Big Data Blog Posts

Data Engineering Projects & Topics Data engineering is among the core branches of big data. If you’re studying to become a data engineer and want some projects to showcase your skills (or gain knowledge), you’ve come to the right place. In this article, we’ll discuss data engineering project ideas you can work on and several data engineering projects, and you should be aware of it. 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. 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.

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. 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 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.

Big Data – the name indicates voluminous data, which can be both structured and unstructured. Many companies collect, curate, and store data, but how do they access the data in an instant? How do the analysts and other professionals identify the data they need? How do companies manage to handle the ferocious velocity at which data is churned? They then need to analyze the data, and hence, data architecture and analytics has gained prominence. The methodologies involved in how Big Data is accessed and used for business intelligence can play a crucial role in surviving in a highly competitive environment.  What is Big Data? Big Data is a minimum of 1024 terabytes of data. Companies use computational processes to manage the data, which is defined by volume, velocity, variety, veracity, visualization, and value.  Who is a Big Data Architect? A Big Data Architect is like a traditional architect who visualize, design, and build structures. The only difference is that they build physical structures, while Big Data Architects deal with voluminous data sets that are growing by millions of data bytes. It is their job to integrate the large volume of data into an organization’s current system and create safeguards to protect the priceless data while ensuring it is easily and quickly accessible.  Big Data Architect Salary in India Business decisions are often dependent on the trends the Big Data Analysts and Data Scientists are able to unearth from the large data sets. As companies have started shifting to big data to better understand and implement new business ideas, many new openings have raised for Big Data Architects.  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 The Demand for Big Data Architect Big Data has become a buzzword for nearly every industry. It can be reflected by the number of job opportunities you can have for this role. A simple LinkedIn search can land you on more than 5000 related job roles.  Source On Naukri.com, you can find more than 14000 results for Big Data Architect, which shows the rising demand for experts.  Source There is a requirement for Big Data Architect in almost every business. Telecommunication, Marketing, Healthcare, Retail, Government, Insurance, Banking, Finance – Big Data is fast becoming the lifeline of business decisions. So, the demand is high, but what is the salary that you can expect?  Read: Role of Big Data in COVID-19 Aid – Since The Very Beginning The Average Salary of a Big Data Architect According to Glassdoor, the average salary for a Data Architect in India is above Rs 17, 00,000 per annum.  Source The average salary is impressive on its own. In comparison, the average salary in India for a software developer, which is the job that most Computer Science graduates choose, is around only Rs 5, 00,000.  Source This means that you could be earning three times more than your software developer counterparts if you have the right skills and are willing to put in that extra effort! Factors Affecting Big Data Architect Salary in India The three main factors affecting the Big Data Architect Salary in India are: Company – The bigger the data, the higher is the salary you can expect. It does make sense that the bigger the company, the more voluminous their data will be, and hence, you will get a higher salary.  Experience – Your experience to build data management systems and operate them for optimum benefits will have an impact on the salary you get. Business decisions are based on the data, and hence, many companies will pay higher salaries to the right individuals.  Location – The location of the company you approach for a Big Data role will have a direct impact on your final salary. Do remember that the cost of living in some of these cities is also higher in comparison to other cities.  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 Big Data Salary in India: Based on Company The company that you work for will have a direct impact on your salary. The image below shows the salaries that different companies in India offer, and as you can see that Tata Consultancy Services lead the pack with the average salary averaging at Rs 15,00,000 lakhs per annum. It is followed by Ford Motor Company, Wipro, and Deloitte. The requirement for qualified Architects is across industry types!  Source The role of a data architect can be very challenging, requiring skills that deliver results efficiently. This is why companies are ready to pay fantastic salary packages to candidates with the right experience and skillsets.  Big Data Salary in India: Based on Experience Your earnings will largely depend on the experience you have in the industry. So, to earn a higher salary, you need more experience. According to PayScale, early in your career with 1-4 years of experience, you can earn a total compensation of more than Rs 8, 00,000 per annum. The salary can almost double to Rs 15, 00,000 per annum as soon as your experience with 5-9 years of experience. After the 10-year mark, you can expect to get paid over Rs 20, 00,000 per annum.  The image below captures the differences of a Big Data Architect Salary in India based on experience.  Source Thus, if you have the right skill sets and are willing to keep pace with the technological advancements through your careers, you can achieve the highest possible salaries on offer.  Big Data Salary in India: Based on Location The salary also depends on the location. You can earn a higher salary if you are working in IT hubs like Bangalore, Hyderabad, and Pune. The hubs provide companies the right infrastructure to survive, and hence, you can find some of the biggest names in these locations. As the image below shows, Big Data Architect Salary in Bangalore, the IT city of India, is 17% higher than the national average. On the other hand, in Noida, a suburb of Delhi, the salary is 19% below the national average.  Source You can get almost Rs 22,00,000 per annum working in Bangalore, while the figure for Hyderabad is around Rs 17, 00,000 per annum. Source So, you see, a few key factors affect the salary, but one thing is certain – you cannot perform the job without the right skill sets. Big Data is attracting some of the best talents from the tech field, and if you want to succeed, then honing your skills is essential. Freshers need to understand the job requirements while experienced personnel should keep abreast of the technological advancements in the field. Learn more: Big Data Salaries Guide Big Data Architect Salaries Based on Job Roles The salaries of big data solution architecture also vary based on the specific job roles they undertake. Here are the top job roles that offer high salaries to Big Data Architects in India: 1. Data Architect As a Data Architect, the primary responsibility is to design and manage the overall data architecture of an organization. Big Data Architects in this role are responsible for designing the data infrastructure, ensuring data integration, and implementing data governance practices. The highest salary of an architect in India is around ₹2,269,633. 2. Data Scientist Data Scientists possess strong data science skills. They apply statistical analysis and machine learning algorithms to extract insights and generate predictive models from large datasets. The average salary of a data scientist in India is around ₹911,750. 3. Data Engineer Data Engineers focus on building and maintaining the data pipelines and infrastructure required for data processing and analysis. They work closely with Big Data Architects to implement scalable and efficient data solutions. Big Data Architects who take on the role of Data Engineers often earn high salaries due to their expertise in designing robust data pipelines. The data engineer architect monthly salary is around INR 73,663. 4. Solutions Architect Solutions Architects design and implement end-to-end big data solutions for organizations. They work closely with stakeholders to understand business requirements and translate them into technical solutions. Big Data Architects with strong solution architecture skills often earn high salaries due to their ability to deliver comprehensive and effective big data solutions. The big data solution architecture average salary is ₹2,169,880 yearly. Skills required by a Big Data Architects You may want to earn an excellent salary as a Big Data Architect, but to do that, you need to develop the right skill sets.  Knowledge about data architectures and an ability to handle and analyze large scale data You should be able to benchmark systems, analyze and identify any possible bottlenecks in the architecture, and then propose appropriate and efficient solutions to solve them The ability to process and analyze data to understand the behavior of the customer Have good knowledge about a scripting language like Python, Ruby or R. Language proficiency in C# and .Net are often required Good working ability and understanding of Hadoop to solve various problems The job of a Big Data Architect is a highly technical one, but being a professional, some other skills are often expected by the companies. Excellent verbal and communication skills Ability to explain your work in plain language Be able to work in teams and in a fast-paced agile environment 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 What does a Big Data Architect do? The Big Data field has a plethora of opportunities, and so it is important to know what is expected.   