Mohit Soni

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Introduction The demand for potential candidates is increasing rapidly in the big data technologies field. There are plenty of opportunities in this field if you aspire to be part of this domain. The most fruitful domains under big data technologies are data analytics, data science, big data engineering, and so on. For getting success in admission in big data, it is crucial to understand what kind of questions are asked in the interviews and how to answer them. This article will help you to find a direction for the preparation of big data testing interview questions and will increase your chances of selection. Attending a big data interview and wondering what are all the questions and discussions you will go through? Before attending a big data interview, it’s better to have an idea of the type of big data interview questions so that you can mentally prepare answers for them. To help you out, I have created the top big data interview questions and answers guide to understand the depth and real-intend of big data interview questions. Check out our free courses to get an edge over the competition. You won’t belive how this Program Changed the Career of Students Check out the scope of a career in big data. We’re in the era of Big Data and analytics. With data powering everything around us, there has been a sudden surge in demand for skilled data professionals. Organizations are always on the lookout for upskilled individuals who can help them make sense of their heaps of data. The 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.

Big Data, as a concept, has been evoked in almost every conversation about digital innovations, the Internet of Things (IoT), and data science research. However, there’s still some confusion about what exactly this term means. In this Big Data tutorial, we aim to clarify everything you need to know before getting started with Big Data. Simply put, big data is the gathering, analysis, and processing of large amounts of varied data emerging from multiple sources. These large datasets can provide insights into human behaviour, and inform business practices, strategies, product design, artificial intelligence, and more. In this Big Data tutorial, we’ll walk you through the key concepts and terminologies around the buzzword. Watch youtube video We hope that by the end of this tutorial, you’ll have enough idea to take your first steps in the journey of Big Data. But, before we proceed to that in our Big Data tutorial, let’s see the difference between small data and Big Data. Small data vs. Big Data It’s easy to understand the scope of big data through comparison to small data. Small data is information that can be managed by a single machine, or by using traditional methods of analysis. The source and impact of this data are on a smaller scale. For example, production logs can be used to develop weekly performance reports on the productivity of a manufacturing line; or survey results can be used in a marketing report about brand perception. To understand the clear distinction between the two types of data, all we have to do is look at some statistics- by 2020, every person on earth will generate 1.7MB of data per second, sourced from over 50 billion devices connected to the internet. Such a large volume of data, from almost as many sources, can be used to inform business decisions for entire industries, restructuring e-commerce sites, and even revolutionizing health-care delivery. Big Data: Must Know Tools and Technologies Now that you have a rough idea of what Big Data is, let’s take this Big Data tutorial a step further and talk about the core concepts. Big Data Tutorial For Beginners: Types To Know About!  There are three types of big data that we will discuss in this section of our big data tutorial for beginners:  Structured Big Data Structured data is defined as information that can be processed and stored in a set way. RDBMS, or Relational Database Management System, is an example of structured big data. Since structured data has a predetermined schema, processing it is simple. Such data is frequently managed using SQL, which stands for Structured Query Language.  Semi-Structured Big Data Semi-structured data is a data type that falls short of the formal structure of a data model. Nevertheless, several organisational features simplify the analysis, such as tags and other markers to divide semantic parts. Semi-structured data is an example of which are XML or JSON files.  Unstructured Big Data Unstructured big data is a type of data that:  Cannot be stored in an RDBMS Lacks a known or recognizable form Cannot be assessed without being transformed into a structured form. Unstructured data includes multimedia and text files like photographs, audio, and videos. According to experts, unstructured data makes up 80% of the data in a company and is growing more quickly than other types.  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 Characteristics How do you process heterogeneous data on such a large scale, where traditional methods of analytics definitely fail? This has been one of the most significant challenges for big data scientists. To simplify the answer, Doug Laney, Gartner’s key analyst, presented the three fundamental concepts of to define “big data”. Volume This is the primary distinguisher when it comes to Big Data systems. Each of us has a digital footprint, and the amount of data-sets that can be gathered from each of our devices is mind-boggling. Take Facebook for example- as of 2016, there were 2.6 trillion posts on the social networking platform. Twitter logs in at 500 million tweets per day. Add this to all the other digital devices one is connected to, and it is easy to understand how every human on the planet generates an average of 0.77 GB data, per day. Velocity 90% of data currently available was generated in the last two years alone. 2.5 quintillion bytes of data gets generated every single day, and this data is expected to be processed in real-time (or near real-time), to generate insights that will not be rendered redundant in a constantly changing world. This is why big data analysts have stepped away from a traditional batch-oriented approach, and have adopted real-time analysis to ensure they’re generating information that is relevant to the current situation. 