Top 50 Big Data Interview Questions and Answers blog

Top 50 Big Data Interview Questions and Answers

Organizations are searching for talent at all levels in the area of Big data. With these top 50 Big data interview questions and answers, you can get ahead of the competition for that big data career. Big Data is a game-changer technology. It has changed the way data are previously collected and processed, and it is expected to continue to do so shortly. The massive amounts of data aren’t as overwhelming as they once were. Big Data has applications in every industry and has aided the growth of the automation and artificial intelligence (AI) industries. This is why every company in the world needs Big Data experts to help them streamline their operations by handling large amounts of structured, unstructured, and semi-structured data. Since Big Data has become a standard, there are a plethora of job opportunities. This article will go over some of the most popular Big Data interview questions and how to respond to them. BIG DATA INTERVIEW QUESTIONS FOR FRESHERS The below basic set of big data questions will strongly equip freshers to face the interview. 1. Tell us about Big Data in your own words. Big Data is a collection of huge amounts of data that cannot be handled, stored, or analyzed using conventional data processing techniques due to its scale and exponential growth. 2. Explain in detail the 3 different types of big data. STRUCTURED DATA: It implies that the information can be processed, stored, and retrieved in a predetermined format. Contact numbers, social security numbers, ZIP codes, employee records, and wages, among other things, are examples of highly ordered information that can be quickly accessed and processed. UNSTRUCTURED DATA: This is data that does not have a particular structure or type. Audio, video, social media posts, digital surveillance data, satellite data, and other forms of unstructured data are the most common types. SEMI-STRUCTURED DATA: This is an undefined but essential term that applies to both structured and unstructured data formats. 3. What is Hadoop? Hadoop is an open-source software architecture for storing and processing data on commodity hardware clusters. It has a lot of storage for any kind of data, a lot of computing power, and it can handle practically unlimited concurrent tasks or jobs. 4. Are Hadoop and Big Data interconnected? Big Data is a resource, and Hadoop is an open-source software application that helps to manage that resource by achieving a set of goals and objectives. To extract actionable insights, Hadoop is used to process, store, and analyze complex unstructured data sets using proprietary algorithms and methods. So, Yes, they are related, but they are not the same. 5. Mention the important tools used in Big Data analytics. The important tools used in Big Data Analytics are as follows,
  • NodeXL
  • Tableau
  • Solver
  • OpenRefine
  • Rattle GUI
  • Qlikview
6. Explain the 5 V’s of Big Data? Big data has five ‘Vs’: – Value, Variety, Veracity, Velocity, Volume. Value: The worth of the data being collected is referred to as value. Variety (Data in a variety of formats): Variety describes various types of data, such as text, audios, images, photographs, and PDFs, among others. Veracity (Data in Doubt): Veracity refers to the processed data’s consistency, trustworthiness, and accuracy. Velocity (Data in motion): The speed at which data is produced, processed, and analyzed is referred to as velocity. Volume (Data at Rest): The volume or sum of data is represented by volume. The amounts of data are mostly contributed by social media, cell phones, vehicles, credit cards, photographs, and videos. 7. List of the various vendor-specific distributions of Hadoop? Hadoop uses the various vendor-specific distributions as below,
  • Cloudera
  • MapR
  • Amazon EMR (Elastic MapReduce)
  • Microsoft Azure HDInsight
  • IBM InfoSphere Information Server for Data Integration and
  • Hortonworks.
