30 Hadoop Questions for Interview With Answers

If you are looking to learn how to use Hadoop, there are a few questions that you should ask in an interview. These quality Hadoop Questions for Interviews will give you a good idea of what the platform can do for your data science projects. In addition, Hadoop is well known for its high speed and ability to scale. Some of the Hadoop Questions for Interview With Answers might ask are: 

Question 01: What is Hadoop?

Answers: Hadoop is an open-source framework from Apache that stores and processes large data sets in a distributed computing environment. Hadoop, also known as the Apache Hadoop Distributed File System, is a powerful data management platform for large-scale data analysis and machine learning. It is free and open-source software.

Question 02: Why is Hadoop used for big data?

Answers: Hadoop is used for big data because it is an open-source framework that can process and store large amounts of data. Hadoop is designed to be scalable and can be used on commodity hardware.

Question 03: What is Hadoop used for?

Answers: Hadoop is an open-source distributed processing framework that can be used for various big data applications. Hadoop is a potent data management stage for different purposes, such as data analysis, sharing, and machine learning.

Question 04: What is HDFS architecture in Hadoop?

Answers: The HDFS is a robust distributed file system designed to drive on commodity hardware. It has an enslaver/slave architecture. A cluster consists of a single enslaver and multiple enslaved people. The clients are responsible for managing the file system namespace and regulating access to files. The enslaved people are responsible for storing and replicating the data.

Question 05: What is a hive in Hadoop?

Answers: Hive is a data one kind of warehouse system for Hadoop that gives an advantage to easy data summarization, ad-hoc queries, and the deep complete analysis of large complete datasets stored in Hadoop files. It effectively serves as a mechanism to program structure onto this valuable data and query it using SQL.

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Question 06: Is Hadoop a database?

Answers: No, Hadoop is not a database. Hadoop is a robust framework for the distributed complete storage and processing of big data sets.

Question 07: Is Hadoop an ETL tool?

Answers: No, Hadoop is not an ETL tool. Hadoop is a framework that can process large amounts of data.

Question 08: What is NameNode in Hadoop?

Answers: The NameNode is the center pinpoint of an HDFS file system. It is in the directory tree of all files in the file technique and tracks where the file data is kept broadwise the cluster. It does not store the data of those files itself.

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Question 09: What are the different types of Hadoop?

Answers: Hadoop is a platform that helps organizations process large amounts of data. It has different types of Hadoop, which can be used to manage data better.

The different types of Hadoop are: 

  • Apache Hadoop
  • Cloudera Hadoop
  • Hortonworks Hadoop
  • Amazon EMR
  • MapR

Question 10: What are data nodes? – Hadoop Questions for Interview

Answers: Data nodes are machines in a Hadoop cluster that store and process data. A data node is a facility in a network that permits the exchange of data between nodes. The nodes in a network can be computers, printers, or other devices. Data nodes can exchange information about the network’s state or transfer data between computers.

Question 11: What are the components of Hadoop?

Answers: The components of Hadoop are the Hadoop Distributed File technique, Hadoop YARN, and Hadoop MapReduce.

Question 12: How many modes can Hadoop run?

Answers: Hadoop can successfully run in pseudo-distributed, standalone, and fully distributed modes.

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Question 13: What is the limitation of Hadoop?

Answers: The main limitation of Hadoop is that it is not suitable for real-time processing. It is also not easy to use for interactive queries.

Question 14: What are the features of Hadoop?

Answers: Hadoop, an open-source platform for data analysis and storage, is used by organizations of all sizes to store and analyze their data. It offers many features that make it an attractive choice for large-scale data analysis and storage.

Some of the key features of Hadoop are as follows:

  • Scalability: Hadoop is highly scalable as it can scale from a single server to thousands of servers. There is no limit to the amount of data stored in a Hadoop cluster.
  • Fault Tolerance: Hadoop is designed to be fault tolerant. If a node in a Hadoop cluster goes down, the system automatically detects the failure and re-replicates the data on another node. 
  • High Availability: Hadoop is designed to provide high availability of data. In a Hadoop cluster, the data is automatically replicated on another node if a node goes down. 
  • Ease of Use: Hadoop is easy to use as it has a simple programming model.
  • Cost Effective: Hadoop is a cost-effective solution as it is open source and has a low cost of ownership.

Question 15: What are the properties of Hadoop?

Answers: Hadoop is a scalable, fault-tolerant, distributed computing platform. HDFS, Hive, and HBase are the most popular workhorse File Systems used in big data applications. Hadoop is a cutting-edge open source platform for distributed data management that lets you take advantage of its vast storage capacity and processing power to process large data sets.

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Question 16: Is Hadoop Java-based?

Answers: Hadoop is based on the Java programming language.

Question 17: What are the Hadoop daemons, and explain their roles in a Hadoop cluster?