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 Variety What makes big data systems so relevant to businesses and communities is the fact that these are unique datasets, as they emerge from varied sources, and are processed using diverse methods. Data can be sourced from social media feeds, physical devices such as Fitbit, home security systems, automobile GPS systems, and more. The data itself is hugely diverse- it could be rich media (photos, videos, audios), or structured logs and unstructured data. The USP of big data is that it consolidates all this information, regardless of its origin, to provide a comprehensive data set of every user. The Three Vs have been used to distinguish big data since 2001, but the latest narratives are in favour of adding ‘veracity, visualization, variability, and value’ to this list, which widens the scope of big data analysis even further. That was about the characteristics of Big Data, next on this Big Data tutorial, let’s talk about how to make this data workable and derive insights from it. Big Data Applications in Pop-Culture How to make sense of big data? The USP of Big Data is the variety of insights that can be drawn. This usually cannot be done through traditional methods, as a lot of the insights, trends, and patterns are often not-obvious. Moreover, small data analysis technologies do not lend themselves to the large volume and variety of content generated through big data methods. To overcome these barriers, various new technologies have been developed- the most popular being the Apache Hadoop. These technologies utilize clustered computing to ingest information into a data system, and compute and analyze the data, and visualize the data streams. Big Data has found a firm place in any imaginable domain and it’ll be wrong to not talk about the wonders this Big Data is doing. Big Data: What is it and Why does it Matter? Watch youtube video Let’s wrap up this Big Data tutorial by talking about the Applications of Big Data: 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 Applications of Big Data Personal development: On a more individual level, big data is being used to optimize individual health. Armbands and smartwatches use data about sleep cycle, calorie consumption, activity levels, and more to develop insights on improving the user’s health- which feeds back to the individual user in a personalized manner. Advertising: Marketing companies are utilizing a variety of data points, including GPS, traffic patterns, eye-movement tracking, etc. to determine what advertisements people are more interested in, thereby determining a more accurate marketing strategy. This is a break from the traditional marketing strategy, where the pricing was ‘per impression’ of the ad. Supply chain optimization: Big data is playing a big role in delivery route optimization (a huge concern for companies like Amazon and eBay), where live traffic data, driver behaviour, etc. are tracked using radio frequency identifiers, and GPS systems, to identify the right route to take, depending on the time of day and year. Weather forecasting: Applications on mobile phones are being used to crowdsource information about weather patterns, in real time. By using a combination of ambient thermometers, barometers, and hygrometers, these apps can generate accurate real-time data for predictive models, which can vastly improve the accuracy of weather forecasting systems. Building smart city infrastructure: Cities are piloting big data analysis systems to develop smart city infrastructure. Drought-ridden California used big data analytics to track water usage by consumers, helping the cut-down water usage by 80%. Los Angeles has reduced its traffic congestion by 16% by monitoring traffic signals around the city. Big Data Engineers: Myths vs. Realities With each passing year, Big Data is only getting bigger and is strengthening its grips on every domain. We hope that this Big Data tutorial was able to help you understand the hype behind the word “Big Data”. If you’re interested in diving deeper, there are numerous Big Data tutorials, courses, and certifications that’ll get you going well. Don’t wait any longer, let this Big Data tutorial be the spark you need to tame the beast that is big data. If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career.

Although Data Science has been around us ever since the 1960s, it has only gained traction in the last few decades. This is one of the main reasons why budding data scientists find it quite challenging to find the right mentors. However, this scenario is drastically changing now. With the right approach and by looking at the right corners, you can find data scientist mentors who can help you bridge the gap between theoretical and practical applications of data science.  Mentoring allows students to acknowledge their weak points and work towards strengthening them. Good data scientist mentors offer constructive criticism that can help students grow and upskill. There are many ways to find the perfect mentor who are well aware of the dos and don’ts of data science. While LinkedIn is a great platform to search for professional data scientist mentors, there are online platforms that are specifically designed to connect aspiring data science professionals with reputed data science programmers, developers, engineers, consultants, and tutors. these platforms allow you to search for mentors that meet your specific requirements while also offering a chat feature to connect with the person. Data Science Summarized In One Picture Another excellent way of connecting with peers, mentors, and even potential data scientist employers is tech conferences. Summits and conferences bring together the best minds in data science under one roof and what better a chance to get in touch with them than to attend these events? There are tons of data science conferences held across the globe where the most talented and top data scientists in the industry come to share their knowledge on big data engineering, data science, artificial intelligence, machine learning, predictive analytics and so much more. Here are some of the top tech conferences from around the world: Big Data Analytics Tokyo   DataWorks Summit   KDD   Strata Data Conference   Cypher   The Data Science Conference   Predictive Analytics World   TDWI Big Data And Analytics Events Apart from these, you can keep track of local hackathon events and meetups in your town/city. When it comes to mastering a dynamic field like data science, nothing beats hands-on training. Data science boot camps (both online and offline) are excellent environments to nurture your data science skills with the help of professional mentors. At these programs, you not only get the guidance of seasoned data scientists, but you also grow through peer-to-peer learning. Then again, if you are faced with a time crunch, you can always opt for online data science courses that walk you through diverse data science concepts in a stepwise approach. The benefit of online data science program is that it allows you to learn and grow at your own time and pace and at a fraction of cost as compared to a full-time course. Our learners also read: Free Python Course with Certification 5 Reasons Why Marketers should Invest in Developing Data Skills upGrad’s Exclusive Data Science Webinar for you – How upGrad helps for your Data Science Career? document.createElement('video'); https://cdn.upgrad.com/blog/alumni-talk-on-ds.mp4 Explore our Popular Data Science Online Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Online Certifications Lately, the concept of workplace mentoring has become quite popular in the industry. Since data science is continually evolving, companies are now arranging for formal training programs to train their employees in mastering data science technologies and help them stay updated with the latest trends in the field. Similar to workplace mentoring, some organizations have also started to invest in student mentoring through internships. Thus, while students get a taste of the job experience, they also get the opportunity to learn new things related to the realms of big data and data science. Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification So, as you can see, there are many different ways to find and connect with data science mentors. There has never been a better time to begin a career in data science than now, and that’s why you should not waste another minute – get started on the mentor hunt right away and dive into the field of data science! Converting Business Problems to Data Science Problems Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Learn data science courses 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 buzzword today. When harnessed wisely Big Data holds the potential to transform organisations for the better drastically. And the wave of change has already started – Big Data is rapidly changing the IT and business sector, the healthcare industry, as well as academia too. However, the key to leveraging the full potential of Big Data is Open Source Software (OSS). Ever since Apache Hadoop, the first resourceful Big Data project came to the fore, it has laid the foundation for other innovative Big Data projects. According to Black Duck Software and North Bridge’s survey, nearly 90% of the respondents maintain that they rely on open source Big Data projects to facilitate “improved efficiency, innovation, and interoperability.” But most importantly, it is because these offer them “freedom from vendor lock-in; competitive features and technical capabilities; ability to customise; and overall quality.”   Big Data Tutorial for Beginners: All You Need to Know Now, let us check out some of the best open source Big Data projects that are allowing organisations not only to improve their overall functioning but also enhancing their customer responsiveness aspect. Apache Beam This open source Big Data project derived its name from the two Big Data processes – Batch and Stream. Thus, Apache Beam allows you to integrate both batch and streaming of data simultaneously within a single unified platform. When working with Beam, you need to create one data pipeline and choose to run it on your preferred processing framework. The data pipeline is both flexible and portable, thereby eliminating the need to design separate data pipelines everytime you wish to choose a different processing framework. Be it batch or streaming of data, a single data pipeline can be reused time and again. Apache Airflow An open source Big Data project by Airbnb, Airflow has been specially designed to automate, organise, and optimate projects and processes through smart scheduling of Beam pipelines. It allows you to schedule and monitor data pipelines as directed acyclic graphs (DAGs). Airflow schedules the tasks in an array and executes them according to their dependency. The best feature of Airflow is probably the rich command lines utilities that make complex tasks on DAGs so much more convenient. Since the configuration of Airflow runs on Python codes, it offers a very dynamic user experience. 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 Apache Spark Spark is one of the most popular choices of organisations around the world for cluster computing. This Big Data project is equipped with a state-of-the-art DAG scheduler, an execution engine, and a query optimiser, Spark allows super-fast data processing. You can run Spark on Hadoop, Apache Mesos, Kubernetes, or in the cloud to gather data from diverse sources. It has been further optimised to facilitate interactive streaming analytics where you can analyse massive historical data sets complemented with live data to make decisions in real-time. Building parallel apps are now easier than ever with Spark’s 80 high-level operators that allow you to code interactively in Java, Scala, Python, R, and SQL. Apart from this, it also includes an impressive stack of libraries such as DataFrames, MLlib, GraphX, and Spark Streaming. Big Data Applications in Pop-Culture Apache Zeppelin Another inventive Big Data project, Apache Zeppelin was created at the  NFLabs in South Korea. Zeppelin was primarily developed to provide the front-end web infrastructure for Spark. Rooting on a notebook-based approach, Zeppelin allows users to seamlessly interact with Spark apps for data ingestion, data exploration, and data visualisation. So, you don’t need to build separate modules or plugins for Spark apps when using Zeppelin. Apache Zeppelin Interpreter is probably the most impressive feature of this Big Data project. It allows you to plugin any data-processing-backend to Zeppelin. The Zeppelin interpreter supports Spark, Python, JDBC, Markdown, and Shell. Apache Cassandra If you’re looking for a scalable and high-performance database, Cassandra is the ideal choice for you. What makes it one of the best OSS, are its linear scalability and fault tolerance features that allow you to replicate data across multiple nodes while simultaneously replacing faulty nodes, without shutting anything down! In Cassandra, all the nodes in a cluster are identical and fault tolerant. So, you never have to worry about losing data, even if an entire data centre fails. It is further optimised with add-ons such as  Hinted Handoff and Read Repair that enhances the reading and writing throughput as and when new machines are added to the existing structure. Big Data: Must Know Tools and Technologies TensorFlow TensorFlow was created by researchers and engineers of Google Brain to support ML and deep learning. It has been designed as an OSS library to power high-performance and flexible numerical computation across an array of platforms like CPU, GPU, and TPU, to name a few. TensorFlow’s versatility and flexibility also allow you to experiment with many new ML algorithms, thereby opening the door for new possibilities in machine learning. Magnates of the industry such as Google, Intel, eBay, DeepMind, Uber, and Airbnb are successfully using TensorFlow to innovate and improve the customer experience constantly. 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 Kubernetes It is an operations support system developed for scaling, deployment, and management of container applications. It clubs the containers within an application into small units to facilitate smooth exploration and management. Kubernetes allows you to leverage hybrid or public cloud infrastructures to source data and move workloads seamlessly. It automatically arranges the containers according to their dependencies, carefully mixing the pivotal and best-effort workloads in an order that boosts the utilisation of your data resources. Apart from this, Kubernetes is self-healing – it detects and kills nodes that are unresponsive and replaces and reschedules containers when a node fails. Big Data Engineers: Myths vs. Realities These Big Data projects hold enormous potential to help companies ‘reinvent the wheel’ and foster innovation. As we continue to make more progress in Big Data, hopefully, more such resourceful Big Data projects will pop up in the future, opening up new avenues of exploration. However, just using these Big Data projects isn’t enough. 