big Data Analyst? Now that you have a fair understanding of the job profile of a Big Data Analyst, let’s look at their core duties: To gather and accumulate data from disparate sources, clean it, organize it, process it, and analyze it to extract valuable insights and information. To identify new sources of data and develop methods to improve data mining, analysis, and reporting. To write SQL queries to extract data from the data warehouse. To create data definitions for new database files or alterations made to the already existing ones for analysis purposes. To present the findings in reports (in table, chart, or graph format) to help the management team in the decision-making process. To develop relational databases for sourcing and collecting data. To monitor the performance of data mining systems and fix issues, if any. To apply statistical analysis methods for consumer data research and analysis purposes. To keep track of the trends and correlational patterns among complex data sets. To perform routine analysis tasks to support day-to-day business functioning and decision making. To collaborate with Data Scientists to develop innovative analytical tools. To work in close collaboration with both the IT team and the business management team to accomplish company goals. What are the skills required to become a Big Data Analyst? Programming A Big Data Analyst must be a master coder and should be proficient in at least two programming languages (the more, the merrier). Coding is the base for performing numerical and statistical analysis on large data sets. Some of the most commonly used programming languages in Big Data analysis are R, Python, Ruby,  C++, Java, Scala, and Julia. Start small and master one language first. Once you get the hang of it, you can easily pick up other programming languages as well. Quantitative Aptitude  To perform data analysis, you must possess a firm grasp over Statistics and Mathematics including Linear Algebra, Multivariable Calculus, Probability Distribution, Hypothesis Testing, Bayesian Analysis, Time Series and Longitudinal Analysis, among other things. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Checkout: Data Analyst Salary in India Knowledge of computational frameworks The job of a Big Data Analyst is a versatile one. Thus, you need to be comfortable in working with multiple technologies and computational frameworks including both basic tools like Excel and SQL as well as advanced frameworks like Hadoop, MapReduce, Spark, Storm, SPSS, Cognos, SAS, and MATLAB. Data warehousing skills Every Big Data Analyst must have proficiency in working with both relational and non-relational database systems such as MySQL, Oracle, DB2, NoSQL, HDFS, MongoDB, CouchDB, Cassandra, to name a few. Business acumen What good would be the findings of Big Data Analysts if they couldn’t visualize them from a business angle? To use the extracted insights to transform businesses for the better, a Big Data Analyst must possess an acute understanding of the business world. Only then can they identify potential business opportunities and use the data findings to steer the business in a better direction. Communication Skills As we mentioned earlier, Big Data Analysts must know how to effectively convey and present their findings for the ease of understanding of others. Hence, they need to possess impeccable written and verbal communication skills so that they can explain their vision to others and breakdown complex ideas into simpler terms. Data Visualization  Trends, patterns, and outliers in your data are communicated through visualizations. If you’re relatively fresh to data analysis and searching for an initiative, making visualizations is a wonderful place to start. Choose graphs that are appropriate for the narrative you’re attempting to portray. Bar and line charts portray changes over duration concisely, whereas pie charts represent part-to-whole correlations. On the other hand, histograms and bar charts depict data distribution.  An instance of data visualization is when data analyst Han visualizes the level of expertise needed in 60 distinct activities to determine which one is the most difficult. Following are some excellent examples of data analytics projects for beginners using data visualization: Astronomical Visualization History Visualization Instagram Visualization Here are a few free data visualizations tools you can use for your data analyst projects: Google Charts This data visualization tool and interactive chart gallery make it simple to insert visualizations inside a portfolio using JavaScript code and HTML. A comprehensive Guides feature walks you through the design process. RAW Graphs This free and open-source web application makes it simple to convert CSV files or spreadsheets into a variety of chart kinds that would be challenging to create otherwise. You can even play with example data sets provided by the program. Data-Driven Documents or D3 You can accomplish a lot using this JavaScript package if you have basic technical knowledge. Data