Top 50 Hadoop Interview Questions

04 Apr 2015

IT organizations from various domains are investing in big data technologies, increasing the demand for technically competent Hadoop developers. To build career as a Hadoop developer, one must be clear with Hadoop concepts and have a working knowledge of analysing data using MapReduce, Hive and Pig. Typical Hadoop interview questions include topics such as replication factor, node failures and distributed caching. If you are looking for frequently asked Hadoop Interview questions then you are at the right place. We have put together the list of top 50 Hadoop Interview Questions with the help of DeZyre’s Hadoop faculty that will help you get through your first Hadoop Interview. The most probably questions asked during Hadoop Interviews are covered in this list. If you would like to read more questions, check out our blog on Top 100 Hadoop Interview Questions and Answers

Gartner predicted that, “Big Data Movement will generate 4.4 million new IT jobs by end of 2015 and Hadoop will be in most advanced analytics products by 2015.” With the increasing demand for Hadoop for Big Data related issues, the prediction by Gartner is ringing true. 

The President of Dice.com, Mr. Shravan Goli said “The demand for Hadoop developers is up 34% from a year earlier, based on the number of jobs posted and Hadoop-related searches on the site.”

During March 2014, there were approximately 17,000 Hadoop Developer jobs advertised online. As of 4 ^th, April 2015 - there are about 50,000 job openings for Hadoop Developers across the world with close to 25,000 openings in the US alone. Of the 3000 Hadoop students that we have trained so far, the most popular blog article request was one on hadoop interview questions. 

On “The Hiring Scale” score chart where 1 denotes “Easy-to-fill” and 99 denotes “Hard-to-fill” positions, the role of a Hadoop Developer is ranked at 83 on an average in US which is slightly higher than the average IT jobs score which is 78. Three years ago only few companies were using Hadoop. Now, Hadoop technology is at the top in Big Data Analytics with an increasing user base.

A  Hadoop Developer is the person behind coding and programming of Hadoop applications in the Big Data domain. A Hadoop Developer should have strong understanding and hands-on experience of building, designing, installing and configuring Hadoop as he/she is the person responsible for Hadoop development and implementation which include implementation of MR jobs, preprocessing of Pig and Hive, loading of disparate data sets, database tuning and troubleshooting. With so many responsibilities to handle, it is not easy to get into the role of a Hadoop developer.

First impression is all it takes to impress recruiters. The best way to do this is by creating the most technically sound resume that can sell your Hadoop skills with viable examples of how you put those skills to use. A resume which does not address the need of the company that you are applying to – speaks volumes of your lack of knowledge of the industry and creates the impression that you will not be able to successfully utilize your skills. DeZyre's Hadoop faculty has outlined some tips for improving your technical resume:

“For the past couple of years, I have been training aspiring Big Data enthusiasts across the globe on the Hadoop Stack. A couple of really common questions that pop up midway into the course or close to the end are, "How do I tailor my resume to land a job?" or "I am learning this for the first time, how do I showcase the learning to be able to get a job?"

While these are really valid and critical questions, the answer is rather complex in today's IT scenario. There are several articles by eminent and experienced recruiters and hiring managers on what they like or dislike about resumes. These articles are quite comprehensive, and clearly define the aesthetic hygiene, the flow and the relevance of a resume. Therefore, I am not going to talk about how to write a resume and get noticed. Good resources to get that information, is from LinkedIn Pulse articles and careercup.com among other sources.

Big Data Hadoop Resume Tips

Apart from tailoring your resume, there are 4 steps which you must take if you are trying to get a job in emerging technology domains, including but not restricted to Big Data, Mobile Development, Cloud computing etc.

1. Carefully outline the roles and responsibilities:

The space of designation nomenclature has become really creative and innovative in the last few years. There is no way to generalize a Software Engineer or an ETL Architect in the industry today. Therefore it requires a bit of searching and introspection to zero in on the job profiles one wishes to apply for. Research and identify the roles and responsibilities and shortlist potential positions. The introspection part is needed to figure out if you have the necessary skills or the learning curve to take up the new role.

2. Make your resume highlight the required core skills

Every designation that you will come across on job portals will be searching for ‘Demi Gods’ amongst tech professionals. Multiple Programming Languages, Multiple Software Tools, Multiple Technology Platforms there is no end to the list. Identify the skills which you already have from the list of desired skills and highlight them on your profile. Try to figure out which are the most important skills for the role and make an attempt to learn about the skills.

3. Document each and every step of your efforts

This is possibly one of the most important areas where you should focus on. There are several online platforms which allow you to showcase your skills while you contribute and collaborate. Getting shortlisted for a job interview is much more than just because of your skills. Here is what I have seen work time and again for professionals in my network.

Active experimentation and blogging about the newly learned skills:

You could use "WordPress" or "Blogger" or send your blogs to manisha@dezyre.com and we will publish them. Add the blog links to your resume.

Answer questions on forums:

If you have figured out certain pieces of working with new technologies actively search and help answer questions on forums like "Stack Overflow" on the same topic.

Maintain code base and collaborate on GitHub:

Maintain all your experimental code on GitHub and contribute to projects that interest you. Get some friends to work on the project with you. Mention the GitHub project link on your resume.

4. Purposefully Network:

Be genuine and connect with people in the technical domain where you are trying to get into. Engage in meaningful conversations and share your work. Collect feedback and be open to assist and consult for free.”

