Big Data is revolutionary. It has evolved the way data was collected and analyzed earlier, and it is expected to keep on evolving in the near future. The huge volumes of data are n­o longer intimidating. Big Data has its applicability in every industry and it has been contributing to the expansion of automation and Artificial Intelligence (AI) segments. This is why every business across the world poses requirements for Big Data professionals to streamline their business services by managing big volumes of structured, unstructured, and semi-structured data.

Since Big Data has now become mainstream, employment opportunities are immense. Employers seek professionals with a good command over the subject, hence knowing all the technicalities of the subject and strong market knowledge is something that can help you to fetch a job. This article will discuss some of the most commonly asked Big Data interview questions and their answers.

Q1. What is Big Data?

Ans. Big Data is a set of massive data, a collection of huge in size and exponentially growing data, that cannot be managed, stored, and processed by traditional data management tools.

Q2. What are the different types of Big Data?

Ans. There are three types of Big Data.

Structured Data – It suggests that the data can be processed, stored, and retrieved in a fixed format. It is a highly organized information that can be easily assessed and stored, for e.g. phone numbers, social security numbers, ZIP codes, employee information, and salaries, etc.

Unstructured Data – This refers to the data that has no specific structure or form. The most common types of unstructured data are formats like audio, video, social media posts, digital surveillance data, satellite data, etc.

Semi-structured Data – This refers to both structured and unstructured data formats and is unspecified yet important.


Q3. Are Hadoop and Big Data co-related?

Ans. Big Data is an asset, while Hadoop is an open-source software program, which accomplishes a set of goals and objectives to deal with that asset. Hadoop is used to process, store, and analyze complex unstructured data sets through specific proprietary algorithms and methods to derive actionable insights. So yes, they are related but are not alike.

Q4. Why is Hadoop used in Big Data analytics?

Ans. Hadoop is an open-source framework in Java, and it processes even big volumes of data on a cluster of commodity hardware. It also allows running many exploratory data analysis tasks on full datasets, without sampling. Features that make Hadoop an essential requirement for Big Data are –

  • Data collection
  • Storage
  • Processing
  • Runs independently

Q5. Name some of the important tools useful for Big Data analytics.

Ans. The important Big Data analytics tools are –

  • NodeXL
  • Tableau
  • Solver
  • OpenRefine
  • Rattle GUI
  • Qlikview

Q6. What are the five ‘V’s of Big Data?

Ans. The five ‘V’s of Big data are –

Value – Value refers to the worth of the data being extracted.

Variety (Data in Many forms) – Variety explains different types of data, including text, audios, videos, photos, and PDFs, etc.

Veracity (Data in Doubt) – Veracity talks about the quality or trustworthiness and accuracy of the processed data.

Velocity (Data in Motion) – This refers to the speed at which the data is being generated, collected, and analyzed.

Volume (Data at Rest) – Volume represents the volume or amount of data. Social media, mobile phones, cars, credit cards, photos, and videos majorly contribute to the volumes of data.

Q7. What are HDFS and YARN? What are their respective components?

Ans. HDFS or Hadoop Distributed File System runs on commodity hardware and is highly fault-tolerant. HDFS provides file permissions and authentication, and is suitable for distributed storage and processing. It is composed of three elements, including NameNode, DataNode and Secondary NameNode.

YARN is an integral part of Hadoop 2.0 and is an abbreviation for Yet Another Resource Negotiator. It is a resource management layer of Hadoop and allows different data processing engines like graph processing, interactive processing, stream processing, and batch processing to run and process data stored in HDFS. ResourceManager and NodeManager are the two main components of YARN.

Also Read>>Top Big Data Certifications That Will Boost Your Career

Q8. What is FSCK?

Ans. FSCK or File System Check is a command used by HDFS. It checks if any file is corrupt, has its replica, or if there are some missing blocks for a file. FSCK generates a summary report, which lists the overall health of the file system.

Q9. Name some key components of a Hadoop application.

Ans. The key components of a Hadoop application are –

  • HDFS
  • YARN
  • MapReduce
  • Hadoop Common

Also Read>>Career Advantages of Hadoop Certification!

Q10. What are the different core methods of a Reducer?

Ans. There are three core methods of a reducer-

setup() – It helps to configure parameters like heap size, distributed cache, and input data size.

reduce() – Also known as once per key with the concerned reduce task. It is the heart of the reducer.

cleanup() – It is a process to clean up all the temporary files at the end of a reducer task.

Q11. What is the command for starting all the Hadoop daemons together?

Ans. The command for starting all the Hadoop daemons together is –


Q12. What are the most common input formats in Hadoop?

Ans. The most common input formats in Hadoop are –

  • Key value input format
  • Sequence file input format
  • Text input format

Q13. What are the different file formats that can be used in Hadoop?

Ans. File formats used with Hadoop, include –

  • CSV
  • JSON
  • Columnar
  • Sequence files
  • AVRO
  • Parquet file

Q14. What is the standard path for Hadoop Sqoop scripts?

Ans. The standard path for Hadoop Sqoop scripts is –

/usr/bin/Hadoop Sqoop


Q15. What is commodity hardware?