Mining The procedure of converting raw data into meaningful information is known as data mining. One of the data mining assignments you can conduct to boost your data analyst portfolio is Speech Recognition. Speech recognition software recognizes spoken words and converts them to written content. Install speech recognition programs in Python, such as SpeechRecognition, Watson-developer-cloud, or Apiai. DeepSpeech is a free and open-source engine that is based on TensorFlow by Google. You can use the application to convert speeches into texts. Another data analytics example using data mining is Anime Recommendation System. While streaming recommendations are great, why not create one targeting a certain genre? You can make use of user preference data and develop multiple recommendation systems by categorizing related shows based on characters, reviews, and synopses.  Natural Language Processing (NLP) NLP is an area of artificial intelligence that assists computers in interpreting and manipulating natural language in the manner of audio and text. To acquire a higher senior-level role, try to include some of the following NLP projects works in your portfolio: Autocorrect and Autocomplete In Python, you can generate a neural network that autocompletes phrases and identifies grammatical problems.  News Translation Python can be used to create a web-based program that converts news from one particular language into another. Salary of a Big Data Analyst According to Glassdoor, the average salary of a Big Data Analyst is Rs. 6,54,438 in India. The salary of Big Data professionals depends on many factors including educational background, level of expertise in Big Data, years of working experience, and so on. The entry-level salaries can be anywhere between 5 – 6 LPA, the salary increases exponentially with experience and upskilling. Experienced Big Data Analyst can earn as high as 25 LPA, depending upon the company they work for. Steps to launch a career as a Big Data Analyst Here’s how you can launch your career as a Big Data Analyst in three simple steps: Graduate with a Bachelor’s degree with STEM (science, technology, engineering, or math) background. While the job profile of a Big Data Analyst doesn’t demand highly advanced degrees,  most companies look for candidates who’ve graduated with a Bachelor’s degree with a specialization in STEM subjects. This is the minimum selection criteria for the job, so you have to make sure you attain it. Learning STEM subjects will introduce you to the fundamentals of Data Science, including programming, statistical, and mathematical skills. As for project management and database management, you can take special classes for these. Get an internship or entry-level job in data analysis. While it is difficult to bag data analysis jobs with zero experience in the field, you must always be on the lookout for opportunities. Many institutions or companies offer internship programs in data analysis, which could be a great start to your career. Then there are also various in-house training programs in Big Data Management, Statistical Analysis, etc. Enrolling into such programs will help you gain the necessary skills required for data analysis. Another option is to look for entry-level jobs related to this field, such as that of a Statistician, or a Junior Business Analyst/Data Analyst. Needless to say, these positions will not only help further your training, but they will also act as a stepping stone to a Big Data career. Get an advanced degree. Once you obtain working experience, it is time to amp up your game. How so? By getting an advanced degree like a Master’s degree in Data Science, or Data Analytics, or Big Data Management. Having an advanced degree will enhance your resume and open up new vistas for employment in high-level data analysis positions. Naturally, your prospective salary package will also increase by a great extent if you have a Master’s degree or equivalent certification. Preparing for a data analyst role? Sharpen your interview skills with our comprehensive list of data analyst interview questions and answers to confidently tackle any challenge thrown your way. Job Outlook of Big Data Analysts According to the predictions of the World Economic Forum, Data Analysts will be in high demand in companies all around the world. Furthermore, the US Bureau of Labor Statistics (BLS) maintains that employment opportunities for market research analysts, including Data Analysts, will grow by 19% between 2014 to 2024. This is hardly surprising since the pace at which data is increasing every day, companies will need to hire more and more skilled Data Science professionals to meet their business needs. All said and done, the career outlook of Big Data Analysts looks extremely promising. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? What are you waiting for? We’ve provided you with all the vital information you need to gear up for a career as a Big Data Analyst. The ball’s in your court! If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career.