Answers: Hadoop daemons manage the cluster’s services, perform data analysis, and verify that data is stored and processed appropriately. There are Some Hadoop daemons: 

  • NameNode: Maintains the master copy of the file system metadata.
  • Secondary NameNode: Maintains a backup copy of the file system metadata. *DataNode: Stores actual data blocks of files in the file system. 
  • JobTracker: Manages MapReduce jobs and distributes tasks to available TaskTracker nodes in the cluster.
  • TaskTracker: Runs MapReduce tasks assigned by the JobTracker.

Question 18: What is Avro Serialization in Hadoop?

Answers: Avro is a data serialization system that uses a schema to define data structure. Avro serialization converts data into a binary format that Hadoop can read.

Question 19: List the various HDFS Commands.

Answers:  HDFS is a distributed file system that enables users to store, search, and analyze data. It is well-suited for large-scale data gathering and provides many features unavailable with traditional file systems. HDFS commands are available to help users access and manipulate data. There are some HDFS commands:

  • Hadoop fs -ls
  • Hadoop fs -mkdir
  • Hadoop fs -copy from local
  • Hadoop fs -copyToLocal
  • Hadoop fs -rm 
  • Hadoop fs -rmdir 
  • Hadoop fs -put
  • Hadoop fs -get

Question 20: What is the purpose of the admin tool?

Answers: The purpose of the admin tool is to help manage your website. It provides a central location to add, edit, and delete content. It also helps you keep track of your website’s statistics and performance.

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Question 21: What is a Hadoop counter?

Answers: A Hadoop counter is a data structure used to store the counts of various events that occur during the execution of a Hadoop job. Counters are useful for monitoring a Hadoop job’s progress and debugging issues that may occur during the execution of the job.

Question 22: What are the basic parameters of Mapper?

Answers: Mappers are used for data entry and analysis in many industries. They are valuable for their ability to map data consistently and efficiently. Mappers can be used for various purposes, from finding trends in data to deciphering complex business processes.

There are four basic parameters of the Mapper:

  1. InputSplit: An InputSplit describes a unit of work for a Mapper. It is a subset of the input data, typically determined by the InputFormat. 
  2. RecordReader: A RecordReader reads a record from an InputSplit and converts it into a key-value pair for the Mapper. 
  3. Mapper: The Mapper class is the heart of the MapReduce framework. It is responsible for mapping input key-value pairs to output key-value pairs.
  4. OutputCollector: The OutputCollector collects the output of the Mapper for further processing.

Question 23: What is Identity Mapper?

Answers: The Identity Mapper is a tool that allows you to create a mapping between your organization’s identity data and your user data. This mapping can synchronize your data across multiple systems or convert it between different identity data formats.

Question 24: What is a Combiner? – Hadoop Questions for Interview

Answers: A combiner is a device that combines two or more signals into a single signal. A combiner can be used to combine two or more electrical signals, two or more optical signals, or two or more radio signals.

Question 25: List the Apache Flume features.

Answers: Apache Flume is a powerful stream processing library for data analytics. It offers an API that makes streaming data easy and versatile, as well as support for a variety of data formats. With Flume, data scientists can process large amounts of data quickly and easily. Some of the critical features of Apache Flume are as follows: 

  • Distributed and reliable 
  • Simple and flexible 
  • Scalable
  • Fault-tolerant

Question 26: What are the Benefits of using a zookeeper?

Answers: Server zookeeper systems are becoming more popular as they offer some benefits. They can help to improve productivity, manage data, and keep track of inventory. Server zookeeper systems can also help prevent data breaches and protect users’ privacy. Some benefits of using a zookeeper include: 

  • They help to keep animals safe.
  • They can help to keep animals healthy.
  • They can help to keep animals clean. 
  • They can help to keep animals fed. 
  • They can help to keep animals watered.
  • They can help to keep animals warm. 
  • They can help to keep animals calm.
  • They can help to keep animals sheltered.
  • They can help to keep animals exercised. 
  • They can help to keep animals entertained.

Question 27: Mention the types of Znode.

Answers: Znode is a project that aims to create a platform for data analysis and machine learning. The company has developed the Znode platform, designed to make it easy for users to access and use data. There are four types of Znodes: persistent, transient, sequential, and container.

Question 28: What is the default replication factor?

Answers: The default replication factor is three (3).

Question 29: What is speculative execution in Hadoop?

Answers: Speculative execution is a technique used in Hadoop to improve performance. When a task is started, Hadoop will run multiple copies of the task in parallel on different nodes. The first copy to finish will be used, and the others will be killed. This can improve performance by taking advantage of unused resources in the cluster.

Question 30: What are the different schedulers available in YARN?

Answers: YARN has three schedulers: FIFO, Capacity, and Fair. Regarding cloud-based orchestration platforms, YARN is one of the most popular options. YARN is free and open-source software that allows you to manage your services and data in a centralized way. Different schedulers are available for YARN, which can be customized to fit your needs.

Conclusion about Hadoop Questions for Interview

The 30 Hadoop Questions for Interview With Answers provide a good starting point for interviewer conversations. Knowing the right questions to ask, you can better understand your potential candidates and explore their capabilities in the data warehouse software. Thanks reader to reading this important post about Hadoop Questions for Interview.