Roles and Responsibilities  The role of a Big Data Architect is important as it aims to address big data problems and requirements.  Assessing the structure and behavior of a certain problem, form feasible solutions and delivering the solutions using technologies like Hadoop Forming the necessary link between the organization’s needs and the Data Scientists and Analysts Responsible for developing as well as maintaining a full life cycle of a Hadoop solution Designing the technical architecture for a possible solution based on the analysis and selected platform Designing the application architecture and are responsible for a smooth development process, including testing and deployment The architect needs to be skilled and specific for this cross-industry, cross-functional and cross-domain job. As various other important tasks may depend upon the output given by a data architect, companies often tend to pay quite handsomely for this role. Expectations and requirements by employers For a task as heavy as this, companies often have a certain level of expectation and requirements. Some of these are:  A bachelor’s degree in Computer Science, Information Technology, or other related streams.  For mid-management and senior management, experience in the field is a must  Good understanding of information systems and their applications to data management processes Ability to perform a detailed analysis of business problems and use the technical environments to formulate a feasible and efficient solution Experience working with SQL and NoSQL databases, including Postgres and Cassandra. Excellent Hadoop skills, along with data mining and modeling tools Check out: Hadoop Developer Salary in India 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? Top industries hiring Big Data Architects in India Various industries in India are actively hiring Big Data Architects and offer competitive salaries. Let’s explore some of the top industries and their salary ranges for Big Data Architects. IT and Technology The IT and technology industry is one of the largest employers of Big Data Architects in India. Companies in this industry heavily rely on data analysis to gain insights and make informed business decisions. Big Data Architects working in the IT and technology sector can earn around 24 LPA per annum. Finance and Banking The finance and banking industry heavily relies on data analysis for risk assessment, fraud detection, and customer segmentation. Big Data Architects working in this industry earn a competitive salary of 27 LPA yearly. The complexity and sensitivity of financial data make skilled Big Data Architects highly valuable in this sector. Healthcare The healthcare industry in India is rapidly adopting Big Data technologies to improve patient care, optimize operations, and conduct medical research. Big Data Architects in the healthcare industry can earn around INR 25.6 LPA per annum. The demand for healthcare data analytics professionals is expected to grow significantly in the coming years. Telecommunications Telecommunications companies generate vast amounts of data from their customers’ usage patterns, network performance, and market trends. Big Data Architects working in the telecommunications industry’s salary is around INR 26 LPA yearly. Their expertise in analyzing and leveraging this data is crucial for driving business growth and improving customer experiences. Job prospects and future growth opportunities for Big Data Architects in India The job prospects for Big Data Architects in India are incredibly promising. The increasing adoption of Big Data analytics across industries has soared the demand for skilled professionals. The Big data analytics industry was worth $8.5 billion in 2017 and is expected to expand at a compound annual growth rate (CAGR) of 29.7% to $40.6 billion by 2023. Why should you become a Big Data Architect? While there are several reasons for becoming a Big Data Architect, we have listed the most common ones here.  Great Opportunities As companies depend heavily on their big data analysis to plan future business plans and projects, they require expert data architects to help them formulate and consolidate those ideas. This has created a lot of opportunities for data architects in the big data field. Future Scope As the technology evolves and becomes more complex while continuously integrating with other technologies, the industry will continue to grow.  Companies will need experts in the big data field for niche job roles and responsibilities.  Higher Salaries As you have already seen, the pay structure for a big data architect is way more than that of many other job roles in the same field. And you have also seen how it will grow as your career grows. Just make sure to keep yourself updated and on track. 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. Personal Growth This job is a challenging one and requires you to multitask, handling many responsibilities at once. But this can also be a chance for great personal growth as the learning curve will be continuous. Also Read: Data Scientist Salary in India upGrad’s Exclusive Software Development Webinar for you – SAAS Business – What is So Different? document.createElement('video'); https://cdn.upgrad.com/blog/mausmi-ambastha.mp4 Conclusion The job of a Big Data Architect is now a mandated requirement for companies dealing with data on a large scale. You will be the person who will develop the architecture to help organizations get the data, when they need it and how they need it, without a glitch. It is an interesting and empowering field, and if you want to earn the highest salary possible, an online course with upGrad is the right option. If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

Do you have an upcoming big data interview? Are you wondering what questions you’ll face regarding MapReduce in the interview? Don’t worry, we have prepared a list of the most common MapReduce interview questions asked by recruiters to help you out. These questions range from the basics to advanced concepts of MapReduce.Additionally, We’ll cover all the frequently asked questions in this blog post about Hadoop interview questions and answers, along with the best answers, to help you crack the interview. What is MapReduce Architecture? A programming methodology and software framework called MapReduce Architecture is used for processing huge amounts of data. Map and Reduce are the two phases of the MapReduce program’s operation. While Reduce jobs reduce and shuffle the data, Map requests organise by separating and mapping the data. Running MapReduce programs written in C, Python, Ruby, and Java is possible using the Hadoop MapReduce Architecture. Cloud computing MapReduce projects are equivalent, enabling a wide range of data analysis tasks to be carried out using diverse cluster computers. Here is the MapReduce example to understand it better: The microblogging website Twitter receives close to 500 million tweets every day, or 3000 tweets per second. With the help of MapReduce, we can view the example on Twitter. Twitter data is the input in the aforementioned MapReduce example, while MapReduce handles the tokenization, filtering, counting, and aggregating of counters. 15 Most Common MapReduce Interview Questions & Answers 1. What is MapReduce? Hadoop MapReduce is a framework used to process large data sets (big data) across a Hadoop cluster. 2. Mention three benefits/advantages of MapReduce. The three significant benefits of MapReduce are: Highly scalable: Stores and distributes enormous data sets across thousands of servers. Cost-effective: Allows data storage and processing at affordable prices. Secure: It allows only approved users to operate on the data and incorporates HDFS and HBase security. Read: MapReduce Architecture  3. What are the main components of MapReduce? The three main components of MapReduce are: Main Driver Class: The Main Driver Class provides the job configuration parameters. Mapper Class: This class is used for mapping purposes. Reducer Class: Reducer class divides the data into splits. 4. What are the configuration parameters required to be specified in MapReduce? The required configuration parameters that need to be specified are: The job’s input and output location in HDFS The input and output format The classes containing the map and reduce functions The .JAR file for driver, mapper, and reducer classes. 5. Define shuffling in MapReduce. Shuffling is the process of transferring data from Mapper to Reducer. It is part of the first phase of the framework. 6. What is meant by HDFS? HDFS stands for Hadoop Distributed File System. It is one of the most critical components in Hadoop architecture and is responsible for data storage. 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 7. What do you mean by a heartbeat in HDFS? Heartbeat is the signal sent by the datanode to the namenode to indicate that it’s alive. It is used to detect failures and ensure that the link between the two nodes is intact. 8. Can you tell us about the distributed cache in MapReduce? A distributed cache is a service offered by the MapReduce framework to cache files such as text, jars, etc., needed by applications. 9. What do you mean by a combiner? Combiner is an optional class that accepts input from the Map class and passes the output key-value pairs to the Reducer class. It is used to increase the efficiency of the MapReduce program. However, the execution of the combiner is not guaranteed. 