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by upGrad

16 Dec 2023

12 Exciting Hadoop Project Ideas & Topics For Beginners [2024]
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Hadoop Project Ideas & Topics Today, big data technologies power diverse sectors, from banking and finance, IT and telecommunication, to manufacturing, operations and logistics. Most of the Hadoop project ideas out there focus on improving data storage and analysis capabilities. With Apache Hadoop frameworks, modern enterprises can minimize hardware requirements and develop high-performance distributed applications. Read: Apache Spark vs Hadoop Mapreduce Introducing Hadoop Hadoop is a software library designed by the Apache Foundation to enable distributed storage and processing of massive volumes of computation and datasets. This open-source service supports local computing and storage can deal with faults or failures at the application layer itself. It uses the MapReduce programming model to bring the benefits of scalability, reliability, and cost-effectiveness to the management of large clusters and computer networks.  Why Hadoop projects Apache Hadoop offers a wide range of solutions and standard utilities that deliver high throughput analysis, cluster resource management, and parallel processing of datasets. Coming to Hadoop tools, here are some of the modules supported by the software: Hadoop MapReduce Hadoop Distributed File System or HDFS Hadoop YARN Note that technology companies like Amazon Web Services, IBM Research, Microsoft, Hortonworks, and many others deploy Hadoop for a variety of purposes. It is an entire ecosystem replete with features that allow users to acquire, organize, process, analyze, and visualize data. So, let us explore the system tools through a set of exercises.  Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Hadoop Project Ideas For Beginners 1. Data migration project Before we go into the details, let us first understand why you would want to migrate your data to the Hadoop ecosystem. Present-day managers emphasize on using technological tools that assist and improve decision-making within dynamic market environments. While legacy software like a relational database management system (RDBMS) help store and manage data for business analysis, they pose a limitation when a more substantial amount of data is involved. It becomes challenging to alter tables and accommodate big data with such traditional competencies, which further affects the performance of the production database. Under such conditions, smart organizations prefer the toolsets offered by Hadoop. Its powerful commodity hardware can significantly capture insights for massive pools of data. This is particularly true for operations like Online Analytical Processing or OLAP.  Now, let us see how you can migrate RDBMS data to Hadoop HDFS.  You can use Apache Sqoop as an intermediate layer to import data from a MySQL to the Hadoop system, and also to export data from HDFS to other relational databases. Sqoop comes with Kerberos security integration and Accumulo support. Alternatively, you can use the Apache Spark SQL module if you want to work with structured data. Its fast and unified processing engine can execute interactive queries and streaming data with ease.  2. Corporate data integration When organizations first replace centralized data centers with dispersed and decentralized systems, they sometimes end up using separate technologies for different geographical locations. But when it comes to analytics, it makes sense for them to want to consolidate data from multiple heterogeneous systems (often from different vendors). And herein comes the Apache Hadoop enterprise resource with its modular architecture.  For example, its purpose-built data integration tool, Qlick (Attunity), helps users configure and execute migration jobs via a drag-and-drop GUI. Additionally, you can freshen up your Hadoop data lakes without hindering the source systems.  Check out: Java Project Ideas & Topics for Beginners 3. A use case for scalability  Growing data stacks mean slower processing times, which hampers the procedure of information retrieval. So, you can take up an activity-based study to reveal how Hadoop can deal with this issue.  Apache Spark—running on top of the Hadoop framework to process MapReduce jobs simultaneously—ensures efficient scalability operations. This Spark-based approach can help you to get an interactive stage for processing queries in near real-time. You can also implement the traditional MapReduce function if you are just starting with Hadoop.  Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses 4. Cloud hosting In addition to hosting data on on-site servers, Hadoop is equally adept at cloud deployment. The Java-based framework can manipulate data stored in the cloud, which is accessible via the internet. Cloud servers cannot manage big data on their own without a Hadoop installation. You can demonstrate this Cloud-Hadoop interaction in your project and discuss the advantages of cloud hosting over physical procurement.  5. Link prediction for social media sites The application of Hadoop also extends to dynamic domains like social network analysis. In such advanced scenarios where variables have multiple relationships and interactions, we require algorithms to predict which nodes could be connected. Social media is a storehouse of links and inputs, such as age, location, schools attended, occupation, etc. This information can be used to suggest pages and friends to users via graph analysis. This process would involve the following steps: Storing nodes/edges in HBase Aggregating relevant data  Returning and storing intermediate results back to HBase Collecting and processing parallel data in a distributed system (Hadoop) Network clustering using k-means or MapReduce implementations You can follow a similar method to create an anomaly predictor for financial services firms. Such an application would be equipped to detect what types of potential fraud particular customers could commit.  6. Document analysis application With the help of Hadoop and Mahout, you can get an integrated infrastructure for document analysis. The Apache Pig platform matches the needs, with its language layer, for executing Hadoop jobs in the MapReduce and achieving a higher-level abstraction. You can then use a distance metric to rank the documents in text search operations.  