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 Watch youtube video. You must strive to become an active member of the OSS community by contributing your own technological finds and progresses to the platform so that others too can benefit from you. As put by  Jean-Baptiste Onofré: “It’s a win-win. You contribute upstream to the project so that others benefit from your work, but your company also benefits from their work. It means more feedback, more new features, more potentially fixed issues.” 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. 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We also saw how any domain or industry (you just name it!) could improve its operations by putting Big Data to good use. Organisations are realizing this fact and are trying to onboard the right set of people, armor them with the correct set of tools and technologies, and make sense of their Big Data. As more and more organisations wake up to this fact, the Data Science market is growing all the more rapidly alongside. Everyone wants a piece of this pie – which has resulted in a massive growth in big data tools and technologies. Watch youtube video. In this article, we’ll talk about the right tools and technologies you should have in your toolkit as you jump on the big data bandwagon. Familiarity with these tools will also help you any upcoming interviews you might face. Hadoop Ecosystem You can’t possibly talk about Big Data without mentioning the elephant in the room (pun intended!) – Hadoop. An acronym for ‘High-availability distributed object-oriented platform”, Hadoop is essentially a framework used for maintaining, self-healing, error handling, and securing large datasets. However, over the years, Hadoop has encompassed an entire ecosystem of related tools. Not only that, most commercial Big Data solutions are based on Hadoop. A typical Hadoop platform stack consists of HDFS, Hive, HBase, and Pig. HDFS It stands for Hadoop Distributed Filesystem. It can be thought of as the file storage system for Hadoop. HDFS deals with distribution and storage of large datasets. MapReduce MapReduce allows massive datasets to be processed rapidly in parallel. It follows a simple idea – to deal with a lot of data in a very little time, simply employ more workers for the job. A typical MapReduce job is processed in two phases: Map and Reduce. The “Map” phase sends a query for processing to various nodes in a Hadoop cluster, and the “Reduce” phase collects all the results to output into a single value. MapReduce takes care of scheduling jobs, monitoring jobs, and re-executing the failed task. Hive Hive is a data warehousing tool which converts query language into MapReduce commands. It was initiated by Facebook. The best part about using Hive is that developers can use their existing SQL knowledge since Hive uses HQL (Hive Query Language) which has a syntax similar to the classic SQL. HBase HBase is a column-oriented DBMS which deals with unstructured data in real time and runs on top of Hadoop. SQL cannot be used to query on HBase as it doesn’t deal with structured data. For that, Java is the preferred language. HBase is extremely efficiently in reading and writing large datasets in real-time. Pig Pig is a high-level procedural programming language that was initiated by Yahoo! And became open source in 2007. As strange as it may sound, it’s called Pig because it can handle any type of data you throw at it!   Spark Apache Spark deserves a special mention on this list as it is the fastest engine for Big Data processing. It’s put to use by major players including Amazon, Yahoo!, eBay, and Flipkart. Take a look at all the organisations that are powered by Spark, and you will be blown away! Spark has in many ways outdated Hadoop as it lets you run programs up to a hundred times faster in-memory, and ten times faster on disk. It complements the intentions with which Hadoop was introduced. When dealing with large datasets, one of the major concerns is processing speed, so, there was a need to diminish the waiting time between the execution of each query. And Spark does exactly that – thanks to its built-in modules for streaming, graph processing, machine learning, and SQL support. It also supports the most common programming languages – Java, Python, and Scala. The main motive behind introducing Spark was to speed up the computational processes of Hadoop. However, it should not be seen as an extension of the latter. In fact, Spark uses Hadoop for two main purposes only — storage and processing. Other than that, it’s a pretty standalone tool.   NoSQL Traditional databases (RDBMS) store information in a structured way by defining rows and columns. It is possible there because the data being stored isn’t unstructured or semi-structured. But when we talk about dealing with Big Data, we’re talking about largely unstructured datasets. In such datasets, querying using SQL won’t work, because the S (structure) doesn’t exist here. So, to deal with that, we have NoSQL databases. NoSQL databases are built to specialise in storing unstructured data and provide quick data retrievals. However, they don’t provide the same level of consistency as traditional databases – you can’t blame them for that, blame the data! The most popular NoSQL databases include MongoDB, Cassandra, Redis, and Couchbase. Even Oracle and IBM – the leading RDBMS vendors – now offer NoSQL databases, after seeing the rapid growth in its usage. Data Lakes Data lakes have seen a continuous rise in their usage over the past couple of years. However, a lot of people still think Data Lakes are just Data Warehouse revisited – but that’s not true. The only similarity between the two is that they’re both data storage repositories. Frankly, that’s it. A Data Lake is can be defined as a storage repository which holds a huge amount of raw data from a variety of sources, in a variety of formats, until it is needed. You must be aware that data warehouses store the data in a hierarchical folder structure, but that’s not the case with Data Lakes. Data Lakes use a flat architecture to save the datasets. Many enterprises are switching to Data Lakes to simplify the processing of accessing their Big Data. The Data Lakes store the collected data in their natural state – unlike a data warehouse which processes the data before storing. That’s why the “lake” and “warehouse” metaphor is apt. If you see data as water, a data lake can be thought of a water lake – storing water unfiltered and in its natural form, and a data warehouse can be thought of as water stored in bottles and kept on the shelf. 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 In-memory Databases In any computer system, the RAM, or Random Access Memory, is responsible for speeding up the processing. Using a similar philosophy, in-memory databases were developed so that you can move your Data to your system, instead of taking your system to the data. What that essentially means is that if you store data in-memory, it’ll cut down the processing time by quite a margin. Data fetching and retrieval won’t be a pain anymore as all the data will be in-memory. But practically, if you’re handling a really large dataset, it’s not possible to get it all in-memory. However, you can keep a part of it in-memory, process it, and then bring another part in-memory for further processing. To help with that, Hadoop provides several tools that contain both on-disk and in-memory databases to speed up the processing. 