10. Is the renaming of the output file possible? Yes, the implementation of multiple format output class makes it possible to rename the output file. 11. What is meant by JobTracker? JobTracker is a service that is used for processing MapReduce jobs in a cluster. The JobTracker performs the following functions: Accept jobs submitted by client applications Communicate with NameNode to know the data location Locate TaskTracker nodes that are near the data or are available Submit the work to the chosen nodes If a TaskTracker node notifies failure, JobTracker decides the steps be taken next. It updates the status of the job after completion. If the JobTracker fails, all running jobs are stopped. 12. Can you tell us about MapReduce Partitioner and its role? The phase that controls the partitioning of intermediate map-reduce output keys is known as a partitioner. The process also helps to provide the input data to the reducer. The default partitioner in Hadoop is the ‘Hash’ partitioner. 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 13. Can Reducers communicate with each other? No, Reducers can’t communicate with each other as they work in isolation. 14. What do you mean by InputFormat? What are the types of InputFormat in MapReduce? InputFormat is a feature in MapReduce that defines the input specifications for a job. The eight different types of InputFormat in MapReduce are: FileInputFormat TextInputFormat SequenceFileInputFormat SequenceFileAsTextInputFormat SequenceFileAsBinaryInputFormat DBInputFormat NLineInputFormat KeyValueTextInputFormat Must Read: Hitchhicker’s Guide to MapReduce 15. How does MapReduce work? MapReduce works in two phases — the map phase and the reduce phase. In the map phase, MapReduce counts the words in each document. In the reduce phase, it reduces the data and segregates them. 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 interview questions and Answers These Hadoop MapReduce interview questions and answers may help both freshers and experienced job applicants land their dream job. 1. What is Hadoop Map Reduce? The Hadoop MapReduce framework is used to handle massive data sets in parallel across a Hadoop cluster. Map and reduce is a two-step procedure used in data analysis. 2. How does Hadoop MapReduce operate? During the map phase of the MapReduce algorithm, each document’s words are counted, and during the reduction phase, data is aggregated for each document over the whole collection. The incoming data is split up for analysis during the map phase by map processes executing concurrently throughout the Hadoop architecture. 3. Explain the role of MapReduce in the hadoop mapreduce example.  A Hadoop framework called MapReduce is used to create applications that can handle enormous volumes of data on huge clusters. Another name for it is a programming architecture that enables us to process big datasets across computer clusters. This programme enables the distributed storage of data. 4. What does Hadoop’s “speculative execution” mean?  The master node can redundantly run another instance of the identical job on another node if it looks like one node is processing a task more slowly than the others. The task that completes first will then be approved, while the second is terminated. The term “speculative execution” refers to this technique. 5. What is NameNode in Hadoop? Hadoop keeps all of the HDFS file location information in NameNode. It is the master node that the metadata-based job tracker operates on. Conclusion In conclusion, anyone attempting to handle large amounts of data must have a solid grasp of the MapReduce architecture and how it works with Hadoop. You’ll be better prepared to handle the difficulties of working with distributed data processing systems and prove your knowledge in interviews by studying real-world examples and practising MapReduce interview questions. We hope that you find this blog was informative and helpful for the preparation of your interview. We have tried to cover basic, intermediate, and advanced MapReduce interview questions. Feel free to ask your doubts in the comments section below. We will try to answer them to the best of our capabilities. 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.

What is Spark? Spark is an essential instrument in advanced analytics as it can swiftly handle all sorts of data, independent of quantity or complexity. Spark may also be easily incorporated with Hadoop’s Distributed File System to facilitate data processing. Combining with Yet Another Resource Negotiator (YARN) additionally allows processing data.  Given that Hadoop is designed for sequential processing, Spark is designed for data in real time. Hadoop is regarded as a high-latency computing architecture that lacks interactive options. Spark, on the other hand, can handle data interactively. Why Spark? As you must put into practice if you wish to begin working on an Apache large-scale data project with Spark, we have listed a few use cases below to help you move forward in your learning journey!  Spark project ideas combine programming, machine learning, and big data tools in a complete architecture. It is a relevant tool to master for beginners who are looking to break into the world of fast analytics and computing technologies.  Check out our free courses to get an edge over the competition. Why Spark? Apache Spark is a top choice among programmers when it comes to big data processing. This open-source framework provides a unified interface for programming entire clusters. Its built-in modules provide extensive support for SQL, machine learning, stream processing, and graph computation. Also, it can process data in parallel and recover the loss itself in case of failures.  Spark is neither a programming language nor a database. It is a general-purpose computing engine built on Scala. It is easy to learn Spark if you have a foundational knowledge of Python and other APIs, including Java and R.  The Spark ecosystem has a wide range of applications due to the advanced processing capabilities it possesses. We have listed a few use cases below to help you move forward in your learning journey!  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 Spark Project Ideas & Topics 1. Spark Job Server This project helps in handling Spark job contexts with a RESTful interface, allowing submission of jobs from any language or environment. It is suitable for all aspects of job and context management.  The development repository with unit tests and deploy scripts. The software is also available as a Docker Container that prepackages Spark with the job server.  2. Apache Mesos The AMPLab at UC Berkeley developed this cluster manager to enable fault-tolerant and flexible distributed systems to operate effectively. Mesos abstracts computer resources like memory, storage, and CPU away from the physical and virtual machines. Learn to build applications like Swiggy, Quora, IMDB and more It is an excellent tool to run any distributed application requiring clusters. From bigwigs like Twitter to companies like Airbnb, a variety of businesses use Mesos to administer their big data infrastructures. Here are some of its key advantages: It can handle workloads using dynamic load sharing and isolation It parks itself between the application layer and the OS to enable efficient deployment in large-scale environments It facilitates numerous services to share the server pool It clubs the various physical resources into a unified virtual resource You can duplicate this open-source project to understand its architecture, which comprises a Mesos Master, an Agent, and a Framework, among other components. Read: Web Development Project Ideas 3. Spark-Cassandra Connector Cassandra is a scalable NoSQL data management system. You can connect Spark with Cassandra using a simple tool. The project will teach you the following things: Writing Spark RDDs and DataFrames to Apache Cassandra tables Executing CQL queries in your Spark application Earlier, you had to enable interaction between Spark and Cassandra via extensive configurations. But with this actively-developed software, you can connect the two without the previous requirement. You can find the use case freely available on GitHub.  Read more: Git vs Github: Difference Between Git and Github 4. Predicting flight delays You can use Spark to perform practical statistical analysis (descriptive as well as inferential) over an airline dataset. An extensive dataset analysis project can familiarize you with Spark MLib, its data structures, and machine learning algorithms.  Furthermore, you can take up the task of designing an end-to-end application for forecasting delays in flights. You can learn the following things through this hands-on exercise: Installing Apache Kylin and implementing star schema  Executing multidimensional analysis on a large flight dataset using Spark or MapReduce Building Cubes using RESTful API  Applying Cubes using the Spark engine 5. Data pipeline based on messaging A data pipeline involves a set of actions from the time of data ingestion until the processes of extraction, transformation, or loading take place. By simulating a batch data pipeline, you can learn how to make design decisions along the way, build the file pipeline utility, and learn how to test and troubleshoot the same. You can also gather knowledge about constructing generic tables and events in Spark and interpreting output generated by the architecture.  Read: Python Project Ideas & Topics 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 6. Data consolidation  This is a beginner project on creating a data lake or an enterprise data hub. No considerable integration effort is required to consolidate data under this model. You can merely request group access and apply MapReduce and other algorithms to start your data crunching project. Such data lakes are especially useful in corporate setups where data is stored across different functional areas. Typically, they materialize as files on Hive tables or HDFS, offering the benefit of horizontal scalability.  To assist analysis on the front end, you can set up Excel, Tableau, or a more sophisticated iPython notebook.  