Big Data Hadoop interview questions have grown increasingly prevalent as companies seek to use analytics from Big Data platforms for competitive advantage.

Big Data Hadoop is an open-source framework designed to store, process, and analyze large volumes of structured and unstructured data within a distributed computing environment.

We will explore some key interview questions related to Big Data Hadoophere in this blog post.

No matter where your expertise lies in Big Data Hadoop, this blog can offer valuable insight and tips that will assist in the preparation for an interview. So, let’s dive right in!

1.What is Big Data?

Big Data refers to large data sets that are too large for traditional computers to store andprocess.

2.What is Big Data Hadoop?

Big Data Hadoop is a technology developed by Apache Software Foundation to handle the problems faced by Big Data, storing and processing large amounts of data on cheap, commodity hardware in a distributed manner.

3.What are the components of Big Data Hadoop?

The main components of Big Data Hadoop include HDFS, a Big Data Hadoop distributed file system, MapReduce, a processing layer, and YARN, a Resource Management Layer.

4.What is HDFS?

HDFS is a storage layer for Big Data Hadoop and has a master slave architecture, allowing organizations to store, capture, and analyze Big Data.

where data files are divided into multiple blocks with a size of 128MB or 256MB by default.

5.What is the name node in Big Data Hadoop?

The name node contains all metadata such as name, path, replicas, and block size, while the data node does all the groundwork, creating, replicating, and deleting blocks on the command order.

6.What is YARN in Big Data Hadoop?

YARN, short for Yet Another Resource Negotiator, is a third core component of Big Data Hadoop, consisting of a Resource Manager and Node Manager.

7.What is the use case of Big Data Hadoop?

Big Data Hadoop is a popular technology for handling Big Data problems, storing and processing large amounts of data on cheap, commodity hardware, and providing data analytics through distributed computing.

8.What is the purpose of a partitioner in Big Data Hadoop?

The partitioner is a load balancer that determines the number of reducers required for a given use case.

9.How does a partitioner work?

The partitioner uses a key-dot hash code generated by a JVM Java Virtual Machine to determine the number of reducers. The hash code is divided by the number of reducers, ensuring that the number of reducers is determined.

10.What is the difference between a combiner and a partitioner in Big Data Hadoop?

The combiner and partitioner work together to consolidate the entire map reduce for a given record. A combiner is used to combine something before load balancing.

11.What is the role of the resource manager in Big Data Hadoop?

The resource manager requests two containers for reducers, which can be given on any machine due to traffic.

12.What is the difference between Unix file systems and Big Data Hadoop file systems?

The Linux file system is different from the Big Data Hadoop file system, as it allows for different prefixes to determine whether a command is a Linux command or a Big Data Hadoop command.

13.What is the purpose of the job history server in Big Data Hadoop?

The job history server is used to archive job status, which can be used to verify the functionality of services and ensure that all Java processes are running properly.

14.How do you practice HDFS commands and MapReduce programs in Big Data Hadoop?

It is important to understand the difference between Unix file systems and Big Data Hadoop file systems and practice using HDFS commands. When running MapReduce programs, users will likely practice these commands

15.What is MapReduce?

MapReduce is a powerful data processing tool that enables users to process and analyze large datasets. It consists of three files – driver, mapper, and reducer – that can be chained together in a workflow.

16. What are the four code samples for MapReduce?

The four code samples for MapReduce are a word count program, aggregation, joints, and a Haruk ecosystem example.

17.What is the difference between Eclipse and Ant for creating MapReduce programs?

Eclipse and Ant are two methods for creating MapReduce programs. Eclipse involves unzipping the code from LMS, placing it in the word count folder, and opening the workspace.

Ant, on the other hand, requires installing Ant and then copying the code to a new location and running the command ant.

 18.What is the purpose of the mapper and reducer in a MapReduce program?

The mapper is a smart tool that works on one record at a time and tokenizes the value into a string. It then iterates through iterations and emits word commas.

The reducer takes one bucket at a time and takes Bigcommas one, one, one, one, one.

The key is text and is iterable on integers, starting some from zero. The output is a Bigcomma four, representing HDFS, DFS, CAT, and output slash parts.

19. What is the difference between the Haruk ecosystem and the Big Data Hadoop ecosystem?

The Haruk ecosystem is a collection of applications designed to solve specific problems in the field of data management.

It includes various ecosystem projects such as Scoop, Flume, Uzi, Pig, Hive, Impala, and Spark.

The Big Data Hadoop ecosystem, on the other hand, is a broader set of applications and tools that are used to process and analyze large datasets using MapReduce and other technologies.