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 Wrapping Up… The list provided in the this article is by no means a “comprehensive list of Big Data tools and technologies”. Instead, it focuses on the “must-know” Big Data tools and technologies. The field of Big Data is constantly evolving and new technologies are outdating the older ones very quickly. There are many more technologies beyond the Hadoop-Spark stack, like Finch, Kafka, Nifi, Samza, and more. These tools provide seamless results sans hiccups. Each of these has their specific use cases but before you get working on any of them, it’s important to be aware of the ones we mentioned in the article. 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 If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. 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The ever-increasing growth in the production and analytics of Big Data keeps presenting new challenges, and the data scientists and programmers gracefully take it in their stride – by constantly improving the applications developed by them. One such problem was that of real-time streaming. Real-time data holds extremely high value for businesses, but it has a time-window after which it loses its value – an expiry date, if you will. If the value of this real-time data is not realised within the window, no usable information can be extracted from it. This real-time data comes in quickly and continuously, therefore the term “Streaming”. Analytics of this real-time data can help you stay updated on what’s happening right now, such as the number of people reading your blog post, or the number of people visiting your Facebook page. Although it might sound like just a “nice-to-have” feature, in practice, It is essential. Imagine you’re a part of an Ad Agency performing real-time analytics on your ad-campaigns – that the client paid heavily for. Real-time analytics can keep you posted on how is your Ad performing in the market, how the users are responding to it, and other things of that nature. Quite an essential tool if you think of it this way, right? Looking at the value that real-time data holds, organisations started coming up with various real-time data analytics tools. In this article, we’ll be talking about one of those – Apache Storm. We’ll look at what it is, the architecture of a typical storm application, it’s core components (also known as abstractions), and its real life-use cases. Let’s go! What is Apache Storm? Apache Storm – released by Twitter, is a distributed open-source framework that helps in the real-time processing of data. Apache Storm works for real-time data just as Hadoop works for batch processing of data (Batch processing is the opposite of real-time. In this, data is divided into batches, and each batch is processed. This isn’t done in real-time.)   Apache Storm does not have any state-managing capabilities and relies heavily on Apache ZooKeeper (a centralised service for managing the configurations in Big Data applications) to manage its cluster state – things like message acknowledgments, processing statuses, and other such messages. Apache Storm has its applications designed in the form of directed acyclic graphs. It is known for processing over one million tuples per second per node – which is highly scalable and provides processing job guarantees. Storm is written in Clojure which is the Lisp-like functional-first programming language. At the heart of Apache Storm is a “Thrift Definition” for defining and submitting the logic graph (also known as topologies). Since Thrift can be implemented in any language of your choice, topologies can also be created in any language. This makes Storm support a multitude of languages – making it all the more developer friendly. 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 Storm runs on YARN and integrates perfectly with the Hadoop ecosystem. It is a true real-time data processing framework having zero batch support. It takes a complete stream of data as an entire ‘event’ instead of breaking it into series of small batches. Hence, it is best suited for data which is to be ingested as a single entity. Let’s have a look at the general architecture of a Storm application – It’ll give you more insights into how Storm works! Apache Storm: General Architecture and Important Components There are essentially two types of nodes involved in any Storm application (as shown above). Master Node (Nimbus Service) If you’re aware of the inner-workings of Hadoop, you must know what a ‘Job Tracker’ is. It’s a daemon that runs on the Master node of Hadoop and is responsible for distributing task among nodes. Nimbus is a similar kind of service for Storm. It runs on the Master Node of a Storm cluster and is responsible for distributing the tasks among the worker nodes. Nimbus is a Thrift service provided by Apache which allows you to submit your code in the programming language of your choice. This helps you write your application without having to learn a new language specifically for Storm. As we talked earlier, Storm lacks any state managing capabilities. The Nimbus service has to rely on ZooKeeper to monitor the messages being sent by the worker nodes while processing the tasks. All the worker nodes update their task status in the ZooKeeper service for Nimbus to see and monitor. Worker Node (Supervisor Service) These are the nodes responsible for performing the tasks. Worker nodes in Storm run a service called Supervisor. The Supervisor is responsible for receiving the work assigned to a machine by the Nimbus service. As the name suggests, Supervisor supervises the worker processes and to help them complete the assigned tasks. Each of these worker processes executes a subset of the complete topology. A Storm application has essentially four components/abstractions that are responsible for performing the tasks at hand. These are: Topology The logic for any real-time application is packaged in the form of a topology – which is essentially a network of bolts and spouts. To understand better, you can compare it to the MapReduce jobs (read our article on MapReduce if you’re unaware of what that is!). One key difference is that the MapReduce job finishes when its execution is complete, whereas a Storm topology runs forever (unless you explicitly kill it yourself). The network consists of nodes that form the processing logic, and links (also known as the stream) that demonstrate the passing of data and execution of processes. Stream You need to understand what are tuples before understanding what are streams. Tuples are the main data structures in a  Storm cluster. These are named lists of values where the values can be anything from integers, longs, shorts, bytes, doubles, strings, booleans floats, to byte arrays. Now,.streams are a sequence of tuples that are created and processed in real-time in a distributed environment. They form the core abstraction unit of a Spark cluster. 