Big Data Hadoop Interview Questions and Answers

by GangBoard Admin, January 28, 2019

In case you’re searching for Big Data Hadoop Interview Questions and answers for Experienced or Freshers, you are at the correct place. There is parcel of chances from many presumed organizations on the planet. The Big Data Hadoop advertise is relied upon to develop to more than $5 billion by 2020, from just $180 million, as per Big Data Hadoop industry gauges. In this way, despite everything you have the chance to push forward in your vocation in Big Data Hadoop Development. Gangboard offers Advanced Big Data Hadoop Interview Questions and answers that assist you in splitting your Big Data Hadoop interview and procure dream vocation as Big Data Hadoop Developer.

Q1) What is Big Data.

Answer: Big Data is relative term. When Data can’t be handle using conventional systems like RDBMS because Data is generating with very high speed, it is known as Big Data.

Q2) Why Big Data?

Answer: Since Data is growing rapidly and RDBMS can’t control it, Big Data technologies came into picture.

Q3) What are 3 core dimension of Big Data.

Answer: Big Data have 3 core dimensions:

  • Volume
  • Variety
  • Velocity

Q4) Role of Volume in Big Data

Answer: Volume: Volume is nothing but amount of data. As Data is growing with high speed, a huge volume of data is getting generated every second.

Q5) Role of variety in Big Data

Answer: Variety: So many applications are running nowadays like mobile, mobile sensors etc. Each application is generating data in different variety.

Q6) Role of Velocity in Big Data

Answer: Velocity: This is speed of data getting generated. for example: Every minute, Instagram receives 46,740 new photos. So day by day speed of data generation is getting higher.

Q7) Remaining 2 less known dimension of Big Data

Answer: There are two more V’s of Big Data. Below are less known V’s:

  • Veracity
  • Value

Q8) Role of Veracity in Big Data

Answer: Veracity: Veracity is nothing but the accuracy of data. Big Data should have some accurate data in order to process it.

Q9) Role of Value in Big Data

Answer: Value: Big Data should contain some value to us. Junk Values/Data is not considered as real Big Data.

Q10) What is Hadoop?

Answer: Hadoop: Hadoop is a project of Apache. This is a framework which is open Source. Hadoop is use for storing Big data and then processing it.

Q11) Why Hadoop?

Answer: In order to process Big data, we need some framework. Hadoop is an open source framework which is owned by Apache organization. Hadoop is the basic requirement when we think about processing big data.

Q12) Connection between Hadoop and Big Data

Answer: Big Data will be processed using some framework. This framework is known as Hadoop.

Q13) Hadoop and Hadoop Ecosystem

Answer: Hadoop Ecosystem is nothing but a combination of various components. Below are the components which comes under Hadoop Ecosystem’s Umbrella:

  • HDFS
  • YARN
  •  MapReduce
  •  Pig
  • Hive
  • Sqoop, etc.

Q14) What is HDFS.

Answer: HDFS: HDFS is known as Hadoop Distributed File System. Like Every System have one file system in order to see/manage files stored, in the same way Hadoop is having HDFS which works in distributed manner.

Q15) Why HDFS?

Answer: HDFS is the core component of Hadoop Ecosystem. Since Hadoop is a distributed framework and HDFS is also distributed file system. It is very well compatible with Hadoop.

Q16) What is YARN

Answer: YARN: YARN is known as Yet Another Resource Manager. This is a project of Apache Hadoop.

Q17) Use of YARN.

Answer: YARN is use for managing resources. Jobs are scheduled using YARN in Apache Hadoop.

Q18) what is MapReduce.

Answer: MapReduce: MapReduce is a programming approach which consist of two steps: Map and Reduce. MapReduce is the core of Apache Hadoop.

Q19) Use of MapReduce.

Answer: MapReduce is a programming approach to process our data. MapReduce is use to process Big Data.

Q20) What is Pig.

Answer: This is a project of Apache. It is a platform using which huge datasets are analyzed. It runs on the top of MapReduce.

Q21) Use of Pig.

Answer: Pig is use for the purpose of analyzing huge datasets. Data flow are created using Pig in order to analyze data. Pig Latin language is use for this purpose.

Q22) What is Pig Latin

Answer: Pig Latin is a script language which is used in Apache Pig to create Data flow in order to analyze data.

Q23) What is Hive.

Answer: Hive is a project of Apache Hadoop. Hive is a dataware software which runs on the top of Hadoop.

