Interview Questions

Big Data Interview Questions that you Can’t Miss to Prepare!

Read­ing Time: 6 min­utes

Data is empow­er­ing every­thing around us. It is the era of big data sci­ence and ana­lyt­ics and that is why the demand for skilled data pro­fes­sion­als has increased mas­sive­ly. There­fore, we are pro­vid­ing you with a list of most fre­quent­ly asked big data inter­view ques­tions and answers. So that you can pre­pare for the big data and be assured of success.

More and more com­pa­nies are inclined towards the use of big data in order to func­tion­al­ly run their oper­a­tions. From data ana­lysts, data sci­en­tists to Big Data Engi­neer, the posi­tions are numer­ous to choose from if you want to start a career in the field of big data.

Being well pre­pared for the big data inter­view ques­tions and answers will give you an edge over the oth­er appli­cants. This arti­cle has a list of ques­tions rang­ing from the most basic to the most advanced ones.

1. What do you understand by Big Data?

Big data is a col­lec­tion of com­plex unstruc­tured or semi-struc­tured large data sets. These sets help a com­pa­ny in gain­ing action­able insights. These insights help the busi­ness­es under­stand their work­ing in a deep­er sense. The data col­lect­ed ana­lyzes and uncov­ers pat­terns and infor­ma­tion which oth­er­wise might not be available.

2. Define the five V’s of Big Data?

The five V’s of big data are;

  • Vol­ume: It is the data vol­ume in Petabytes which gives infor­ma­tion about the amount of data which grows at a high rate.
  • Vari­ety: The data types curat­ed from Big Data pro­cess­ing are of var­i­ous kinds. For instance, data for­mats of text, audio and videos.
  • Veloc­i­ty: Veloc­i­ty is sim­ply the rate at which the data grows.
  • Verac­i­ty: High vol­ume of data often brings in incon­sis­ten­cy and incom­plete­ness. There­fore, veloc­i­ty indi­cates this uncer­tain­ty in data.
  • Val­ue: Busi­ness can also gen­er­ate rev­enue by con­vert­ing the accessed big data into values.

Know that this is one of the most com­mon ques­tions asked in the inter­view. How­ev­er it depends on you as to how you wish to answer the ques­tion, depend­ing upon the response of the inter­view­er. You can men­tion only the names if that is what asked. Or you could explain the five V’s fur­ther in detail if the recruiter is inter­est­ed in hear­ing from you further.

3. Mention some of the best tools used for Big Data?

Var­i­ous tools are used for the pur­pose of import­ing, sort­ing as well as ana­lyz­ing data. Some of these tools are;

  • Apache Spark
  • Apache Hive
  • Cas­san­dra
  • Apache Flume
  • Apache Pig
  • Mon­goDB
  • Apache Splunk
  • Apache Hadoop
  • MapRe­duce
  • Apache Sqoop

4. Explain how big data analysis is helpful in increasing business revenue?

With this answer, you can actu­al­ly explain to the recruiter as to why you think big data is impor­tant. The first and fore­most rea­son is that it explains the dif­fer­ences between the busi­ness­es and that is how they increase the rev­enue. Big data ana­lyt­ics also helps busi­ness­es ana­lyze the needs and pref­er­ences of the cus­tomers through big data solu­tions, on the basis of which they launch new products.

5. Define what is clustering?

Clus­ter­ing is the process of group­ing of sim­i­lar objects into sets which are known as clus­ters. Clus­ter­ing is an essen­tial part in data min­ing. It is also used in sta­tis­ti­cal data analy­sis. Some of the pop­u­lar clus­ter­ing meth­ods include par­ti­tion­ing, hier­ar­chi­cal, den­si­ty-based as well as mod­el based.

Also, objects clus­tered in one group are most like­ly dif­fer­ent than the objects clus­tered in anoth­er group.

6. How would you justify Big Data Analytics as important?

Big Data ana­lyt­ics has been use­ful and impor­tant for busi­ness­es because it helps busi­ness­es equip data. This equip­ment and data stor­age helps them to iden­ti­fy and not miss the new oppor­tu­ni­ties. Because of this fac­tor, busi­ness­es do not end up mak­ing absurd decisions.

More­over, busi­ness­es tend to make smarter deci­sions and moves. As a result, there are effi­cient oper­a­tions and high­er prof­its for the business.

7. Do you have any experience in Big Data Analytics? If so, then explain about it.

It is obvi­ous that this ques­tion would have no spe­cif­ic answer since it is an objec­tive answer. With these kind of big data inter­view ques­tions, the inter­view­er wants to hear from you about your expe­ri­ence. They also want to know about your work­ing tech­niques and whether you would be fit for the job role that you are inter­view­ing for or not.

Make sure to give a detailed answer to these kind of  big data inter­view ques­tions. Share all your past expe­ri­ences and also add sto­ries to your answer so that the answers sound inter­est­ing. Give details about all the major tasks that you under­went while at your pre­vi­ous job. And also state all the projects that you were a part of and made con­tri­bu­tions to.

But you need to be care­ful about the fact that you do not make your answer go over­board. This ques­tion is gen­er­al­ly asked dur­ing the start­ing of the inter­view itself. So you need to be very care­ful by answer­ing this one.