Check our other Software Engineering Courses at upGrad. 7. Zeppelin It is an incubation project within the Apache Foundation that brings Jupyter-style notebooks to Spark. Its IPython interpreter offers developers a better way to share and collaborate on designs. Zeppelin supports a range of other programming languages besides Python. The list includes Scala, SparkSQL, Hive, shell, and markdown.  With Zeppelin, you can perform the following tasks with ease: Use a web-based notebook packed with interactive data analytics Directly publish code execution results (as an embedded iframe) to your website or blog Create impressive, data-driven documents, organize them, and team-up with others 8. E-commerce project Spark has gained prominence in data engineering functions of e-commerce environments. It is capable of aiding the design of high-performing data infrastructures. Let us first look at what all you is possible in this space: Streaming of real-time transactions through clustering algorithms, such as k-means Scalable collaborative filtering with Spark MLib Combining results with unstructured data sources (for example, product reviews and comments) Adjusting recommendations with changing trends The dynamicity of does not end here. You can use the interface to address specific challenges in your e-retail business. Try your hand at a unique big data warehouse application that optimizes prices and inventory allocation depending upon geography and sales data. Through this project, you can grasp how to approach real-world problems and impact the bottom line.   Check out: Machine Learning Project Ideas 9. Alluxio Alluxio acts as an in-memory orchestration layer between Spark and storage systems like HDFS, Amazon S3, Ceph, etc. On the whole, it moves data from a central warehouse to the computation framework for processing. The research project was initially named Tachyon when it was developed at the University of California.  Apart from bridging the gap, this open-source project improves analytics performance when working with big data and AI/ML workloads in any cloud. It provides dedicated data-sharing capabilities across cluster jobs written in Apache Spark, MapReduce, and Flink. You can call it a memory-centric virtual distributed storage system. 10. Streaming analytics project on fraud detection Streaming analytics applications are popular in the finance and security industry. It makes sense to analyze transactional data while the process is underway, instead of finding out about frauds at the end of the cycle. Spark can help build such intrusion and anomaly detection tools with HBase as the general data store. You can spot another instance of this kind of tracking in inventory management systems. 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. 11. Complex event processing Through this project, you can explore applications with ultra-low latency where sub-seconds, picoseconds, and nanoseconds are involved. We have mentioned a few examples below. High-end trading applications Systems for a real-time rating of call records Processing IoT events The speedy, lambda architecture of Spark allows millisecond response time for these programs.  Apart from the topics mentioned above, you can also look at many other Spark project ideas. Let’s say you want to make a near real-time vehicle-monitoring application. Here, the sensor data is simulated and received using Spark Streaming and Flume. The Redis data structure can serve as a pub/sub middleware in this Spark project. 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 12. The use case for gaming The video game industry requires reliable programs for immediate processing and pattern discovery. In-game events demand quick responses and efficient capabilities for player retention, auto-adjustment of complexity levels, target advertising, etc. In such scenarios, Apache Spark can attend to the variety, velocity, and volume of the incoming data. Several technology powerhouses and internet companies are known to use Spark for analyzing big data and managing their ML systems. Some of these top-notch names include Microsoft, IBM, Amazon, Yahoo, Netflix, Oracle, and Cisco. With the right skills, you can pursue a lucrative career as a full-stack software developer, data engineer, or even work in consultancy and other technical leadership roles.  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? 13. Big Data Analytics Projects with Apache Spark Big data is a collection of semi-structured, unstructured, and structured data obtained by an organization and used for information extraction in exporting modeling, machine learning initiatives, and various other analytics applications. Big Data Analytics Projects with Spark is a holistic structure that combines big data techniques, machine learning, and programming. It is a useful tool for guiding newcomers as they embark on the development of quick analytics and revolutionary computing technologies. Apache Spark is a key open source that is spread throughout the processing system to handle big data workloads. It contains streamlined query implementations and memory conserving for quick requests in the face of information of various sizes. The Spark is used as the rapid and universal engine in massive-scale information processes. If the information does not fit in memory, the developers in Spark must use external techniques. It is used in the procedure of data sets that are larger compared to the cluster’s shared memory. A Spark may attempt to gather the data simultaneously to memory, and the information will eventually be copied onto the disk. 14. PySpark Projects If you’re new to Apache Spark and prefer Python to be your coding language of choice, you should look into PySpark. PySpark serves as an Apache Spark API which enables users to carry out any of the fascinating Python-based programming operations on Spark’s Resilient Distributed Datasets (RDDs). As a result, PySpark may be used to do data analytics while creating robust machine learning algorithms related to applications of Big Data. While learning PySpark, the user must be familiar with Apache Spark programs. You can download a basic dataset and experiment with Spark operations, Spark architecture, Directed Acyclic Graph (DAG), Interactive Spark Shell, and other features. Furthermore, understand the distinctions between Action and Transformation. Additionally, there are no hard-and-fast requirements for learning PySpark; all that is required is an elementary knowledge of advanced statistics, mathematics, and a language for object-oriented programming. PySpark large data projects are the greatest approach to learn PySpark since authentic learning comes through experience. Conclusion The above list on Spark project ideas is nowhere near exhaustive. So, keep unravelling the beauty of the codebase and discovering new applications! If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

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 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  1. Define Big Data and explain the Vs of Big Data. This is one of the most introductory yet important Big Data interview 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 Variety – Talks about the various formats of data Velocity – Talks about the ever increasing speed at which the data is growing Veracity – Talks about the degree of accuracy of data available 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. Hadoop is an open-source framework for storing, processing, and analyzing complex unstructured data sets for deriving insights and intelligence. 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. 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. DataNode – These are the nodes that act as slave nodes and are responsible for storing the data. 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. NodeManager – Executes tasks on every DataNode. 7 Interesting Big Data Projects You Need To Watch Out 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. 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.’ 5. Define and describe the term FSCK. 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. Read: Big data jobs and its career opportunities 6. What is the purpose of the JPS command in Hadoop? 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 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 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 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. NameNode – Port 50070 Task Tracker – Port 50060 Job Tracker – Port 50030 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. 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. reduce() – A parameter that is called once per key with the concerned reduce task cleanup() – Clears all temporary files and called only at the end of a reducer task. 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. Version Delete Marker – For marking a single version of a single column. Column Delete Marker – For marking all the versions of a single column. 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? 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? 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.” 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.” 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. 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? 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? The command to format the NameNode is “$ hdfs namenode -format” Do you like good data or good models more? Why? 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. 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. 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? 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. 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? 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? 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.

