10 Hadoop Interview Questions and Answers

Written by Coursera Staff • Updated on

Learn more about common Hadoop interview questions and how to answer them, as well as some useful tips to help you prepare for your interview.

[Featured Images] A happy woman standing up in an office at a table shaking the hand of another woman who has finished asking Hadoop interview questions with a male coworker.

Hadoop is an open source framework for processing, sharing, and storing big data. It enables you to split data storage and processing among several computers by using cluster servers rather than relying on a single device. This ultimately allows you to process large amounts of data more efficiently, leading to faster implementation. 

Big data plays an important role in several notable machine learning use cases. For example, streaming services can make personalized recommendations for what you should watch next using recommendation engines powered by machine learning and big data. Predictive analytics is another field benefiting from the relationship between machine learning and big data. Businesses can assess the likelihood of possible outcomes by analyzing massive amounts of historical data and combining it with machine learning algorithms. 

While your interviewer will likely ask you various questions at your job interview, it should be helpful to review the frequently asked Hadoop interview questions in this article and practice your answers so you can walk into your next interview feeling comfortable and confident.

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Who uses Hadoop?

If you’re wondering whether or not you will encounter Hadoop-related questions during your next interview, some job titles associated with big data that may require your knowledge of Hadoop include:

  • Data modeler

  • Data scientist

  • Big data engineer

  • Data architect

  • Database developer

  • Database security analyst

Thousands of companies around the world utilize Hadoop for their big data needs, and here are some well-known companies that use Hadoop as part of their tech stack:

  • Google

  • Amazon

  • Spotify

  • LinkedIn

  • Hulu

  • JPMorgan Chase & Co.

10 Hadoop interview questions and answers

Here’s a look at 10 potential Hadoop interview questions you could face and some tips for providing quality answers.

1. What are the different modes where Hadoop can run?

What they’re really asking: Do you know the three different modes and when to use each?

You can use Hadoop in different modes: fully distributed, standalone, and pseudo-distributed.

When answering this question, provide examples as to when you should use each and the characteristics that differentiate them. 

Other forms this question might take:

  • Can you name the three Hadoop modes?

  • How do you determine which Hadoop mode to use?

2. What is HDFS?

What they’re really asking: Are you familiar with Hadoop storage systems?

Hadoop Distributed File System, or HDFS, is a storage system for large data sets that allows access to your application data. It is Hadoop’s primary storage system.

In your answer, explain some specific features that HDFS enables, such as fault detection and storage capabilities.

Other forms this question might take:

  • Explain the HDFS architecture.

  • What are the benefits of HDFS?

3. What is MapReduce?

What they’re really asking: Do you know how to build highly scalable data solutions?

MapReduce is a framework that simplifies distributed programming in Hadoop. This leads to increased scalability and higher processing speeds, and it provides easy access to data from different sources.

When answering this question, be able to explain the three-step process that MapReduce goes through when preparing data.

Other forms this question might take:

  • How does MapReduce work?

  • What programming languages are compatible with MapReduce?

4. What is YARN?

What they’re really asking: What do you know about resource management in Hadoop?

Short for Yet Another Resource Negotiator, YARN allocates resources and creates job schedules in Hadoop, expanding on MapReduce capabilities.

Explaining the relationship between HDFS, MapReduce, and YARN in your answer can further demonstrate your knowledge.

Other forms this question might take:

  • What are the differences between YARN and MapReduce?

  • Describe YARN architecture.

5. Explain the characteristics of big data.

What they’re really asking: Do you understand what separates big data from typical data?

Knowing the characteristics of big data is important to fully comprehend the challenges and possible advantages of working with immense volumes of data.

You can describe big data through distinct characteristics known as the five Vs: volume, variety, velocity, veracity, and value. 

Other forms this question might take:

  • What makes big data different from other data?

  • What are the challenges that come with handling big data?

6. Why use Hadoop for big data?

What they’re really asking: Do you understand the relationship between Hadoop and big data?

Hadoop allows you to store and process different data types in massive quantities within a highly scalable and affordable framework. With Hadoop, you have a place to store your data without having to process it first.

In your answer, you can display your knowledge by also discussing some of the challenges associated with using Hadoop.

Other forms this question might take:

  • What are the pros and cons of using Hadoop for big data?

  • How does Hadoop work?

7. What is JobTracker?

What they’re really asking: Do you understand the role JobTracker plays in MapReduce?

JobTracker assigns MapReduce tasks to different nodes, tracks resource assignments, and identifies which available resources are best suited for a given task.  

When answering this question, discussing the relationship between JobTracker and TaskTracker can be helpful.

Other forms this question might take:

  • How does JobTracker work?

  • Explain the relationship between JobTracker and TaskTracker.

8. What are the different InputFormats in Hadoop?

What they’re really asking: Do you understand how to select the right InputFormat?

Three common types of InputFormats are KeyValueTextInputFormat, TextInputFormat, and SequenceFileInputFormat. 

If your interviewer asks this question, prepare to discuss the advantages of each, as well as when to use different InputFormats.

Other forms this question might take:

  •  How do you determine which InputFormat to use?

  • Describe the use cases for different InputFormats.

9. How does data replication occur in Hadoop?

What they’re really asking: Do you know how to prevent data loss?

Through HDFS, Hadoop can replicate data automatically, allowing for high data availability in addition to helping mitigate data loss and preventing node failure. 

To take your answer one step further, be able to explain how HDFS recovers from data loss.

Other forms this question might take:

  •  What is data replication?

  •  Why is data replication important?

10. What is speculative execution?

What they’re really asking: Do you understand how to handle high-volume workloads in Hadoop?

Speculative execution allows you to address a slow DataNode by transferring the operation to a different node. This enables the system to work more efficiently in high-workload cases.

When answering this question, you can go on to explain the process of enabling speculative execution to demonstrate your knowledge.

Other forms this question might take:

  • How does speculative execution work?

  • How do you enable speculative execution?

Tips to prepare for your interview

In addition to practicing potential questions and answers, use these tips to prepare adequately for your interview.

Before your job interview, take some time to research the company you are interviewing with. This will allow you to learn more specific details about the position and help you prepare answers on how your skills can help them succeed. 

It’s also a good idea to come to the interview with a few questions to ask the interviewer. Not only does this show your interest in the position and your desire to learn more, but it will also help you determine if this position is a good fit for you.

Getting started with Coursera

On Coursera, you can find highly rated courses to learn more about big data. The Big Data Specialization from the University of California, San Diego, can help you understand how to create value from large data sets and use Hadoop and other tools for working with big data.

Earning the IBM Data Engineering Professional Certificate is another great way to develop skills in Hadoop, Python, SQL, and more in order to transform data into usable information.

Updated on
Written by:
Coursera Staff

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