Top Hadoop Interview Questions & Answers

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Hadoop is an open source, Java-based programming framework that supports the processing and storage of extremely large data sets in a distributed computing environment. It has become one of the important tools for big data professionals, giving them the ability to process large sets of data, which was otherwise difficult with traditional methods.

Big data is a growing industry with worldwide revenues growing every year and with the help of innovative new technology, it has become a common practice among many organisations. If you want to be an expert in the field, you have to be proficient in some of the big data tools and that includes Hadoop. You can go for a Hadoop certification course to improve your job opportunities.

Being successful in a job interview is the first step to the start of your big data career. Always be prepared to answer all types of hadoop interview questions — technical skills, interpersonal, leadership or methodology.

If you are looking to crack a Hadoop interview, here are some of the Hadoop interview questions along with answers that are frequently asked:


Q1. What are the five ‘Vs’ of big data?


Ans. The five ‘Vs’ are volume, velocity, variety, veracity, and value.


Q2. What is Hadoop MapReduce?


Ans. It is a java based programming paradigm of Hadoop framework that provides scalability across various Hadoop clusters.


Q3. What are HDFS and YARN?


Ans. HDFS (Hadoop Distributed File System) is the storage unit of Hadoop. It is responsible for storing different kinds of data as blocks in a distributed environment.

YARN (Yet Another Resource Negotiator) is the processing framework in Hadoop, which manages resources and provides an execution environment to the processes.


Q4. What is a block and block scanner in HDFS?


Ans. Block is the minimum amount of data that can be read or written. The default size of a block in HDFS is 64MB for Hadoop1 and 128MB for Hadoop2.

Block Scanner tracks the list of blocks present on a DataNode and verifies them to find any kind of checksum errors.


Q5. Explain the difference between NameNode, Backup Node and Checkpoint NameNode.


Ans. NameNode is at the heart of the HDFS file system which manages the metadata.

Checkpoint Node keeps track of the latest checkpoint in a directory that has same structure as that of NameNode’s directory.

Backup Node also provides check pointing functionality like that of the checkpoint node but it also maintains its up-to-date in-memory copy of the file system namespace that is in sync with the active NameNode.


Q6. Give the differences between Hadoop and traditional RDBMS


Ans. Hadoop processes semi-structured and unstructured while RDBMS processes structured data.

Hadoop writes are fast while RDBMS reads are fast.


Also Read>> Reasons to Learn Python and Hadoop


Q7. Differentiate between active and passive NameNodes


Ans. Active NameNode works and runs in the cluster while Passive NameNode is a standby NameNode which has similar data as active NameNode.


Q8. Give a difference between NAS and HDFS


Ans. Network-attached storage (NAS) runs on a single machine, while HDFS runs on a cluster of machines.


Q9. Explain about the indexing process in HDFS.


Ans. Indexing process in HDFS depends on the block size. HDFS stores the last part of the data that further points to the address where the next part of data chunk is stored.


Q10. What is a checkpoint?


Ans. Checkpointing is a process that takes an FsImage, edit log and compacts them into a new FsImage.


Q11. What is a rack awareness?


Ans. The process of selecting closer data nodes depending on the rack information is known as Rack Awareness.


Q12. What is Replica Replacement Policy?


Ans. After consulting with the NameNode, client allocates 3 data nodes for each data block. For each data block, there exists 2 copies in one rack and the third copy is present in another rack. This is generally referred to as the Replica Placement Policy.


Q13. Can a NameNode exist without any data?


Ans. No, it should have some sort of data in it.


Q14. What does ‘jps’ command do?


Ans. The ‘jps’ command helps us to check if the Hadoop daemons are running or not.


Q15. How do you define “Rack Awareness” in Hadoop?


Ans. Rack Awareness is the algorithm in which the NameNode decides how blocks and their replicas are placed, based on rack definitions to minimize network traffic between DataNodes within the same rack.


Also Read>> Career Advantages of Hadoop Certification!


Q16. Explain about the partitioning, shuffle and sort phase


Ans. This process of moving the intermediate outputs of map tasks to the reducer is referred to as shuffle phase.

In sort phase, Hadoop MapReduce automatically sorts the set of intermediate keys on a single node before they are given as input to the reducer.

Partitioning phase is the process that determines which intermediate keys and value will be received by each reducer instance is referred to as partitioning. The destination partition is same for any key irrespective of the mapper instance that generated it.


Q17. What are the different operational commands in HBase at record level and table level?


Ans. Record Level Operational Commands in HBase are –put, get, increment, scan and delete.

Table Level Operational Commands in HBase are-describe, list, drop, disable and scan


Also Read>> Top Big Data Interview Questions & Answers


Q18. What is Row Key used for?


Ans. It is used for grouping cells logically.


Q19. What is a Combiner?


Ans. A Combiner is a mini reducer that performs the local reduce task.


Q20. What is MapReduce?


Ans. It is a framework/a programming model that is used for processing large data sets over a cluster of computers using parallel programming.



Q21. Explain WAL in HBase?


Ans. Write Ahead Log (WAL) is a file attached to every Region Server inside the distributed environment.


Q22. Differentiate between Sqoop and distCP.


Ans. DistCP utility can be used to transfer data between clusters whereas Sqoop can be used to transfer data only between Hadoop and RDBMS.


Q23. Does Flume provide 100% reliability to the data flow?


Ans. Yes, Apache Flume provides end to end reliability because of its transactional approach in data flow.


Q24. What is Hadoop streaming?


Ans. It is the process of writing Map and Reduce jobs in any desired programming language like Python, Perl, Ruby, etc.


Q25. What are the most commonly defined input formats in Hadoop?


Ans. Text Input Format, Key Value Input Format, and Sequence File Input Format.

These are some of the popular questions asked in a Hadoop interview. If you are someone who has recently started your career in big data, you can always get certified in Hadoop to get the techniques and skills required to be an expert in the field.

[course category=’Big Data & Hadoop’ total-course=’2’ ]

About the Author

Hasibuddin Ahmed

Hasibuddin Ahmed

Hasib is a professional writer associated with He has written a number of articles related to technology, marketing, and career on various blogs and websites. As an amateur career guru, he often imparts nuggets of knowledge related to leadership and motivation. He is also an avid reader and passionate about the beautiful game of football.