7. Specialized analytics You can select a project topic that addresses the unique needs of a specific sector. For instance, you can apply Hadoop in the Banking and Finance industry for the following tasks: Distributed storage for risk mitigation or regulatory compliance Time series analysis Liquidity risk calculation Monte Carlo simulations  Hadoop facilitates the extraction of relevant data from warehouses so that you can perform a problem-oriented analysis. Earlier, when proprietary packages were the norm, specialized analytics suffered challenges related to scaling and limited feature sets.  8. Streaming analytics In the fast-paced digital era, data-driven businesses cannot afford to wait for periodic analytics. Streaming analytics means performing actions in batches or a cyclical manner. Security applications use this technique to track and flag cyber attacks and hacking attempts.  In the case of a small bank, a simple combination of Oracle and VB code could run a job to report abnormalities and trigger suitable actions. But a statewide financial institution would need more powerful capabilities, such as those catered by Hadoop. We have outlined the step-by-step mechanism as follows: Launching a Hadoop cluster Deploying a Kafka server Connecting Hadoop and Kafka Performing SQL analysis over HDFS and streaming data  Read: Big Data Project Ideas & Topics  In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses 9. Streaming ETL solution As the title indicates, this assignment is about building and implementing Extract Transform Load (ETL) tasks and pipelines. The Hadoop environment contains utilities that take care of Source-Sink analytics. These are situations where you need to Capture streaming data and also warehouse it somewhere. Have a look at the tools below. Kudu HDFS HBase Hive 10. Text mining using Hadoop Hadoop technologies can be deployed for summarizing product reviews and conducting sentiment analysis. The product ratings given by customers can be classified under Good, Neutral, or Bad. Furthermore, you can bring slangs under the purview of your opinion mining project and customize the solution as per client requirements. Here is a brief overview of the modus operandi: Use a shell and command language to retrieve HTML data Store data in HDFS Preprocess data in Hadoop using PySpark Use an SQL assistant (for example, Hue) for initial querying Visualize data using Tableau 11. Speech analysis  Hadoop paves the way for automated and accurate speech analytics. Through this project, you can showcase the telephone-computer integration employed in a call center application. The call records can be flagged, sorted, and later analyzed to derive valuable insights. A combination of HDFS, MapReduce, and Hive combination works best for large-scale executions. Kisan Call Centers operating across multiple districts in India form a prominent use case.   12. Trend analysis of weblogs You can design a log analysis system capable of handling colossal quantities of log files dependably. A program like this would minimize the response time for queries. It would work by presenting users’ activity trends based on browsing sessions, most visited web pages, trending keywords, and so on.  Also read: How to Become a Hadoop Administrator 13. Predictive Maintenance for Manufacturing Minimizing downtime and improving equipment performance are crucial in the industrial sector. Making a predictive maintenance system is a simple Hadoop project concept. You may anticipate equipment failure and plan maintenance in advance by gathering and analyzing sensor data from the gear. This decreases downtime while also saving money by averting costly catastrophic failures. 14. Healthcare Analytics From patient records and diagnostic pictures to research data, the healthcare industry creates enormous volumes of data. Beginners might start a Hadoop project to examine medical records. You may look at things like predicting patient outcomes, finding illness outbreaks or getting recommendations for certain medicines. You may effectively handle and analyze huge medical datasets by utilizing Hadoop’s distributed computing capabilities. 15. Retail Inventory Optimization Retailers must effectively manage their inventory to prevent either overstocking or understocking of items. A Hadoop project for beginners can entail developing an inventory optimization system. You may create algorithms that assist merchants in making data-driven choices about inventory management by examining historical sales data and external factors like seasonality and promotions. 16. Natural Language Processing (NLP) Beginners may use Hadoop in the exciting subject of NLP. You may create text analytics programs for chatbots, sentiment analysis, or language translation. You may analyze massive text datasets to gain insightful information and enhance language-related applications by utilizing Hadoop’s parallel processing. 17. Energy Consumption Analysis Global energy usage is a serious issue. The analysis of energy consumption data from multiple sources, including smart meters, weather information, and building information, might be the subject of a Hadoop project. Consumers and organizations may improve their energy use and cut expenses by spotting trends and anomalies. 18. Recommender Systems Many industries, including e-commerce, content streaming, and others, employ recommender systems. You may use Hadoop to create a recommendation engine as a beginner’s project. You may create algorithms that propose goods or material catered to specific consumers. 19. Environmental Data Monitoring Environmental problems and climate change are major world concerns. Collecting and analyzing environmental data from sensors, satellites, and weather stations might be the focus of a Hadoop project for beginners. To track environmental changes, such as temperature patterns, pollution levels, and wildlife movements, you may develop visualizations and prediction algorithms. 20. Supply Chain Optimization Management of the supply chain is essential for companies to ensure effective product delivery. Supply chain operation optimization is a Hadoop project idea. You may create algorithms that improve supply chain efficiency and save costs by evaluating data on supplier performance, transportation routes, and demand changes. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Conclusion These Hadoop projects provide possibilities for hands-on learning about various aspects of Hadoop in big data analytics. With this, we have answered ‘What is big data Hadoop?’ and covered the top Hadoop project ideas. You can adopt a hands-on approach to learn about the different aspects of the Hadoop platform and become a pro at crunching big data! If you are interested to know more about Big Data, check out our If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. which is designed for working professionals and provides 7+ case studies & projects, covers 14 programming languages & tools, practical hands-on workshops, more than 400 hours of rigorous learning & job placement assistance with top firms. Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career.