Once you know that you have optimally restricted your resume to show up in recruiter’s search results, you now have to prepare in order to clear your technical interviews. As Hadoop grows and the bugs get eliminated to produce improved versions – we can see that Hadoop interview questions are maturing a good deal as well. There are several technical, scenario-based, complex and analytical Hadoop interview questions asked in Hadoop Developer job interviews which are unlike other technical interviews.

Tom Hart, vice president of Eliassen Group "If you really want to get a big data job, ideally, if you knew something about storing, retrieving and interpreting data, and something more about representing that information in a meaningful way with the use of dashboards and business intelligence tools, and you could convey your knowledge of both of these things in an interview with a hiring manager, your chances of employment would be materially enhanced.”

Enrol for Hadoop Online Training to join the Big Data Bandwagon!

Big Data Hadoop Interview Questions

Hadoop interviewers don’t bother with syntax questions or other simple hadoop interview questions that can be easily answered with the help of Google. You can answer the Hadoop interview questions if your basic concepts about the components are clear - as most of the Hadoop interview questions are based on the understanding of the concepts. Hadoop interview questions are generally based on the core components of Hadoop:

·         Hadoop basic interview questions

·         Hdfs interview questions

·         Hadoop YARN Interview questions

·         Hadoop MapReduce Interview Questions

With the help of our best in class Hadoop faculty, we have gathered top Hadoop developer interview questions that will help you get through your Hadoop Developer and admin job interviews.

Hadoop Developer Interview Questions

1) Explain how Hadoop is different from other parallel computing solutions.

2) What are the modes Hadoop can run in?

3) What will a Hadoop job do if developers try to run it with an output directory that is already present?

4) How can you debug your Hadoop code?

5) Did you ever built a production process in Hadoop? If yes, what was the process when your Hadoop job fails due to any reason? (Open Ended Question)

6) Give some examples of companies that are using Hadoop architecture extensively.

Hadoop Admin Interview Questions

7) If you want to analyze 100TB of data, what is the best architecture for that?

8) Explain about the functioning of Master Slave architecture in Hadoop?

9) What is distributed cache and what are its benefits?

10) What are the points to consider when moving from an Oracle database to Hadoop clusters? How would you decide the correct size and number of nodes in a Hadoop cluster?

11) How do you benchmark your Hadoop Cluster with Hadoop tools?

Hadoop Interview Questions on HDFS

12) Explain the major difference between an HDFS block and an InputSplit.

13) Does HDFS make block boundaries between records?

14) What is streaming access?

15) What do you mean by “Heartbeat” in HDFS?

16) If there are 10 HDFS blocks to be copied from one machine to another. However, the other machine can copy only 7.5 blocks, is there a possibility for the blocks to be broken down during the time of replication?

17) What is Speculative execution in Hadoop?

18) What is WebDAV in Hadoop?

19) What is fault tolerance in HDFS?

20) How are HDFS blocks replicated?

21) Which command is used to do a file system check in HDFS?

22) Explain about the different types of “writes” in HDFS.

Hadoop MapReduce Interview Questions

23) What is a NameNode and what is a DataNode?

24) What is Shuffling in MapReduce?

25) Why would a Hadoop developer develop a Map Reduce by disabling the reduce step?

26) What is the functionality of Task Tracker and Job Tracker in Hadoop? How many instances of a Task Tracker and Job Tracker can be run on a single Hadoop Cluster?

27) How does NameNode tackle DataNode failures?

28) What is InputFormat in Hadoop?

29) What is the purpose of RecordReader in Hadoop?

30) What is InputSplit in MapReduce?

31)In Hadoop, if custom partitioner is not defined then, how is data partitioned before it is sent to the reducer?

32) What is replication factor in Hadoop and what is default replication factor level Hadoop comes with?

33) What is SequenceFile in Hadoop and Explain its importance?

34) If you are the user of a  MapReduce framework, then what are the configuration parameters you need to specify?

35) Explain about the different parameters of the mapper and reducer functions.

36) How can you set random number of mappers and reducers for a Hadoop job?

37) How many Daemon processes run on a Hadoop System?

38) What happens if the number of reducers is 0?

39) What is meant by Map-side and Reduce-side join in Hadoop?

40) How can the NameNode be restarted?

41) Hadoop attains parallelism by isolating the tasks across various nodes; it is possible for some of the slow nodes to rate-limit the rest of the program and slows down the program. What method Hadoop provides to combat this?

42) What is the significance of conf.setMapper class?

43) What are combiners and when are these used in a MapReduce job?

44) How does a DataNode know the location of the NameNode in Hadoop cluster?

45) How can you check whether the NameNode is working or not?

Pig Interview Questions 

46) When doing a join in Hadoop, you notice that one reducer is running for a very long time. How will address this problem in Pig?

47) Are there any problems which can only be solved by MapReduce and cannot be solved by PIG? In which kind of scenarios MR jobs will be more useful than PIG?

48) Give an example scenario on the usage of counters.

Hive Interview Questions

49) Explain the difference between ORDER BY and SORT BY in Hive?

50)  Differentiate between HiveQL and SQL.

We would like to know about your experience in  Hadoop interviews. Please comment below to let us know if we missed any important question that is regularly asked in these interviews.