Ans. Commodity hardware is the basic hardware resource required to run the Apache Hadoop framework. It is a common term used for affordable devices, usually compatible with other such devices.

Q16. What do you mean by logistic regression?

Ans. Also known as the logit model, logistic regression is a technique to predict the binary result from a linear amalgamation of predictor variables.

Also Read>>Top Hadoop Interview Questions & Answers

Q17. What is the goal of A/B Testing?

Ans. A/B testing is a comparative study, where two or more variants of a page are presented before random users and their feedback is statistically analyzed to check which variation performs better.

Q18. What is Distributed Cache?

Ans. Distributed Cache is a dedicated service of Hadoop MapReduce framework, which is used to cache the files whenever required by the applications. This can cache read-only text files, archives, jar files, among others, which can be accessed and read later on each data nodes where map/reduce tasks are running.

Q19. Name the modes in which Hadoop can run.

Ans. Hadoop can run on three modes, which are –

  • Standalone mode
  • Pseudo Distributed mode (Single node cluster)
  • Fully distributes mode (Multiple node cluster)

Q20. Name the port numbers for NameNode, Task Tracker, and Job Tracker.

Ans. NameNode – Port 50070

Task Tracker – Port 50060

Job Tracker – Port 50030


Q21. Name the most popular data management tools used with Edge Nodes in Hadoop.

Ans. The most commonly used data management tools that work with Edge Nodes in Hadoop are –

  • Oozie
  • Ambari
  • Pig
  • Flume

Q22. What happens when multiple clients try to write on the same HDFS file?

Ans. Multiple users cannot write on the same HDFS file at the similar time. When the first user is accessing the file, inputs from the second user will be rejected because HDFS NameNode supports exclusive write.

Q23. What do you know about collaborative filtering?

Ans. Collaborative filtering is a set of technologies that forecast which items a particular consumer would like depending on the preferences of the scores of individuals. It is nothing but the tech word for questioning individuals for suggestions.

Q24. What is a block in Hadoop Distributed File System (HDFS)?

Ans. When the file is stored in HDFS, all file system breaks down into a set of blocks and HDFS unaware of what is stored in the file. A block size in Hadoop must be 128MB. This value can be tailored for individual files.

Q25. Name various Hadoop and YARN daemons.

Ans. Hadoop daemons –

  • NameNode
  • Datanode
  • Secondary NameNode

YARN daemons

  • ResourceManager
  • NodeManager
  • JobHistoryServer

Q26. What is the functionality of ‘jps’ command?

Ans. The ‘jps’ command enables to check if the Hadoop daemons like namenode, datanode, resourcemanager, nodemanager, etc. are running on the machine.


Q27. What types of biases can happen through sampling?

Ans. Three types of biases can happen through sampling, which are –

  • Survivorship bias
  • Selection bias
  • Under coverage bias

Q28. Define Active and Passive Namenodes.

Ans. Active NameNode runs and works in the cluster, whereas Passive NameNode has comparable data like active NameNode.

Q29. How will you define checkpoint?

Ans. Checkpoint is a crucial element in maintaining filesystem metadata in HDFS. It creates checkpoints of file system metadata by joining fsimage with edit log. The new version of fsimage is named as Checkpoint.

Q30. What is the major differences between “HDFS Block” and “Input Split”?


HDFS Block Input Split
Physical division of the data Logical division of the data
Divides data in blocks to store the blocks together for processing Divides the data into the input split and assign it to mapper function for processing
Minimum amount of data that can be read/write Doesn’t contain any data and is only used during data processing by MapReduce

Q31. What is the command for checking all the tables available in a single database using Sqoop?

Ans. The command for checking all the tables available in a single database using Sqoop is –

Sqoop list-tables –connect jdbc: mysql: //localhost/user;


Q32. How do you proceed with data preparation?

Ans. Since data preparation is a critical approach to big data projects, the interviewer might be interested in knowing what path you will take up to clean and transform raw data before processing and analysis. As an answer to this Big Data interview question, you should discuss the model you will be using, along with logical reasoning for it. In addition, you should also discuss how your steps would help you to ensure superior scalability and accelerated data usage.

Q33. What is the main difference between Sqoop and distCP?

Ans. DistCP is used for transferring data between clusters, while Sqoop is used for transferring data between Hadoop and RDBMS, only.

Q34. How do you transform unstructured data into structured data?

Ans. Structuring of unstructured data has been one of the essential reasons why Big Data revolutionized data science domain. The unstructured data is transformed into structured data to ensure proper data analysis. In reply to this interview question, you should first differentiate between these two types of data and then discuss the methods you use to transform one form to another. Emphasize the role of machine learning in data transformation while sharing your practical experience.


Q35. How much data is enough to get a valid outcome?

Ans.  All the businesses are different and measured in different ways. Thus, you never have enough data and there will be no right answer. The amount of data required depends on the methods you use to have an excellent chance of obtaining vital results.

These big data interview questions and answers will help you get a dream job of yours. You can always learn and develop new Big Data skills by taking one of the best Big Data courses.