20.What is Scoop and how is it used in the Big Data Hadoop ecosystem?

Scoop is a project that is used to transfer data from RDBMS to Big Data Hadoop.

It is used in the Big Data Hadoop ecosystem to enable users to retrieve data from various sources and process it using MapReduce, Hive, and other tools.

21.What is the focus of this introduction to Hive?

The focus of this introduction to Hive is on how to write SQL queries on the same data, such as HDFS, DFS, L, and user logs, using Hive.

22.What is the difference between Pig and Hive?

Pig is a more expressive and efficient alternative to Hive, offering more flexibility and flexibility in data management. It is recommended to use Pig for tasks that require more complex operations and data management.

23.Where is the data stored for the Pig programs?

The data is stored in a high-speed Big Data Hadoop warehouse, which allows for connections to high tables.

24.What is a successful data transfer from MySQL to Big Data Hadoop?

The successful data transfer from MySQL to Big Data Hadoopfocuses on the path and number of rows in the MySQL table.

25.What are the five V’s of Big Data?

The five V’s of Big Data include volume, velocity, variety, value, and veracity.

26.What are the benefits of using Big Data?

The benefits of using Big Data are numerous and include gaining insights and finding hidden information, handling large volumes of data, collecting and analyzing data, and drawing insights for business decisions.

 27.What is the main challenge of storing Big Data?

The main challenge of storing Big Data is the vast volume of data generated daily, which could be viral or have a lot of value. Processing this massive volume of data would take a long time, and encryption of Big Data is difficult.

28.What is the role of Hadoop in Big Data?

Big Data Hadoop is an open-source framework for storing and processing large amounts of data on commodity machines, without the need for carrier-class hardware.

It has two main components: a storage layer called HDFS and a processing layer called yarn.

Big Data Hadoop Map Reduce is the oldest and most mature processing framework, derived from GFS, which solves the issue of storing large amounts of data in a distributed fashion.

29.What are the use cases for Big Data Hadoop in Big Data?

Big Data Hadoop is used in various use cases such as handling large volumes of data, collecting and analyzing data, and drawing insights for business decisions.

30.What is the difference between RDBMS and Big Data Hadoop?

RDBMS has only 10-20% of data online and the rest is archived, while Big Data Hadoop allows organizations to work on all the data.

31.What are some examples of industries using Big Data?

Big Data is used in various industries such as healthcare, pharmaceuticals, transportation, retail, IT,

32.What is the logical storage layer for a Big Data Hadoop cluster?

The logical storage layer for a Big Data Hadoop cluster is the storage layer, which is responsible for storing large volumes of data on commodity machines.

33.What is fault-tolerant, reliable, and scalable?

Fault-tolerant, reliable, and scalable are properties of the Big Data Big Data,Big Data Hadoop distributed file system (HDFS)

that allow for access to data across multiple Big Data Big Data,Big Data Hadoop clusters.

34.What are data nodes and node managers in a Big Data Hadoop cluster?

Data nodes are responsible for storing data blocks, while node managers are responsible for managing the data nodes in a Big Data Hadoop cluster.

35.What is replication in HDFS?

Replication is a process in HDFS that ensures that data is stored across multiple machines in a distributed fashion.

36.What is the minimum number of resource managers and node managers required for HDFS to function properly?

For HDFS to function properly, a minimum of one resource manager and multiple node managers are needed.

37.What is the Big Data Hadoop cluster management system?

The Big Data Hadoop cluster management system is a multi-tiered system that includes the storage layer and the processing layer.

38.What is the cloud-era manager in the Hortonworks data platform?

The cloud-era manager in the Hortonworks data platform automatically handles downloading Big Data Hadoop-related packages, editing config files, formatting HDFS, and starting the cluster.

39.What are some storage managers used in Big Data Hadoop?

Some storage managers used in Big Data Hadoop include HDFS, HBase, and Solar.

40.What are processing frameworks used for Big Data Hadoop?

There are various processing frameworks available for Big Data Hadoop, including Apache projects like Hive, Big Spark, Cascading Crunch, Drill, Impala, and Presto.

By reviewing these Big Data Hadoop interview questions,Weexplored key concepts and technologies used in Big DataBig Data Hadoop, such as MapReduce, HDFS, YARN, Hive, and Pig and Spark.

by developing an in-depth knowledge of Big Data Hadoop technologies, you can prepare for your upcoming Big Data Hadoopinterviewshelping ensure success in this exciting, rapidly expanding field