Data analytics and Big Data are the buzzwords for smart and effective data management these days, and it is here to stay in the future too. Organizations require professionals who are good at handling Big Data. From data analysts to data scientists, Big Data has created a range of job profiles and being a big data professional; you will be expected to be well versed with Hadoop. This write up lists some of the most popular Hadoop interview questions that cover the Hadoop Ecosystem Components.

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 is Hadoop?

Ans. Hadoop is an open-source software framework equipped with relevant tools and services to process and store Big Data. It is used to store the massive amount of data, handle virtually limitless concurrent tasks, and run applications on clusters of commodity hardware.


Q2. What are the primary components of Hadoop?


Data Access Components – HDFS, Hadoop MapReduce, Hadoop Common, and YARN

Data Storage Component – HBase

Data Management and Monitoring Components – Ambari, Oozie, and ZooKeeper

Data Serialization components – Thrift and Avro

Data Integration Components – Apache Flume, Sqoop, and Chukwa

Data Intelligence Components – Apache Mahout and Drill

Q3. What is Hadoop MapReduce?

Ans. Hadoop MapReduce is a framework used to process large data sets in parallel across a Hadoop cluster.


Q4. How does Hadoop MapReduce function?

Ans. When is MapReduce job is in progress, Hadoop sends the Map and Reduce tasks to the respective servers in the Hadoop cluster. The framework then aggregates all the data and manages all the related details of data passing, including task issues, task completion verification, and data copy.


Q5. How is 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.

Q6. 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 –

  • Massive data collection and storage
  • Data processing
  • Runs independently

Q7. What is HDFS and what are its 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.

Q8. What Is Apache Yarn?

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.

Q9. Name the main components of Apache Yarn.

ResourceManager and NodeManager are the two main components of YARN.

Also Read>> Reasons to Learn Python and Hadoop

Q10. 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.

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. 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

Q16. Name various Hadoop and YARN daemons.

Ans. Hadoop daemons –

  • NameNode
  • Datanode
  • Secondary NameNode

YARN daemons

  • ResourceManager
  • NodeManager
  • JobHistoryServer

Q17. 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.

Q18. 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)

Q19. 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.

Q20. 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.

Also Read>> Career Advantages of Hadoop Certification!

O21. What is a Mapper?

Mapper is the first code responsible for migrating or manipulating the HDFS block stored data into key and value pair. There is one mapper for every data block on HDFS.

O22. Mention the basic parameters of a Mapper.

A Mapper is –

  • LongWritable and Text
  • Text and IntWritable

Also Read>> Top Big Data Interview Questions & Answers

Q23. What is Hadoop streaming?

Hadoop Streaming is a generic API that enables a user to create and run Map/Reduce jobs with any executable or script or any programming language like Python, Perl, Ruby, etc. Spark is the latest tool for Hadoop streaming.

Q24. What is NAS?

NAS is the abbreviation for Network-Attached Storage (NAS). It is a file-level computer data storage server, which is connected to a computer network. It offers data access to a heterogeneous group.


Q24. What are the differences between NAS and HDFS?


Runs on a single machine Runs on a cluster of different machines
No probability of data redundancy Chances of data redundancy due to replication protocol
Stores data on a dedicated hardware Data blocks are distributed across local drives
Does not use Hadoop MapReduce Works with Hadoop MapReduce