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by Rohit Sharma

29 Nov 2023

Top 10 Exciting Data Engineering Projects & Ideas For Beginners [2024]
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Data engineering is an exciting and rapidly growing field that focuses on building, maintaining, and improving the systems that collect, store, process, and serve data. As more companies realize the value of their data, there is an increasing demand for data engineers who can design and implement data pipelines, warehouses, lakes, and other significant data infrastructures. If you’re interested in getting into data engineering or want to level up your skills with hands-on practice, taking on a few data projects is great. In this blog, I’ll share some of my favorite data engineering projects ideas suitable for beginners and more experienced engineers.  No Coding Experience Required. 360° Career support. PG Diploma in Machine Learning & AI from IIIT-B and upGrad.   You should note that you should be familiar with some topics and technologies before you work on these projects. Companies are always on the lookout for skilled data engineers who can develop innovative data engineering projects. So, if you are a beginner, the best thing you can do is work on some real-time data engineering projects. Working on a data engineering project will not only give you more insight into how data engineering works but will also strengthen your problem-solving skills when you encounter bugs inside the project and debug them yourself. We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting data engineering projects which beginners can work on to put their data engineering knowledge to test. In this article, you will find top data engineering projects for beginners to get hands-on experience. If you are a beginner and interested to learn more about data science, check out our data analytics courses from top universities. Amid the cut-throat competition, aspiring Developers must have hands-on experience with real-world data engineering projects. In fact, this is one of the primary recruitment criteria for most employers today. As you start working on data engineering projects, you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career. That’s because you’ll need to complete the projects correctly. Here are the most important ones: Python and its use in big data Extract Transform Load (ETL) solutions Hadoop and related big data technologies Concept of data pipelines Apache Airflow Also Read: Big Data Project Ideas What is a Data Engineer? Data engineers make raw data usable and accessible to other data professionals. Organizations have multiple sorts of data, and it’s the responsibility of data engineers to make them consistent, so data analysts and scientists can use the same. If data scientists and analysts are pilots, then data engineers are the plane-builders. Without the latter, the former can’t perform its tasks. Data engineering topics have been word of mouth everywhere in the domain of data science, from an analyst to a Big Data Engineer. Data engineers play a pivotal role in the data ecosystem, acting as the architects and builders of infrastructure that enables data analysis and interpretation. Their expertise extends beyond data collection and storage, encompassing the intricate task of transforming raw, disparate data into a harmonized and usable format. By designing robust data pipelines, data engineers ensure that data scientists and analysts have a reliable and structured foundation to conduct their analyses. These professionals possess a deep understanding of data manipulation tools, database systems, and programming languages, allowing them to orchestrate the seamless flow of information across various platforms. They implement strategies to optimize data retrieval, processing, and storage, accounting for scalability and performance considerations. Moreover, data engineers work collaboratively with data scientists, analysts, and other stakeholders to comprehend data requirements and tailor solutions accordingly. Essentially, data engineers are the architects of the data landscape, laying the groundwork for actionable insights and informed decision-making. As the data realm continues to evolve, the role of data engineers remains indispensable, ensuring that data flows seamlessly, transforms meaningfully, and empowers organizations to unlock the true potential of their data-driven endeavors. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Skills you need to become a Data Engineer As a Data Engineer you have to work on raw data and perform certain tasks on the data. Some tasks of a data engineer are: Acquiring and sourcing data from multiple places Cleaning the data and get rid of useless data & errors Remove any duplicates present in the sourced data Transform the data into the required format To become a proficient data engineer, you need to acquire certain skills. Here’s a list and a bit about each skill that will help you become a better data engineer: Coding skills: Most data engineering jobs nowadays need candidates with strong coding skills. Numerous job postings stipulate a minimum requirement of applicants’ familiarity with a programming language, often one of the popular coding languages such as Scala, Perl, Python, Java, etc. DBMS: Engineers working with data should be well-versed in all things related to database administration. An in-depth understanding of Structured Query Language (SQL) is crucial in this profession since it is the most popular choice. SQL stands for Structured Query Language and retrieves and