Q24) Use of Hive.

Answer: Hive works as a storage layer which is used to store structured data. This is very useful and convenient tool for SQL user as Hive use HQL.

Q25) What is HQL.

Answer: HQL is an abbreviation of Hive Query Language. This is designed for those user who are very comfortable with SQL. HQL is use to query structured data into hive.

Q26) What is Sqoop.

Answer: Sqoop is a short form of SQL to Hadoop. This is basically a command line tool to transfer data between Hadoop and SQL and vice-versa.

Q27) Use of Sqoop.

Answer: Sqoop is a CLI tool which is used to migrate data between RDBMS to Hadoop and vice-versa.

Q28) What are other components of Hadoop Ecosystem.

Answer: Below are other components of Hadoop Ecosystem:

  1. a) HBase
  2. b) Oozie
  3. c) Zookeeper
  4. d) Flume etc.

Q29) Difference Between Hadoop and HDFS

Answer: Hadoop is a framework while HDFS is a file system which works on the top of Hadoop.

Q30) How to access HDFS

Answer: below is command:

hdfs fs or hdfs dfs

Q31) How to create directory in HDFS

Answer: below is command:

hdfs fs -mkdir <dir_name>

Q32) How to keep files in HDFS

Answer: below is command:

hdfs fs -put <source_file_path> <destination_file_path>


hdfs fs -copyfromLocal <source_file_path> <destination_file_path>

Q33) How to copy file from HDFS to local.

Answer: below is command:

hdfs fs -copyToLocal <source_file_path> <destination_file_path>

Q34) How to Delete directory from HDFS.

Answer: below is command:

hdfs fs -rm <dir_name>

Q35) How to Delete file from HDFS.

Answer: below is command:

hdfs fs -rm <file_name>

Q36) How to Delete directory and files recursively from HDFS.

Answer: below is command:

hdfs fs -rm -r <file_path>

Q37) How to read file in HDFS.

Answer: below is command:

hdfs fs -cat <file_path>

Q38) What are the other file system available in market.

Answer: FAT, NAS, EXT are the well-known file systems available in market.

Q39) What are basic steps to be performed while working with big data.

Answer: below are the basic steps to be done while working with Big Data:

  • Data Ingestion
  • Data Storage
  • Data Processing

Q40) What is data ingestion.

Answer: Before Big Data came into Picture, our data used to reside into RDBMS. Data Ingestion is a process to move/ingest your data from one place to another place. In the reference of Big Data, Data movement from RDBMS to Hadoop is known as Data Ingestion.

Q41) Explain data storage.

Answer: This steps comes into picture after Data Ingestion. Ingested data is stored into different storage layers like: HDFS, Hive tables etc.

Q42) What is data processing in big data.

Answer: Data Processing: Once you have data in HDFS, Data is processed for different purpose. Data can be processed using MapReduce, Hive tables etc.

Q43) What is unstructured data.

Answer: There are a huge number of source available which generate different types of data. Some Sources generate data which can’t be stored into tables i.e. that data is not in tabular form. Such data is known as Unstructured Data.

Q44) What are the storage layer available to store unstructured data.

Answer: HBase, Cassandra, MongoDB are well known storage layers available in market to store unstructured data.

Q45) What are the most important qualities of Hadoop.

Answer: below are most of the well-known and useful features of Hadoop:

  • Open Source
  • Distributed Processing
  • Fault tolerant
  • High available
  • Commodity Hardware

Q46) What do you mean by Open Source.

Answer: Hadoop is a framework given by Apache. Frameworks which are available for free of cost are known as Open Source.

Q47) What do you mean by Distributed processing.

Answer: Data stored in Hadoop is distributed across clusters in order to give better performance and to make data highly available.

Q48) What is Fault tolerance in Hadoop.

Answer: Since Data is Highly available in Hadoop. There is very minimum or no chance to lose data as each data is replicated 3 times by default. So Hadoop is known as Highly Fault tolerant framework.

Q49) What is High availability in Hadoop.

Answer: Hadoop stores all data 3 times i.e. it makes 3 copy of each data. This number can be change. By doing this, Hadoop makes data highly available as there is no chance to lose data. If data will not be available on any node, Hadoop will bring data from other node and will provide to client.

Q50) What is replication factor in Hadoop.

Answer: Replication factor is the term which is used to decide the number by which each data will be replicated into Hadoop. User can change replication factor according to their need. By default, its value is 3.

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