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 Spout A sprout is the source of streams in a Storm tuple. It is responsible for getting in touch with the actual data source, receiving data continuously, transforming those data into the actual stream of tuples and finally sending them to the bolts to be processed. It can be either reliable or unreliable. A reliable Spout will replay the tuple if it failed to be processed by Storm, an unreliable Spout, on the other hand, will forget about the tuple soon after emitting it. Bolt Bolts are responsible for performing all the processing of the topology. They form the processing logic unit of a Storm application.  One can utilise bolt to perform many essential operations like- filtering, functions, joins, aggregations, connecting to databases, and many more.   Who Uses Storm? Although a number of powerful and easy to use tools have their presence in the market of Big Data, Storm finds a unique place in that list because of its ability to handle any programming language you throw at it. Many organisations put Storm to use. Let’s look at a couple of big players that use Apache Storm and how! Twitter Twitter uses Storm to power a variety of its systems – from the personalisation of your feed, revenue optimisation, to improving search results and other such processes. Because Twitter developed Storm (which was later bought by Apache and named Apache Storm), it integrates seamlessly with the rest of Twitter’s infrastructure – the database systems (Cassandra, Memcached, etc.), the messaging environment (Mesos), and the monitoring systems. Spotify Spotify is known for streaming music to over 50 million active users and 10 million subscribers. It provides a wide range of real-time features like music recommendation, monitoring, analytics, ads targeting, and playlist creations. To achieve this feat, Spotify utilises Apache Storm. Stacked with Kafka, Memcached, and netty-zmtp based messaging environment, Apache Storm enables Spotify to build low-latency fault-tolerant distributed systems easily. 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 To Wrap Up… If you wish to establish your career as a Big Data analyst, streaming is the way to go. If you’re able to master the art of dealing with real-time data, you’ll be the number one preference for companies hiring for an analyst role. There couldn’t be a better time to dive into real-time data analytics because that is the need of the hour in the truest sense! If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Engineering degrees online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.

If you’ve read our article on Big Data Tools and Technologies, you might remember coming across the term “MapReduce”. It’s one of the core components of the Hadoop architecture and forms the whole processing layer of Hadoop. In this article, we’ll be talking about MapReduce in a bit more depth – but in a beginner-friendly way. For your ease of understanding, we’ve broken down the article as follows: Introduction Core Concepts A Complete SWOT Analysis Introduction to MapReduce MapReduce is essentially a concept that allows lightning fast processing of massive datasets on a distributed cluster. It is a programming model patented by Google which was first adopted by the guys at Apache and is now at the heart of the whole Hadoop ecosystem. Its simplicity is what makes it so effective and commendable. MapReduce follows a very simple logic, one that’s been followed for ages for managing day to day tasks – when you have to deal with a lot of material, employing a large number of workers will pace up the process (the age-old adage “too many cooks spoil the broth” doesn’t fit here!).   However, at this point, one question arises – does MapReduce work well ONLY with huge datasets? The answer for this is very simple – the datasets needn’t necessarily be extremely large. However, owing to economic and computational reasons, it’s recommended to put MapReduce to work only if you have large datasets, large enough for traditional computations. It’s always better to process small datasets on your local machine itself – the way it was before MapReduce was introduced. Because honestly, using MapReduce for small chunks of data is very much like trying to kill a spider using a machine gun. The spider will get killed, no doubt, but is it worth it? 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 The Core Concepts of MapReduce By now, you have a fair idea of what exactly is MapReduce. In this section, we’ll talk a bit more about how exactly does MapReduce work – the core concepts that get the wheels in motion. If you’ve ever encountered basic mathematics in your life (which we hope you have if you’re reading this!), you must be aware of the concepts of “ordered pairs”. It’s simply a way of expressing two pieces of data in (x,y) form. Ordered pairs are very useful for representing coordinates, fractions, employee IDs, and other similar forms of data. MapReduce, too, relies heavily on the concept of ordered pairs – basically, any data can be “mapped” into the form of an ordered pair, and can be “reduced” in a variety of ways, depending on the problem statement at hand. Suppose you’re left alone in a digital library containing a hundred thousand books and given a daunting task of finding the number of times a particular word occurs in all the books combined. Phew, how would you start? The first approach would be to write a program that’d iterate over every word, right from Book 1 – Word 1, to Book n – Word n, and keep incrementing a counter variable each time you encounter the needed word. Just imagining this would make you realize the amount of time it’ll take to accomplish this task. Now, let’s take a look at how MapReduce will respond to the task: MapReduce Requirements: MapReduce works well in a distributed cluster. What is a distributed cluster, you ask? Very simply, it is a large number (often thousands!) of commodity computers (low-cost computers) interwoven into a network that performs a single operation. Now, assume you have one such cluster. For our problem statement, the input to this cluster will be the content of all the books. As soon as we feed this data into our system, it gets copied into each and every computer (node) on the cluster (assuming we’re only dealing with one job at the moment). This is done to provide a level of fault tolerance – to help you in case of data loss. Programming In MapReduce Since MapReduce works in two phases – Map and reduce, we have to write two programs, Map() and Reduce(), to get a MapReduce workflow up and running. Earlier you had use Java to write these programs, but now because of the rapid growth in Data Science, the MapReduce framework has been made flexible enough to handle codes written in Python or R too. The scripts you write will run one after the other, that is, in a successive manner. This brings us to one of the major drawbacks of MapReduce paradigm – the fact that you cannot run both the scripts in parallel. Accomplishing that will make MapReduce a lot faster, and that has been a subject of extensive research. Workflow Parallelization: The input (content of all the books) will be divided into various segments (equal to the number of computers in the cluster, essentially), and each computer will be assigned a  particular segment. Suppose we’ve divided the file based in lines, then node 