manipulates information in a database table. If you want to succeed as a data engineer, learning about Bigtable and other database systems is essential. Data Warehousing: Data engineers are responsible for managing and interpreting massive amounts of information. Consequently, it is essential for a data engineer to be conversant with and have expertise with data warehousing platforms like Redshift by AWS. Machine Learning: Machine learning is the study of how machines or computers may “learn” or use information gathered from previous attempts to improve their performance on a given task or collection of activities.Though data engineers do not directly work on creating or designing machine learning models. It is their job to create the architecture on which Data Scientists and Machine Learning Engineers apply their models. Hence, a knowledge of Machine Learning is essential for a Data Engineer. Operating Systems, Virtual Machines, Networking, etc. As the demand for big data is increasing, the need for data engineers is rising accordingly. Now that you know what a data engineer does, we can start discussing our data engineering projects.  Let’s start looking for data engineering projects to build your very own data projects! So, here are a few data engineering projects which beginners can work on: Data Engineering Projects You Should Know About To become a proficient data engineer, you should be aware of your sector’s latest and most popular tools. Working on a data engineer project will help you know the ins and outs of the industry. That’s why we’ll focus on the data engineering projects you should be mindful of: 1. Prefect Prefect is a data pipeline manager through which you can parametrize and build DAGs for tasks. It is new, quick, and easy-to-use, due to which it has become one of the most popular data pipeline tools in the industry. Prefect has an open-source framework where you can build and test workflows. The added facility of private infrastructure enhances its utility further because it eliminates many security risks a cloud-based infrastructure might pose.  Even though Prefect offers a private infrastructure for running the code, you can always monitor and check the work through their cloud. Prefect’s framework is based on Python, and even though it’s entirely new in the market, you’d benefit greatly from learning Prefect. Taking up a data engineering project on Prefect will be convenient for you due to the resources available on the internet, being an open-source framework. Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses 2. Cadence Cadence is a fault-tolerant coding platform that gets rid of many complexities of building distributed applications. It secures the complete application state that allows you to program without worrying about the scalability, availability, and durability of your application. It has a framework as well as a backend service. Its structure supports multiple languages, including Java and Go. Cadence facilitates horizontal scaling along with a replication of past events. Such replication enables easy recovery from any sorts of zone failures. As you would’ve guessed by now, Cadence is undoubtedly a technology you should be familiar with as a data engineer. Using Cadence for a data engineer project will automate a lot of mundane tasks that you would otherwise need to perform to build your own data engineer project from scratch. 3. Amundsen Amundsen is a product of Lyft and is a metadata and data discovery solution. Amundsen offers multiple services to users that make it a worthy addition to any data engineer’s arsenal. The metadata service, for example, takes care of the metadata requests of the front-end. Similarly, it has a framework called data builder to extract metadata from the required sources. Other prominent components of this solution are the search service, the library repository named Common, and the front-end service, which runs the Amundsen web app.  4. Great Expectations Great Expectations is a Python library that lets you validate and define rules for datasets. After determining the rules, validating data sets becomes easy and efficient. Moreover, you can use Great Expectations with Pandas, Spark, and SQL. It has data profilers that can produce automated expectations, along with clean documentation for HTML data. While it’s relatively new, it is certainly gaining popularity among data professionals. Great Expectations automates the verification process for new data you receive from other parties (teams and vendors). It saves a lot of time in data cleaning, which can be a very exhaustive process for any data engineer.  Must Read: Data Mining Project Ideas In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Data Engineering Project Ideas You can Work on This list of data engineering projects for students is suited for beginners, intermediates & experts. These data engineering projects will get you going with all the practicalities you need to succeed in your career. Further, if you’re looking for data engineering projects for final year, this list should get you going. If you are keen on data engineering and want to write your final year thesis on data engineering topics, then you should definitely start looking up data engineering research topics online without any delay. So, without further ado, let’s jump straight into some data engineering projects that will strengthen your base and allow you to climb up the ladder. Here are some data engineering project ideas that should help you take a step forward in the right direction and strengthen your profile as a project data engineer. 