Thanks to improved healthcare services, today, the average human lifespan has increased to a great extent. While this is a commendable milestone for humankind, it also poses lots of new and diverse challenges for health care providers (HCPs). They face increasing amounts of challenges in delivering healthcare services to patients. This is where Big Data comes in the scenario.  Big Data in healthcare pertains to the massive amounts of healthcare data gathered from multiple sources such as pharmaceutical research, electronic health records (EHRs), healthcare wearables, medical imaging, genomic sequencing, and other such processes. The digitization of healthcare information and the increase in demand for value-based care are the primary reasons behind the rapid rise in Big Data in healthcare. As the ever-increasing pile of healthcare data continues to pose new challenges for HCPs, it calls for the adoption of Big Data technologies and tools that can efficiently collect, store, and analyze large datasets to deliver actionable insights. Rise of Big Data in Healthcare The adoption of big data use cases in healthcare has been quite slow compared to other industries (manufacturing, BFSI, logistics, etc.) due to reasons like the sensitivity of private healthcare data, security issues, and budget constraints, among other things. However, a report by the International Data Corporation (IDC) sponsored by Seagate Technology maintains that Big Data is likely to grow faster in healthcare than in sectors like media, manufacturing, or financial services. Furthermore, estimates suggest that healthcare data will grow at a CAGR of 36% all through till 2025. Currently, 2 primary trends have encouraged the adoption of big data use cases in healthcare. The first push came from the transition from the ‘pay-for-service’ model (it offers financial incentives to HCPs and caregivers for delivering healthcare services) to a ‘value-based care’ model (it rewards HCPs and caregivers according to the overall health of their patient population). This transition has been possible because of the ability of Big Data Analytics to measure and track the health of the patients.  The second trend is where HCPs and medical professionals leverage using  Big Data Analytics to deliver evidence-based information that promises to boost the efficiencies of healthcare delivery while simultaneously increasing our understanding of the best healthcare practices. Bottomline – adopting big data use cases in healthcare can potentially transform the healthcare industry for the better. It is not only allowing HCPs to deliver superior treatments, diagnosis, and care experiences, but it is also lowering healthcare costs, thereby making healthcare services accessible to the mass.   Applications of Big Data in Healthcare Health Tracking Along with the Internet of Things (IoT), Big Data Analytics is revolutionizing how healthcare statistics and vitals are tracked. While wearables and fitness devices can already detect heart rate, sleep patterns, distance walked, etc., innovations in this front can now monitor one’s blood pressure, glucose levels, pulse, and much more. These technologies are allowing people to take charge of their health.  Episode Analytics HCPs are always struggling with offering quality healthcare services at marginalized costs. Episode Analytics and Big Data tools are helping solve this dilemma by allowing HCPs to understand their performance, to identify the areas that offer scope for improvement, and to redesign their care delivery system. Together, all of this helps to optimize the processes as well as reduce the costs. Fraud detection and prevention  Big Data Analytics and tools come in very handy to detect and prevent fraud and human errors. These can validate the patient data, analyze his/her medical history, and point out any out of place errors in prescriptions, wrong medicines, wrong dosage, and other minor human mistakes, thereby saving lives. Real-time alerts Big Data tech allows HCPs and medical professionals to analyze data in real-time and perform accurate diagnoses. For instance, Clinical Decision Support (CDS) software can analyze medical data on-spot, thereby offering crucial medical advice to healthcare practitioners as they diagnose patients and write prescriptions. This helps save a lot of time. Telemedicine  Thanks to Big Data technologies, we are now able to make full use of Telemedicine. It allows HCPs and medical practitioners to deliver remote diagnosis and clinical services to patients, saving them both time and money.  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 Big Data Analytics for Disease Diagnosis and Prediction Big data analytics has emerged as a powerful tool in healthcare for disease diagnosis and prediction. With the exponential growth in healthcare data, including electronic health records, medical imaging, genomic information, and patient-generated data, the potential to extract valuable insights has increased significantly. Here are some key aspects of how big data analytics is transforming disease diagnosis and prediction in healthcare: 1. Early Detection and Diagnosis Big data use cases in healthcare enable healthcare providers to analyze large datasets from diverse sources, helping identify patterns, trends, and anomalies that may indicate early signs of diseases. By analyzing patient data, including vital signs, lab results, and lifestyle information, healthcare professionals can identify high-risk individuals and intervene proactively, leading to early diagnosis and timely treatment. 2. Predictive Analytics for Patient Outcomes Through predictive analytics, big data helps healthcare institutions anticipate patient outcomes, treatment responses, and disease progression. By employing machine learning algorithms on vast amounts of patient data, healthcare providers can create predictive models that estimate the likelihood of specific outcomes based on individual patient characteristics, historical data, and treatment options. 3. Precision Medicine Big data analytics is critical in advancing precision medicine, tailoring medical treatments to individual patients based on their genetic makeup, lifestyle, and other relevant factors. Analyzing massive genomic datasets allows researchers and clinicians to identify genetic markers associated with specific diseases and determine personalized treatment strategies that offer individual patients the highest chances of success. 4. Real-time Data Monitoring Big data analytics enables real-time patient data monitoring, offering healthcare professionals a continuous and comprehensive view of a patient’s health status. This real-time data access facilitates prompt detection of any alarming changes, allowing for timely interventions and reducing the risk of complications. 5. Disease Outbreak Prediction and Management In public health, big data use cases in healthcare assists in disease outbreak prediction and management. By analyzing data from various sources, including social media, surveillance systems, and patient records, public health authorities can identify and respond to potential outbreaks more swiftly and effectively, helping to control the spread of infectious diseases. 6. Drug Discovery and Development Big data analytics is accelerating drug discovery and development processes in the pharmaceutical industry. Researchers can identify potential drug targets, predict drug efficacy, and optimize treatment regimens by analyzing vast datasets, including molecular information, clinical trial results, and drug interactions. 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 Enhancing Personalized Medicine through Big Data Insights Big data use cases in healthcare insights have significantly advanced personalized medicine, tailoring medical treatments to individual patients based on their unique characteristics. Here’s how big data insights are enhancing personalized medicine: 1. Patient Profiling and Risk Stratification Big data analytics allows healthcare providers to create detailed patient profiles by analyzing vast patient data, including medical history, genetic information, lifestyle factors, and treatment outcomes. These profiles enable risk stratification, identifying patients at higher risk for specific diseases or adverse treatment reactions. Healthcare professionals can develop personalized prevention plans and treatment approaches by understanding individual patient risk factors. 