8. Explain FSCK? HDFS uses the FSCK command, which stands for File System Check. It checks if a file is corrupt, if it has a copy, and if it has any missing blocks. FSCK produces a summary report that summarises the file system’s overall health. 9. Explain in your own words about HDFS? Hadoop Distributed File System (HDFS) is a fault-tolerant file system that operates on commodity hardware. HDFS is a distributed storage and processing system that includes file permissions and authentication. NameNode, DataNode, and Secondary NameNode are the three components that make up this node. 10. Explain about YARN? YARN stands for Yet Another Resource Negotiator and is a key component of Hadoop 2.0. It is a Hadoop resource management layer that allows various data processing engines to run and process data stored in HDFS, such as graph processing, interactive processing, stream processing, and batch processing. The two key components of YARN are ResourceManager and NodeManager. 11. What do you mean by Commodity Hardware? The basic hardware resource needed to run the Apache Hadoop system is commodity hardware. It’s a general term for low-cost devices that are typically compatible with other low-cost devices. 12. Tell me about Logistic Regression? Logistic regression, also known as the logit model, is a technique for predicting a binary outcome from a linear combination of predictor variables. 13. Explain Distributed Cache? The Hadoop MapReduce framework’s Distributed Cache is a dedicated service that is used to cache files whenever they are required by applications. Read-only text files, directories, and jar files, for example, can be cached and accessed and read later on each data node where map/reduce tasks are running. 14. In how many modes Hadoop can run? Hadoop can run in three different modes:
  • Standalone mode
  • Pseudo Distributed mode (Single node cluster)
  • Fully distributed mode (Multiple node cluster)
15. What are the most common data management tools for Hadoop Edge Nodes? The following are the most widely used data management methods for Hadoop Edge Nodes:
  • Oozie
  • Ambari
  • Pig
  • Flume
16. When several clients attempt to write to the same HDFS file, what happens? At the same time, multiple users cannot write to the same HDFS file. Since HDFS NameNode supports exclusive write, inputs from the second user will be rejected when the first user accesses the file. 17. Explain Block in HDFS? When a file is stored in HDFS, the whole file structure is broken down into a series of blocks, and HDFS has no idea what is in the file. Hadoop blocks must be 128MB in size. This value can be customized for each file. 18. Tell us about Collaborative Filtering? Collaborative filtering is a collection of technologies that predict which products a specific user would like based on the preferences of a group of people. It’s simply a technical term for asking people for their opinions. 19. Explain the ‘jps’ command functions? We can use the ‘jps’ command to see whether Hadoop daemons such as namenode, datanode, resourcemanager, nodemanager, are running on the machine. 20. List the various Hadoop and YARN daemons. Hadoop Daemons are, NameNode, Datanode, and Secondary NameNode YARN Daemons are ResourceManager, NodeManager, and JobHistoryServer 21. Define Checkpoints? In HDFS, a checkpoint is important for maintaining file system metadata. By entering fsimage and the edit log, it establishes file system metadata checkpoints. Checkpoint is the name of the latest version of fsimage. 22. How is Big Data used in business? Big Data allows businesses to gain a greater understanding of their customers by allowing them to conclude from vast data sets accumulated over time. It aids them in making better choices. 23. Why Hadoop in Big Data? We need a system to process Big Data. Hadoop is a free and open-source platform developed by the Apache Software Foundation. When it comes to processing large amounts of data, Hadoop is a must-have. 24. List the primary steps to take while dealing with big data? Start processing with Big Data with the below basic steps like,
  • Data Ingestion
  • Data Storage and
  • Data Processing
25. Tell us about Fault Tolerance in Hadoop. Hadoop’s data is extremely available. Since each piece of data is reproduced three times by default, there is very little to no risk of data loss. As a result, Hadoop is regarded as a fault-tolerant system. BIG DATA INTERVIEW QUESTIONS FOR EXPERIENCED If you have significant experience working in the Big Data sector, you will be asked a series of questions in your big data interview that are based on your prior experience. These questions could simply be about your background or based on a scenario. So, get ready for your Big Data interview with these top Big Data interview questions and answers. 26. Which configuration of hardware is best for Hadoop jobs? For running Hadoop operations, dual processors or core machines with 4 / 8 GB RAM and ECC memory are suitable. The hardware design, on the other hand, varies depending on the project’s workflow and process flow and must be customized accordingly. 27. When a NameNode goes down, how do you get it back up? To get the Hadoop cluster up and running, perform the following steps:
  • To start a new NameNode, use the fsimage, which is a file system metadata replica.
  • Configure the DataNodes as well as the clients to recognize the newly launched NameNode.
  • The client will be served once the new NameNode has finished loading the last checkpoint FsImage and obtained enough block reports from the DataNodes.
The NameNode recovery process takes a long time in large Hadoop clusters, and it becomes a more significant challenge during routine maintenance. 28. Explain RackAwareness in Hadoop? It’s an algorithm that determines where blocks and replicas are put on the NameNode. Network traffic between DataNodes within the same rack is reduced based on rack definitions. If the replication factor is 3, for example, two copies will be placed on one rack and the third copy will be placed on a different rack. 29. What are the commands for starting and shutting down the Hadoop daemons? /sbin/ to start all daemons and To stop all daemons, ./sbin/ 30. Name the reducer’s core methods. A reducer’s three main methods are, setup(), reduce() and cleanup() 31. What are Hadoop’s real-time applications? Hadoop has real-time implementations in the following areas:
  • Management of information.
  • Financial services.
  • Cybersecurity and protection.
  • Managing social media posts.