1. Build a Data Warehouse One of the best ideas to start experimenting you hands-on data engineering projects for students is building a data warehouse. Data warehousing is among the most popular skills for data engineers. That’s why we recommend building a data warehouse as a part of your data engineering projects. This project will help you understand how you can create a data warehouse and its applications. A data warehouse collects data from multiple sources (that are heterogeneous) and transforms it into a standard, usable format. Data warehousing is a vital component of Business Intelligence (BI) and helps in using data strategically. Other common names for data warehouses are: Analytic Application Decision Support System Management Information System Data warehouses are capable of storing large quantities of data and primarily help business analysts with their tasks. You can build a data warehouse on the AWS cloud and add an ETL pipeline to transfer and transform the data into the warehouse. Once you’ve completed this project, you’d be familiar with nearly all aspects of data warehousing.  2. Perform Data Modeling for a Streaming Platform One of the best ideas to start experimenting you hands-on data engineering projects for students is performing data modeling. In this project, a streaming platform (such as Spotify or Gaana) wants to analyze its user’s listening preferences to enhance their recommendation system. As the data engineer, you have to perform data modeling so they can explain their user data adequately. You’ll have to create an ETL pipeline with Python and PostgreSQL. Data modeling refers to developing comprehensive diagrams that display the relationship between different data points.  Some of the user points you would have to work with would be: The albums and songs the user has liked The playlists present in the user’s library The genres the user listens to the most How long the user listens to a particular song and its timestamp Such information would help you model the data correctly and provide an effective solution to the platform’s problem. After completing this project, you’d have ample experience in using PostgreSQL and ETL pipelines.  3. Build and Organize Data Pipelines If you’re a beginner in data engineering, you should start with this data engineering project which is one of the best data engineering research topics. Our primary task in this project is to manage the workflow of our data pipelines through software. We’re using an open-source solution in this project, Apache Airflow. Managing data pipelines is a crucial task for a data engineer, and this project will help you become proficient in the same. Apache Airflow is a workflow management platform and started in Airbnb in 2018. Such software allows users to manage complex workflows easily and organize them accordingly. Apart from creating workflows and managing them in Apache Airflow, you can also build plugins and operators for the task. They will enable you to automate the pipelines, which would reduce your workload considerably and increase efficiency. Automation is one of the key skills required in the IT industry, from Data Analytics to Web/ Android Development. Automating pipelines in a project will surely give your resume the upper hand when applying as a project data engineer. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? 4. Create a Data Lake  This is an excellent data engineering projects for beginners. Data lakes are becoming more critical in the industry, so you can build one and enhance your portfolio. Data lakes are repositories for storing structured as well as unstructured data at any scale. They allow you to store your data as-is, i.e., and you don’t have to structure your data before adding it to the storage. This is one of the trending data engineering projects. Because you can add your data into the data lake without needing any modification, the process becomes quick and allows real-time addition of data. Many popular and latest implementations such as machine learning and analytics require a data lake to function correctly. With data lakes, you can add multiple file-types in your repository, add them in real-time, and perform crucial functions on the data quickly. That’s why you should build a data lake in your project and learn the most about this technology. You can create a data lake by using Apache Spark on the AWS cloud. To make the project more interesting, you can also perform ETL functions to better transfer data within the data lake. Mentioning data engineering projects can help your resume look much more interesting than others. Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. 5. Perform Data Modeling Through Cassandra This is one of the interesting data engineering projects to create. Apache Cassandra is an open-source NoSQL database management system that enables users to use vast quantities of data. Its main benefit is it allows you to use the data spread across multiple commodity servers, which mitigates the risk of failure. Because your data is spread across various servers, one server’s failure wouldn’t cause your entire operation to shut down. This is just one of the many reasons why Cassandra is a popular tool among prominent data professionals. It also offers high scalability and performance.  In this project, you’d have to perform data modelling by using Cassandra. However, when modelling data through Cassandra, you should keep a few points in mind. First, make sure that your data is spread evenly. It is one of the trending data engineering projects. While Cassandra helps in ensuring an even spread of your data, you’d have to double-check this for surety.  