2. Genomics and Precision Medicine Big data analysis of genomic data is crucial in advancing precision medicine. Researchers can identify genetic variations associated with certain diseases or drug responses by analyzing large-scale genomic datasets. This information helps develop targeted therapies that are more likely effective and reduce the risk of adverse reactions. 3. Treatment Response Prediction Big data analytics leverages machine learning algorithms to analyze patient data and predict individual treatment responses. By considering genetic markers, clinical history, and lifestyle factors, healthcare providers can determine the most suitable treatment options for each patient, increasing the chances of successful outcomes. 4. Real-time Monitoring and Wearable Devices With the proliferation of wearable devices and IoT-enabled healthcare solutions, big data use cases in healthcare insights enable real-time monitoring of patient’s health parameters. Continuous data collection and analysis provide healthcare professionals with up-to-date information about patients’ conditions, facilitating timely adjustments to treatment plans based on their evolving health status. 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 Wrapping up In the future, the healthcare sector will see a lot more of Big Data applications that will revolutionize the healthcare industry one step at a time. Not only will Big Data help streamline the delivery of healthcare services, but it will also allow HCPs to enhance their competitive advantage through smart business solutions. 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.

Big data is the term used for the data, which is either too big, changes with a speed that is hard to keep track of, or the nature of which is just too complex to be handled by traditional data handling techniques. Big data is omnipresent, and every human on earth leaves a trail of data that encapsulates their online presence. Due to the low computational power and the fear of missing out on some crucial insights, data generated was stored until the turn of this century, which is when this industry started gaining its footing in the world. The importance of the sheer volume of big data career opportunities cannot be ignored. Google AI (which is found on every smartphone), advertisements that you see online, and all the various recommending engines (like Amazon and Netflix) are fueled by the big data you generate.  Needless to say, Big data is transforming the world that we see and soon will take the driving seat in propelling the global economy. To suffice Big data’s growing nature, the need for trained personnel will only increase according to the U.S. Bureau of Labor Statistics (BLS). They estimate that career opportunities in big data will see a boom of about 12% by the end of the year 2028, creating more than 500,000 jobs. Finding people with the right skill set is the real challenge due to the lack of people having the required expertise. Thus, increasing the pay of the people substantially who have the right skillset. Pursuing a big data career is a good option, especially if you possess the skills needed to be in the industry.  Are Big Data Careers in Demand? Due to technological advancements and the proliferation of digital platforms, big data has experienced significant growth. Regardless of your location or industry, the term “big data” is frequently encountered in business circles. Enterprises actively seek efficient ways to process and analyze these vast datasets, leading to numerous career opportunities in big data. The demand for professionals skilled in handling big data career opportunities is soaring globally, as organizations heavily rely on it to maintain a competitive edge in the market. As a result, the number of job openings for data experts will continue to rise in the coming years. If you aspire to secure a position in the big data domain, obtaining a BS in Data Science or pursuing a Master’s in Big Data Analytics would be your most advantageous path to be eligible for big data analytics job. Reasons to Choose a Career in Big Data  Increased Job Opportunities for Big Data professionals  As technology advances, it is evident that Big Data is becoming a prominent buzzword and an essential requirement for organizations in the foreseeable future. However, Data is useless without the skill to analyze it. In the present day, there is a tremendous demand for Big Data professionals across organizations globally. Companies are extensively leveraging big data career opportunities to gain a competitive advantage, leading to a high demand for big data job for candidates with expertise and skills in this field.  Salary Growth The high demand for big data job is influencing the salaries offered to qualified individuals. The remuneration for these professionals is directly linked to various factors such as their acquired skills, level of education, experience in the field, proficiency in relevant technologies, and more. Additionally, a thorough understanding of real-world Big Data challenges and proficiency in utilizing various tools and technologies also significantly determine their earning potential. Usage Across numerous firms/industries In the present day, big data job finds applications in nearly every organization. The top five industries that extensively hire Big Data professionals are Professional, Scientific, and Technical Services (27%), Information Technology (19%), Manufacturing (15%), Finance and Insurance (9%), Retail Trade (9%), while other sectors account for 21% of the recruitment. Read: Big Data Interview Questions Career in Big data: What career opportunities in big data are there, and what can you expect? There is no denying the increase in the sheer strengths of the computers, and its presence, the data generated, has also increased. It is also estimated that almost 90% of the big data that we have has been generated in the past few years. To derive insights and use the data to our advantage, a number of big data career opportunities have opened in the big data sector. So, listed below, you will find a few of the plethora of career opportunities in big data: 1. Big Data Engineer Big data engineers are tasked with developing the solutions which the big data architect has designed for the company. They are supposed to be the backbone of the big data pipeline of any organization. They are the ones who maintain, create, develop, and simultaneously test the solutions that they build. A big data engineer’s toolkit must contain tools that are based on Hadoop technology like Hive MongoDB, MapReduce, or Cassandra. Not only this, but a big data engineer should also have a profound knowledge of data warehousing solutions because they have to work in creating the required pipeline (from start to end), which is needed to process data on a large scale in any organization. They are generally also responsible for maintaining the hardware infrastructure, mainly ensuring that the team has enough processing power to tackle huge data volumes each day. If you think Big data engineering is your preferred career in big data, the money you would make would be in the range of 130K to 220K US dollars.  Read: Data Science vs Big Data: Difference Between Data Science & Big Data 2. Data Architects The main job of any data architect is to design and build intricate data frameworks like databases. They usually are a part of the team, which is called for whenever there is a need for databases. A data architect would look at the problem, the data that is available to them with its help they would create a blueprint. This blueprint which they have created would encapsulate all the phases that the database has to go through like, creation, testing, and how that database would be maintained. The blueprint that these architects have created would then be passed onto the big data engineers who would do all the heavy lifting in implementing the architect’s vision. Data architects work with a variety of data, the data that they get can be personal records, the information which is used in marketing or financial records. They, however, also have to work alongside data administrators and data analysts to ensure that the architects are provided with all the data that they need. From this data, they would need to come up with the databases which would be used to store all the information in. They also need to think in a way to make the stored data accessible based on the clearance that any user has, and create a fail-safe mechanism in case the company is attacked for ransomware or any natural calamity. All this information should be neatly presented in the blueprints that these data architects are responsible for. The big data architects make roughly in the range of 120K to 200K US dollars. 