32. In Hadoop architecture, what are JT, TT, and Secondary name nodes? JT – Job Tracker is that assigns jobs to Task Trackers. TT – Task Tracker that performs the job that JT has assigned to it. The metadata information of the name node is stored in a secondary name node. The name node information in the secondary name node is changed every 30 minutes. 33. Tell us about Hive? Hive is a data-querying and data-processing platform. Hive is a Facebook project that was donated to the Apache Software Foundation. Hives are primarily used to store structured data. 34. What will happen after you create a table in Hive? All metadata will be stored in the meta store database, and a default directory with the table name will be built in /hive/usr/warehouse. 35. Name two methods for detecting outliers. Extreme Value Analysis: Extreme value analysis determines the statistical tails of the data distribution; statistical approaches like Altman Z-scores on univariate are good examples. Probabilistic and statistical models: Determine the unlikely cases using a probabilistic data model. 36. Explain the 2 types of Tables in Hive? Managed/internal table: When a table is deleted, both the metadata and the actual data are removed. External table: Only the metadata, not the actual data, is removed when a table is deleted. 37. How will you manage to create a table in Hive? hive>create table student(sname string, sid int) //hands on hive>describe student; 38. What is the command to write static partitioned tables? hive>create table student(sname string, sid int) partitioned by(int year) 39. Can you name the companies that use Big Data?
  • Facebook
  • Adobe
  • Yahoo
  • Twitter
  • Ebay
40. What are the Port Numbers for NameNode, Task Tracker, and Job Tracker? Name Node – Port 50070 Task Tracker – Port 50060 Job Tracker – Port 50030 41. Which configuration of hardware is best for Hadoop jobs? Hadoop operations are best performed on machines with dual processors or cores, 4 / 8 GB RAM, and ECC memory. The hardware design, on the other hand, differs depending on the project-specific workflow and phase of the flow, necessitating customization. 42. What do you know about JobTracker in Hadoop?
  • JobTracker is a Hadoop JVM process for submitting and tracking MapReduce jobs.
  • In Hadoop, JobTracker conducts the following tasks in order:
  • JobTracker receives jobs that are submitted by a client application.
  • NameNode is notified by JobTracker to evaluate the data node.
  • JobTracker assigns TaskTracker nodes based on the number of slots open.
  • It submits the work to the TaskTracker Nodes that have been assigned to it, and JobTracker keeps an eye on the TaskTracker nodes.
43. Why is Data Locality needed in Hadoop? Datasets in HDFS are stored as blocks in the Hadoop cluster’s DataNodes. Person Mapper processes the blocks during the MapReducejob execution (Input Splits). If the data does not reside on the same node where the Mapper is running the job, it must be copied from DataNode to Mapper DataNode over the network. Now, if a MapReduce job has more than 100 Mappers, and each Mapper attempts to copy data from another DataNode in the cluster at the same time, severe network congestion can result, causing a major performance problem for the entire system. As a result, data proximity computation is an efficient and cost-effective approach, which is referred to as Data localization in Hadoop. It aids in increasing the system’s overall throughput. 44. Tell the command to format the NameNode? $ hdfs namenode -format 45. Tell the major difference between HDFS Block and Input Split? HDFS separates the input data into physical blocks for processing, which are referred to as HDFS Blocks. For mapping operations, the input split is a logical division of data by the mapper. 46. Explain MapReduce and write its syntax to run a MapReduce program? MapReduce is a Hadoop programming model for processing large data sets through a cluster of computers, which is generally referred to as HDFS. It’s a blueprint for parallel programming. hadoop_jar_file.jar /input_path /output_path is the syntax for running a MapReduce programme. 47. What happens if a NameNode isn’t populated with any data? In Hadoop, a NameNode with no data does not exist. If a NameNode exists, it will either contain data or will not exist. 48. Tell us about Sequencefileinputformat? Hadoop makes use of a file format known as a Sequence file. A serialized key-value pair is used to store data in the sequence register. The input format for reading sequence files is sequencefileinputformat. 49. DFA can handle large volumes of data. Then why do you need Hadoop Framework? Hadoop is used not only to store but also to process vast amounts of data. While DFS (Distributed File System) can also store data, it lacks the following features:
  • DFA is not fault-tolerant
  • The amount of data that can be moved over a network is determined by bandwidth.
50. What are the fundamental parameters of a Mapper? LongWritable and text, Text and IntWritable are the basic parameters of a Mapper. CONCLUSION: As the Big Data world continues to develop, new opportunities for Big Data practitioners emerged. This comprehensive collection of Big Data interview questions and answers will undoubtedly assist you throughout your interview. Certifications, on the other hand, cannot be overlooked. So, get Big Data certification at NSCHOOL Academy and add a certification to your resume if you want to show your expertise to your interviewer during a big data interview. Simply leave a comment below if you have any questions about Big Data. Our Big Data specialists will gladly assist you. Best of Luck with your Big Data Interview!