All of the oth­er answers that shall be asked to you in the inter­view will be based on the answer you give for this ques­tion. There­fore, do not just stick to one aspect of your pre­vi­ous experience.

8. Would you prefer good data or good models? Give reasons for your choice.

Most can­di­dates pre­fer to answer this ques­tion accord­ing to their expe­ri­ence. Just be sure to nev­er choose both options as your answer because this answer would lack prac­ti­cal­i­ty. It is hard to have both good data as well as mod­els in actuality.

If you answer the ques­tion from your expe­ri­ence, you will also have valid rea­sons to prove your choice of the answer. This way you would be able to give a detailed answer and not sound absurd.

9. How are Big Data and Hadoop related?

Undoubt­ed­ly, Hadoop and Big Data go hand in hand. The func­tion­ing of Hadoop depends on Big data. And the pro­cess­ing of Big Data is depen­dent on Hadoop. Basi­cal­ly, Hadoop is the gate­way for mod­el­ling all oth­er appli­ca­tions for Big Data.

10. Specify what are the essential Hadoop tools that are required for effective working of Big Data?

Hadoop has a num­ber of essen­tial tools that help in enhanc­ing the per­for­mance of big data. Ambari, “HBase, ZooKeep­er, Mahout, Flume, Hadoop Dis­trib­uted File Sys­tem, Sqoop, Pig are some of the examples.

11. Why do you think Hadoop is needed?

The main rea­son why Hadoop is need­ed is because it brings scal­a­bil­i­ty. It gets easy to build solu­tions for a spe­cif­ic amount of data. On the oth­er hand, get­ting solu­tions for increas­ing the amount of data is complex.

12. How do you think Apache Hadoop resolves the challenge of big data storage?

The strong file sys­tem of Hadoop, HDFS enables solv­ing all ends of the data stor­age. HDFS is stored as a bina­ry so it does not have any schema and is high­ly com­pressed in nature. In fact, the file sys­tem also main­tains redun­dan­cy. Due to this, there is data reli­a­bil­i­ty even in con­di­tions when the machine fails.

13. Mention the steps taken to deploy a Big Data Selection?

Deploy­ing Big Data Selec­tion com­pris­es of three steps;

  • Inges­tion of Data
  • Data Stor­age
  • Data Pro­cess­ing

14. What are the components of Hadoop?

The three major com­po­nents of Hadoop are:

HDFS — It is a java based dis­trib­uted file sys­tem. It is basi­cal­ly used for data stor­age. And it requires no pri­or organization.

MapRe­duce — It is a pro­gram­ming mod­el. MapRe­duce process­es large data sets in parallel.

YARN — Yarn is a frame­work that man­ages resources as well as han­dles requests from all the dis­trib­uted applications.

15. Define the various features of Hadoop.

This ques­tion is also one of the most asked big data inter­view ques­tions. The var­i­ous fea­tures of Hadoop are;

Open-Source: Open Source frame­works are inclu­sive of source codes. These source codes are avail­able as well as acces­si­ble all over the World Wide Web. These code snip­pets can also be rewrit­ten, edit­ed or mod­i­fied. This depends on the require­ments of the users and the analytics.

Scal­a­bil­i­ty: Hadoop runs on com­mod­i­ty hard­ware. But even then, addi­tion­al hard­ware resources can be added to new nodes.

User-Friend­ly: The user inter­face of Hadoop is very sim­ple. There­fore the frame­work of Hadoop is per­fect. Clients do not have to han­dle dis­trib­uted com­put­ing process­es any­more because the frame­work takes care of it.

Data Recov­ery: Hadoop splits blocks into three repli­cas across clus­ters, there­by allow­ing the recov­ery of data. It allows the users to recov­er data from node to node. The recov­ery is need­ed in cas­es of fail­ure. Hadoop recov­ers these tasks and nodes auto­mat­i­cal­ly in such circumstances.

Data Local­i­ty: Data Local­i­ty is the fea­ture of Hadoop which moves com­pu­ta­tion to data instead of mov­ing data to com­pu­ta­tion. Data is there­by moved to clus­ters instead of being brought to a loca­tion where­in MapRe­duce algo­rithms are processed as well as submitted.

16. What are Edge Nodes in Hadoop?

The gate­way nodes in Hadoop which act as the inter­face between the exter­nal net­work and the hadoop clus­ter are the Edge Nodes. The run­ning of client appli­ca­tions and clus­ter admin­is­tra­tion tools in Hadoop is done by Edge nodes. These are then used as stag­ing areas for data trans­fers to the Hadoop clusters.

The world of Big Data is extend­ing con­tin­u­ous­ly. And so are the job oppor­tu­ni­ties for big data pro­fes­sion­als. With this set of big data inter­view ques­tions and answers, you will have an idea about the kind of ques­tions that are asked. And also the kind of answers that you should be giv­ing while inter­view­ing for big data job profiles.

Good luck with your inter­view! If you are ful­ly pre­pared, there’s no stopping!


Sumedha is a Post Graduate in English. She has the penchant for creating a variety of content that is attention grabbing.

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