1 might be asked to take care of lines 1 to 10,000, node 2 might be asked for like 10,001 to 20,000, and so on. (Remember these are just illustrative figures – the segments are much larger in real life) Each machine will then run your Map() program. The Map() Program: The Map() program does exactly what you were going to do before giving the task to MapReduce – scanning the file and reading one word at a time. The benefit here is that it’ll be running simultaneously on multiple computers, so it’ll reduce the time by manifolds. The output of the Map() function is an ordered pair. In this case, it will simply be (word, count), i.e. when the Map() function encounters the word “hello” (assuming that’s the word we’re looking for), it will simply eject (hello, 1) as an output. Since various parts of the code body are being worked on at the same time, you will have a mapped version of your dataset in no time! To appreciate the simplicity of MapReduce, realize that the operation being performed here is fairly basic requiring no intensive calculations. Shuffling of the Mapped results: Once we get the final ordered pairs from the Map() function, the results are shuffled. That simply means that (word, count) pairs with the same word are transferred to a single machine. Now, if you’re acquainted with programming, you’ll realize that there is a limit to which this can be achieved. There are literally billions of words and only a finite number of computers in the cluster. At this point, it’s important to note that the shuffle phase is not mandatory for MapReduce to function, it simply makes the Reduce() script much faster by arranging things in an order. For now, let’s just say that we shuffled our Map outputs, and now have all the instances of the word “hello” in a single computer in the form of an ordered pair – (hello, n) where n is the number of times “hello” occurred in one particular book. Finally, the Reduce() phase: Finally, we come to the last phase – Reduce(). If you were paying attention to the whole workflow, you might have correctly guessed that Reduce() will just count the number of times each word appears in the input file. All it has to do is simply add the second component of our ordered pairs. If there are, say 200 mentions of the word “hello” in all the books, the final output that we will get after this phase will be – (hello, 200). Just by simple counting and addition, we’ve arrived at the result. All made possible by employing a number of computers and making them work simultaneously. 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 MapReduce SWOT Analysis The MapReduce paradigm has made the lives of Data Scientists easier. Let’s have a look at what are the strengths, weaknesses, opportunities, and threats of this framework: Strengths Processes massive datasets at lightning fast speeds by processing them parallelly in distributed clusters. Weaknesses The Map() and Reduce() scripts run successively. Read/Write operations are performed on HDFS, which is slow by comparison Opportunities Generating insights from previous discarded sets of data and processing Big Data generated by organizations. Threats Standalone MapReduce operations might slow down the pace of the  Big Data industry without sufficient advancement in analytics. 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 In Conclusion… Now that you’re familiar with the inner-workings of MapReduce, you’ll appreciate that it is by no means a new technology – we have been using similar paradigms to handle large volume of data, like election results, for a very long time now. You’ll also realize the importance of keeping things simple – MapReduce does nothing fancy, at all, and that’s what makes it so powerful. If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. 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? Learn Software Engineering degrees online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.

If you’re a complete newbie in the world of Big Data, the term itself might be slightly confusing. Before we move to the technicalities, let’s ask two essential questions: How big? What data? The answer to the first question isn’t fixed – it would’ve changed by the time you’d have completed reading this line. For all we know, by the time you’ve read through the article, the total amount of data in the world would have soared by quite a bit. According to IBM, we create roughly 2.5 quintillion bytes of data per day – To put things in perspective, that is the capacity you’ll need to hold around 530,000,000 MP3 songs. Look at that number again, there are quite a lot of zeros in there. Now, let’s talk about the “what”. What data is this? It’s almost like the famous song by The Police, which goes something like… “Every breath you take, every move you make, every bond you break, every step you take, I’ll be watching you.” And that’s what they’re doing. By they, we simply mean the ones who’re in charge of collecting this data. Everything you do on the internet is adding to this colossal mountain of data. Your Facebook posts, Tweets, Snapchat stories, and whatever the kids are using these days – are just bricks in the huge wall of Big Data. Watch Youtube video. So, to answer your second question – the data in question is the very data you’re producing every passing moment. Every time you book a cab, or order food online, or even do a very basic google search – It’s all going on top of the heap. Everything is being collected. That’s what is making this big data, bigger – every passing minute.   Now that you’re in control of the situation, let’s dive a little deeper into the ocean of Big Data. Further, we’ll look at why exactly does Big Data matter so much, and who’re the ones benefiting from it? 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 is Big Data? By now, we’re clear that Big Data is just an extremely large volume of data – both structured and unstructured – collected through a variety of sources and in a variety of formats. For the sake of a formal definition, you can have a look at how IBM defines “Big Data”: According to the data scientists at IBM, Big Data can typically be characterized by 4 V’s – Volume, Variety, Velocity, and Veracity. Volume Very simply, volume means how “big” the Big Data is. Like we said earlier, there’s no specific number to it, it’s ever-increasing. Variety The data we’re talking about comes from a number of sources, hence it is in numerous formats. We’re talking about data in the form of audio, video, pdf, email, and more! Most of this data is unstructured – implying not much sense can be made out of it without a proper study.   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 Velocity The flow of Big Data from the variety of sources we discussed above is massive and un-ending. Like we said, by the time you’ve read this article, the amount of Big Data in the world would have increased drastically. If you don’t believe us, listen to the guys at IBM who claim that by 2020, there’ll be 5,200 GB of data for each and every person on Earth. Yeah, talk about velocity! Veracity Veracity in context of Big Data simply refers to the noises and anomalies present in the data. When dealing with Big Data, veracity is one of the biggest challenges that data analysts face. By now, it’s clear that there’s a lot of data around us, almost too much to even think about! Making sense of this data is quite a daunting task in itself. For this, we have data analysts – the heart and soul of any organization’s analytics team – but how exactly do businesses use data to power their operations? Let’s see. 