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Top Hadoop Interview Questions to Practice for Your Tech Interview

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Hadoop is an open-source big-data framework with YARN, HDFS, and MapReduce as the three components. Many companies and organizations use Hadoop globally, including Twitter, Netflix, Uber, and the NSA of the United States. 

It helps to know what kind of Hadoop interview questions to expect in order to better understand what interviewers are looking for. This article focuses on Hadoop interview questions so you can gauge your preparation levels.

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In this article, we’ll cover:

  • Hadoop Interview Questions and Answers
  • Sample Hadoop Interview Questions for Practice
  • Hadoop Interview Questions for Experienced Professionals
  • Hadoop Data Engineer Interview Questions
  • FAQs on Hadoop Interview Questions

Hadoop Interview Questions and Answers

We’ll begin with some sample Hadoop interview questions and answers to get a basic idea of what to expect:

Q1. What is HDFS?

HDFS stands for Hadoop Distributed File System. It serves as the storage unit of Hadoop and follows master and slave topology. It helps us take different types of data as blocks and store them in a distributed environment.

Q2. Name the three modes in which Hadoop can run.

The three modes are:

  • Standalone mode: It’s the default mode. It uses the local FileSystem and a single Java process.
  • Pseudo-distributed mode: It uses a single-node Hadoop deployment
  • Fully-distributed mode: It uses separate nodes.

Q3. Name the critical YARN components.

Resource manager, Node manager, container, and application master are some important YARN components.

Q4. What is YARN?

YARN stands for Yet Another Resource Negotiator and serves as the processing framework in Hadoop. It helps by managing resources and providing an execution environment for the processes.

Q5. How do “reducers” communicate with each other? 

A trick question. MapReduce prohibits reducers from communicating with each other. So reducers can’t communicate with each other and run in isolation.

Sample Hadoop Interview Questions for Practice

Now, here are some sample Hadoop interview questions. See if you can solve them:

  1. Name the challenges involved with Big Data.
  2. Why use Hadoop for Big Data?
  3. Explain the core components of Hadoop.
  4. Explain the three modes in which Hadoop runs.
  5. Explain the HDFS architecture.
  6. Differentiate between a federation and high availability.
  7. What are some limitations of Hadoop?
  8. What is the architecture of HDFS?
  9. What is a heartbeat in HDFS?
  10. Explain the five Vs of Big Data.
  11. Explain the combiner in MapReduce.
  12. How does rack awareness work in HDFS?
  13. What is a block and block scanner?
  14. Explain the resource manager in YARN.
  15. Name the different Hadoop daemons and their roles in a Hadoop cluster.
  16. What is speculative execution in Hadoop? 

Hadoop Interview Questions for Experienced Professionals

Let’s move a step further with some Hadoop interview questions for experienced professionals:

  1. What are the fundamental differences between a relational database and HDFS?
  2. Differentiate between HDFS and regular FileSystem.
  3. Differentiate between HBase and Hive.
  4. State the advantages of using YARN in Hadoop.
  5. Was YARN originally launched to be a replacement for MapReduce?
  6. Talk about the different scheduling policies you can use in YARN.
  7. Differentiate between HDFS and Network Attached Storage (NAS).
  8. Differentiate between Hadoop 1 and Hadoop 2.
  9. Explain active and passive “NameNodes”? 
  10. How is HDFS fault-tolerant? 
  11. Differentiate between an HDFS Block and an Input Split.
  12. Explain the Hadoop-specific data types used in MapReduce?
  13. Why use HDFS for applications having massive data sets only, but not when there are many small files? 
  14. What are the three core methods of a reducer?
  15. Explain the different output formats in MapReduce?
  16. Why is MapReduce slower compared to other processing frameworks in processing data?

Hadoop Data Engineer Interview Questions

Lastly, here are some Hadoop data engineer interview questions. Ensure you can solve them before your interview:

  1. Explain indexing. How is indexing done in HDFS?
  2. Explain the process of storing data in a rack.
  3. What is the port number for NameNode?
  4. Why can’t we perform aggregation/addition in the mapper? What’s the need for the reducer for this?
  5. What happens if we store too many small files in a cluster on HDFS?
  6. Name the command to find the status of blocks and FileSystem health.
  7. How will NameNode tackle DataNode failures?
  8. What does the jps command do? 
  9. Name the main configuration parameters in a MapReduce program.
  10. Why frequently remove or add nodes in a Hadoop cluster?
  11. What will you do when NameNode is down?
  12. How to copy data from the local system onto HDFS? 
  13. Can NameNode and DataNode be commodity hardware? 
  14. What’s the result if two clients try to access the same file in the HDFS?
  15. How would you restart NameNode or all the daemons in Hadoop? 

FAQs on Hadoop Interview Questions

Q1. What are the three main components of Hadoop?

The three components of Hadoop are Hadoop HDFS (storage unit), Hadoop MapReduce (processing unit), and Hadoop YARN (resource management unit).

Q2. Name the three modes in which Hadoop can run.

The three modes Hadoop can run in are Standalone mode, Pseudo-distributed mode, and Fully-distributed mode.

Q3. What is Hadoop used for?

Apache Hadoop is used for the efficient storage and processing of large datasets. The datasets are massive; their size ranges from gigabytes to petabytes. Hadoop enables the clustering of multiple computers to analyze massive datasets faster in parallel instead of using one large computer for storage and processing.

Q4. Name the different Hadoop configuration files.

Hadoop configuration files include hadoop-env.sh, mapred-site.xml, core-site.xml, Master, Slaves, yarn-site.xml, and hdfs-site.xml.

Q5. What are some key characteristics of Hadoop?

Hadoop is open-source, cost-effective, and faster in data processing. Based on the data locality concept, it provides fault tolerance, high availability, and feasibility. Hadoop cluster is also highly scalable.

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