3. Data Warehouse Manager The job of a data warehouse manager is to oversee the team, which is responsible for designing, maintaining, implementing, and supporting the systems designed for data warehousing. They also manage the data’s design, how the database architecture is being created, and the various data repositories are needed in any organization. They have to strive to ensure that perfect harmony is being made between the front end and the back end of data processing. They are also supposed to install processes that measure and ensure the quality of the data in question. Typically, a bachelor’s degree is needed to be a data warehouse manager, and you would usually report to the top of management on a daily basis. Since it is a managerial job, a data warehouse manager not only has to manage the data warehousing solutions, but they also have to manage all the subordinates on a daily basis and assess the performance of the staff. They usually have autonomy over their department, and hence all the personnel actions are directly supervised by the data warehouse manager. They might also be the ones who would have to check for potential risks in the process of data storage and transfer, and the steps needed to be done to mitigate the potential risk to the data. A data warehouse manager can earn anywhere in the ballpark of 80-160K US dollars.  4. Database Manager A database manager is responsible for maintaining the database results by enforcing the standards, checks, and balances. They are the ones who prepare the database for expansion. They make sure that the database is ready for the development by studying the plans that there are and going through the requirements to ensure the process is as smooth as possible. They also advise the senior technical managers on the design and programming by coordinating with the technical management’s upper echelon. Upgrading the piece of hardware and the software that the team has also falls under a database manager’s job category. They are supposed to devise policies, procedures, and controls that would ensure the databases’ safety and security. A good database manager would not only manage the databases but also would update his knowledge by continually participating in learning big data career opportunities. They should develop a habit of reading through tech publications and maintain an excellent professional circle. They should also have a keen eye on any new research in the field of data and think of the ways in which they can implement them at their workplace. If you believe a database manager is the career opportunity in big data for you, then you can earn in the neighborhood of 111-190k US dollars. Learn about: 9 Exciting DBMS Project Ideas & Topics For Beginners 5. Business Intelligence analyst The big data analytics job is responsible for evaluating the company’s data they are currently employed. They are supposed to take this data and then find or collect the industry data and the competition’s data. They should take all this data and derive some meaningful insights. These insights should be aimed at improving the position in the market of the company for which they are currently working. They would also look deep into the base core of the company. They would look and evaluate the company’s system, the way they get things done in the company, and the functions that they perform. This analysis should be aimed to weed out the areas where the company is ineffectively using its resources to ensure substantial profit margins. Any good business Intelligence analyst would also look into the ways in which the company can make new data collection and data analysis policies. They should also make sure that the data is not tinkered and its integrity is preserved. They might also be tasked with some human resource work like hiring other professionals such as data architects or some other data specialists. If you want to be a business intelligence analyst, you can expect to earn in the neighborhood of 89-185k US dollars. 6. Data scientist Data scientists are supposed to be the ones who design and construct any new processes for any data related query like, they are responsible for modelling a process, and thought to improve the data mining and data production processes. They are also tasked to conduct studies on the data and experiment with the product through which they are supposed to develop algorithms. These models are used for prediction, custom-made analysis, etc. Additionally, a data scientist’s job is to find and extract meaning from the data. They spend most of their time collecting, cleaning the data using various techniques, doing exploratory analysis, and then munging the data. The data which went through this process is then used to develop mathematical models that are then used for various machine and deep learning techniques to create a production-ready model which they are tasked to deploy and keep track of its performance. A good data scientist should be skilled in different styles, such as clustering, and have a statistical background to be adept in statistical learning. A good data scientist would spend a great deal of time in exploratory analysis using various graphs and charts. These analyses are usually shared with the team to devise action plans to tackle creating and deploying these machines and deep learning models. A data scientist can earn about 105-180k US dollars. 7. Data Modeler These professionals transform large volumes of data into insightful insights, which include micro and macro patterns and are then compiled into business reports. Data modellers need a fusion of information science understanding, statistical analytic prowess, and strong coding abilities. They frequently focus on specific business fields, which enables them to uncover essential data patterns for their employers more successfully. 8. Database Developer The primary role of database developers involves analyzing existing database processes to modernize, streamline, or remove inefficient coding. Additionally, they monitor database performance, create new databases, and address any troubleshooting issues. These professionals collaborate closely with other development team members and are typically expected to possess prior experience in database development, data analysis, and unit testing. How To Start a Career in Big Data Big data is a fast-growing industry with intriguing career prospects for individuals across various global sectors. This is an excellent opportunity to enter the employment market because of the rising need for qualified big data specialists. If you feel that big data career opportunities are in line with your goals, there are several actions you can take to be ready and position yourself for desirable positions in the sector. You must evaluate the knowledge and abilities necessary to impress potential employers. Since big data occupations are so technical, extensive training and practical learning experience are frequently required. One of the best methods to build the necessary competence and demonstrate your knowledge to potential employers is to pursue a graduate degree in your chosen field of study. Programmes like upGrad’s Big Data courses, for instance, are especially created to give students strong analytical and technical abilities while also providing big data jobs for freshers and possibilities for networking with peers and industry experts. Also Read: Big data Project Ideas Conclusion The field of big data is expanding at a blinding rate. New opportunities are being created for professionals in all industries. The beauty of big data is that it is not industry dependent. Yes, it is true that career opportunities in big data are usually in the tech sector. Still, now we see an emerging trend of the job opening in every industry, making a big data industry career independent.  There is a wide variety of options to choose from if you have decided that big data is the sector for you. Unfortunately, there is a huge skill gap that is slowly becoming the industry standard in hiring professionals to handle their big data needs. So, you should start by improving your data handling skills and gain some industry exposure to get one of these highly sought-after big data jobs for freshers. 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.