Data Science Advanced Certification, 250+ Hiring Partners, 300+ Hours of Learning, 0% EMI Secondly, use the smallest amount of partitions the software reads while modelling. That’s because a high number of reading partitions would put an added load on your system and hamper overall performance. After finishing this project, you’d be familiar with multiple features and applications of Apache Cassandra.  Apart from the ones mentioned here, you can also choose to take up projects about data engineering examples used in the real world. Here’s a list of some other projects on data engineering examples: Event Data Analysis Aviation Data Analysis Forecasting Shipping and Distribution Demand Smart IoT Infrastructure 6. IoT Data Aggregation and Analysis The IoT Data Aggregation and Analysis project involves constructing a robust and scalable data pipeline to collect, process, and derive valuable insights from several Internet of Things (IoT) devices. The objective is to create a seamless data flow from sensors, smart devices, and other connected endpoints into a centralized repository. This repository serves as the foundation for further analysis and visualization. The project encompasses several key components, starting with a data ingestion system design capable of handling real-time data streams. Efficient data storage, utilizing databases optimized for time-series data, is essential to accommodate the high influx of information. Preprocessing steps are data cleansing, transformation, and enrichment to ensure data quality and consistency. For analysis, various techniques such as anomaly detection, pattern recognition, and predictive modeling can uncover meaningful insights. These insights might include identifying operational inefficiencies, predicting maintenance needs, or understanding usage patterns. Ultimately, the project aims to empower stakeholders with actionable insights through interactive dashboards, reports, and visualizations. By successfully executing this project, one gains a deep understanding of data engineering principles, real-time processing, and the complexities of managing diverse IoT data sources. 7. Real-time Data Streaming Pipeline  This project aims at developing a resilient real-time data streaming pipeline capable of receiving and processing data as it comes. This includes picking the right technology stack, for example Apache Kafka, in order to handle data streams effectively. Scalability in horizontal direction is one of the basic principles of architecture in order to cover the growing data volumes and fault tolerance of the continuous data processing.    Some of the solutions for the real-time analytics and storage, Apache Flink or Apache Spark Streaming, for instance, can be integrated to derive meaningful insights from the streaming data.  8. Data Lake Architecture   Data lake construction framework assumes development of a single storage place that can accommodate large amounts of both structured and unstructured data. This project calls for selecting good storage solutions such as Apache Hadoop Distributed File System (HDFS), cloud-based alternatives like AWS S3 or Google Cloud Storage.    The architecture should allow for smooth data integration coming from different sources and ensure data organization and metadata management. Security measures of data lake implementation are important for the protection of sensitive information contained in it.  9. Automated Data ETL (Extract, Transform, Load) Pipeline  The objective of this data engineer project’s idea is to automate the process of retrieving data from different sources, transforming it into a uniformed format and loading it into a dedicated storage or data warehouse. Such ETL workflows can be orchestrated by tools such as Apache NiFi, Apache Airflow, or Talend. The project entails building efficient data transformation scripts, making sure that data quality checks are incorporated, and enabling these workflows to be scheduled or triggered upon setting predefined conditions or events.  10. Data Quality Monitoring System   Designing a data quality monitoring system implies deploying checks and validations throughout the complete data pipeline to guarantee the quality and reliability of the data. This encompasses checks for completeness, verifications of accuracy, and confirmations of consistency. An alerts system is embedded to alert the stakeholders in real time when anomalies or deviations from set data quality standards are identified. The project helps to sustain a high-quality of data that is vital for reliable analytics and decision-making.  Learn More about Data Engineering These are a few data engineering projects that you could try out! Now go ahead and put to test all the knowledge that you’ve gathered through our data engineering projects guide to build your very own data engineering projects! Becoming a data engineer is no easy feat; there are many topics one has to cover to become an expert. However, if you’re interested in learning more about big data and data engineering, you should head to our blog. There, we share many resources (such as this one) regularly.  If you’re interested to learn python & want to get your hands dirty on various tools and libraries, check out Executive PG Program in Data Science. We hope that you liked this article. If you have any questions or doubts, feel free to let us know through the comments below.
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by Rohit Sharma

21 Sep 2023

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