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 matters – but why? The organizations which earlier had to rely on the data collected through archaic spreadsheets now have access to tonnes of data on their customers. Data that can be used to overhaul their business and make profits like never before. Watch Youtube video. Sherlock Holmes puts it right – “It’s a capital mistake to theorize before one has data!” And today, businesses HAVE data – a lot of it. But how exactly does it help them? By carefully examining the data at hand, organizations are performing the following kinds of intricate analytics to gather actionable insights and perform better in the market: Social listening It gives the organizations the power to know the real-time feedback of their consumers. The days of polls or surveys are long gone – sentiment analysis provides much more comprehensive and actionable feedback. Tools like HootSuite, TweetReach, Klout, and BuzzSumo are just a few examples of social listening tools that help the organizations stay a step ahead by knowing what the consumers have to say, their sentiments, and feedback. Comparative analysis Thanks to Big Data, organizations can now compare their products, services, and overall brand image with their competitors by examining user-behavior metrics in real-time. Marketing analytics This helps organizations in promoting new products and services to the target audience in a much more informed and innovative way. There are various sophisticated tools dedicated to Marketing Analytics which help organizations keep a close eye on how their product is being received in the market. Some common tools for this include – Marketing Evolution, Predictive Modeling, Lattice Engines – all of which aim to improve the organization’s ROI by leveraging Big Data. Targeting Using this stream of Big Data analytics, organizations can dive into social media activity on any subject, based on a variety of sources, all in real-time. For example, let’s say you want to target specific customer groups and provide them with exclusive special offers – you can do that now, using Big Data. It’s a win-win situation for both the organization as well as the customers. The same tools as the ones discussed in Social Listening can be used for this purpose as well. Customer satisfaction Organizations can boost customer engagement manifold by analyzing Big Data from a multitude of sources. Also, using these metrics, they’re able to figure out, and eventually iron out any potential customer issues that might go viral – preserving brand loyalty and improving customer service, at the same time. Who’s using Big Data – Real-world applications It’s safe to say that no domain of business today is untouched by the magic that is Big Data. From banking, to healthcare, to social-media, to education, to even government sectors – the list can go on – everyone is trying their best to make sense of the data at hand and outperform their competition. Let’s see some major industries that are affected by the giant that is Big Data: Healthcare Providers Asia’s largest healthcare group – Apollo hospitals – is using Big Data and analytics to control HAI (hospital-acquired infections). Education Big data is used quite extensively to improve higher education. Take the example of the University of Tasmania. It has deployed a management system that tracks things like the time at which a student logs on to the system, time spent on different pages of the system, and even the overall progress of the student. Government Operations Big Data has a wide range of applications in government operations and services. They include energy exploration, fraud detection, environmental protection, financial analysis, and health-related research. We can go on and on about each and every industry, but we think you get the gist. Big Data analytics is being used wherever it is possible. And frankly, there’s no domain that can’t use a little data analytics to improve their operations. Because at the end of the day, data is all that’s there, and all there will ever be. To wrap things up… It’s safe to say that Big Data is not just a fad – it’s a revolution. It’s always better to stay on your toes when you’re in the middle of a revolution, or you’ll be left behind before you know it. What makes one particular organization stand out from the rest is the way they deal with their data. Having said that, it’s only fair to conclude by saying that the demand for good data scientists is, and will keep on, increasing. So, buckle up while you can, and get started with exploring the mad but genius world of Big Data! If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Engineering degrees online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. 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?

Piyush Kumar is the Head of Data Platform Engineering at MakeMyTrip. He heads the Data team (Data platform, Data Science, and Business Intelligence functions) at MakeMyTrip to support various Lines of Business such as Flights, Hotels, Holidays, and Ground. Along with defining Big Data strategy, he looks after designing and building a scalable and distributed machine learning platform for Big Data systems with real-time streaming and batch processing for Clickstream, Mobile, Transactional, CRM (Customer relationship management) & user feedback or reviews data. In an exclusive interview, Piyush provides valuable insights to UpGrad about how MakeMyTrip has leveraged Big Data, in line with current trends, to upgrade and enhance its product offerings. In this first video, Piyush talks about how MakeMyTrip uses Big Data to solve critical business problems in the area of customer segmentation, personalisation, building data pipelines, etc. He also explains the architecture of the Big Data system at MakeMyTrip. In the second video, Piyush shares insights on career planning for Big Data enthusiasts highlighting different career paths available in Big Data and the necessary skill sets required. So, Piyush spoke about how MakeMyTrip uses Big Data in their operations. He provided valuable insights to UpGrad about how MakeMyTrip is leveraging Big Data, in line with current trends, to upgrade and enhance its product offerings. He shared insights on career planning for big data enthusiasts highlighting the necessary skill sets required. 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 Are you planning a big data career? If you want us to cover other topics and interview other industry experts please let us know your thoughts in the comments section. 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 If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. 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 Learn Software Engineering degrees online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. 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?