Have you ever wondered about the concept behind spark dataframes? The spark dataframes are the extension version of the Resilient Distributed Dataset, with a high level of abstractions. Apache Spark dataframes are similar to structured traditional databases with the advancement in optimization techniques. In this blog, we will discuss apache spark dataframes. Source What is Apache Spark? Apache spark is a general open-source cluster computing framework. It is a leading platform for stream processing, batch processing, and large scale SQL. Spark is known as lightning-fast cluster computing in an Apache project. It is programmed in the Scala language. Spark lets you operate programs faster than Hadoop. Also, it is a quick data processing platform. Currently, Spark supports APIs in Python, Java, and Scala, and its core is suited for a set of high level and powerful libraries such as GraphX, SparkSQL, MLlib, and Spark Streaming. SparkSQL: SparkSQL provides querying data through hive query or via SQL. It also offers several data sources to work with SQL with code. GraphX: GraphX library supports the manipulation of graphs. It offers a uniform tool for graph computation, analysis, and ETL. It also supports standard graph libraries such as Pagerank. MLlib: It is a library of machine learning that supports several algorithms for regression, filtering, cluster classification, clustering, etc.    Spark Streaming: Spark streaming provides real-time streaming data processing. It divides the input of data streams into batches.  Reasons To Learn Apache Spark Apache Spark serves as an open-source foundation project that empowers us to conduct in-memory analytics on vast datasets, effectively overcoming some of the limitations of MapReduce. The demand for faster processing in the entire data pipeline is addressed by Spark. Consequently, it has become the fundamental data platform for various big data-related offerings. The rising popularity of in-memory database computation stems from its ability to provide rapid results due to Spark’s new framework leveraging in-memory capabilities. The remarkable speed of Apache Spark dataframes, which is about 100 times faster, has made it increasingly prevalent in the big data domain, particularly for swift data processing. As an open-source framework, it offers an economical solution for processing large data with both speed and simplicity. Spark is particularly suitable for analyzing big data applications and can be seamlessly integrated into a Hadoop environment, operated standalone, or utilized in cloud environments. Being part of the open-source community, Spark fosters a cost-effective approach, enabling developers to work more efficiently. The primary objective of Spark is to provide developers with an application framework centered around a central data structure. This allows Spark to process massive amounts of data in a remarkably short time, ensuring exceptional performance and making it significantly faster. There are several compelling reasons why learning Spark is highly beneficial, as listed below: Makes easier access to Big Data There are many people that deal with large amounts of data, which may frequently approach many terabytes, making it difficult to access and manage effectively. Apache Spark presents itself as a remedy in this situation, making it simple to access enormous volumes of data. Although Hadoop MapReduce fulfilled a similar function, Apache Spark dataframes was able to get around some of its constraints. Machine learning tasks are dramatically accelerated by Spark’s capacity to keep data in memory, leading to quicker processing and a simpler structure. In addition, Spark is more efficient than Hadoop since it supports real-time processing. High demand of Spark Developers in market Spark is becoming more and more popular as the most advantageous replacement for MapReduce. Similar to Hadoop, Spark also necessitates familiarity with OOPs principles, but it provides a simpler and more effective programming and execution environment. As a result, there is a huge increase in career prospects for those with Spark knowledge. Learning Apache Spark is essential for people who want to pursue a profession in big data technologies. A thorough grasp of data frame in spark provides up a variety of professional opportunities. While there are many ways to learn Spark, formal instruction is the most efficient option. This promotes a more practical and immersive learning experience by giving students hands-on experience and exposure to real-world initiatives. Diverse Nature Java, Scala, Python, and R are just a few of the spark dataframe examples on which Spark may run programmes. For all users, this functionality improves the simplicity and accessibility of using Spark. Learn Spark to make Big Money Spark developers are in great demand in the present environment. For the purpose of luring in and hiring Apache Spark dataframes expertise, businesses are prepared to be flexible with their hiring practises. To attract great personnel, they give flexible work hours and appealing incentives. Additionally, understanding Apache data frame in spark and pursuing a career in big data technologies may be quite profitable, offering a fantastic chance to make a good living. This emphasises even more how important Apache Spark is to the sector. Read: Apache Spark Tutorial for Beginners What is Resilient Distributed Dataset? Spark initiates the concept of Resilient Distributed Dataset, also known as RDD. It is a distributed and immutable collection of objects that can be run in parallel. There are two operations supported by RDD, transformations operations, and action operations. The transformations operations are performed on RDD, such as union, map, join filter, etc. The actions operations return a value on RDD, such as count, reduce, first, and many more.  Learn: 6 Game Changing Features of Apache Spark Why do we need dataframes? Apache Spark 1.3 version came with spark dataframes. There were two main limitations of the resilient distributed dataset. First is RDD cannot manage structured data, and second is RDD does not support any in-built optimization engine. The concept of Spark dataframes resolved the limitations of RDD. Although, a Resilient Distributed Dataset cannot improve the system efficiently. So, to overcome the limitations of the Spark Resilient Distributed Dataset, Spark dataframes were introduced. Dataframes are organized into columns and rows. Each data frame column has an associated name and type. Difference between Spark Resilient Distributed Dataset and Spark DataFrames? The below table shows the difference between Spark RDD and Spark DataFrames. S.No. Comparison factors Spark Resilient Distributed Dataset Spark DataFrames 1. Definition Low level of API High level of abstraction 2. Representation of data It is distributed across various cluster nodes It is a collection of named columns and rows. 3. Optimization Engine RDD does not support any in-built optimization engine Utilization of optimization engine to create logical queries 4. Advantage API Distributed data 5. Performance limitation Garbage collection and Java serialization Support huge performance advancement as compared to RDD 6. Interoperability and Immutability Tracing of data lineage It is not possible to get the object domain. What are the features of Spark DataFrames? Provides management of data structure. It supports a systematic approach to view data. When the data is being stored in data frame in spark, it has some meaning to it.  Spark dataframes provide scalability, flexibility, and various APIs such as java, python, R, and Scala programming. Utilization of optimization engines known as catalyst optimizers to process data in an efficient manner.  Apache Spark dataframes can also process different sizes of data. Dataframes support different sets of data formats such as CSV, Cassandra, Avro, and ElasticSearch.  Supports custom memory management and decreases the overload of garbage collection.  Check out: Apache Spark Developer Salary in India The Verdict Apache is very effective and fast. Apache Spark helps to compute an in-depth high volume of processing tasks in real-time. Sparkbyexamples pyspark are useful for developing query plans. Dataframe API improves and enhances the performance of Spark.  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.