Top Hadoop Interview Questions and Answers for Freshers

Summary:- Hadoop is a Java-based big data platform that provides scalable and reliable solutions. It has efficient storage system for processing large sets of data. If you’re looking for a job in the big data industry, it’s important to be familiar with Hadoop interview questions. In this blog post, we’ll provide a list of the top 10 Hadoop interview questions and answers. We’ll also provide tips on how to prepare for these questions. So if you’re looking to make a career move into the big data industry, this blog is for you.

What is Hadoop

Big data is a huge growth industry and every new technology that comes with it brings more opportunities. One such growth industry is Hadoop one, which deals with big data processing and storage. The Apache foundation offers Hadoop under the open-source Apache license, free of charge. Today, the Hadoop industry is becoming very popular. Many companies are vying for top talent to fill positions in this field. However, it’s not enough to have a strong resume and be familiar with the basics of what Big Data entails, though it should help you land an interview at least.

If you’re looking for a career in big data, then you’ll need to know how to answer the top Hadoop interview questions. So whether you’re just starting out in your career or you’re looking to make a move to Hadoop, be sure to read on!

Hadoop Interview Questions and Answers

If your goal is employment as a Hadoop engineer or data scientist, these 10 questions will prepare you best for any job screening process.

What are components of Hadoop? 

Hadoop is an open-source implementation for distributed processing of large data sets (also called Big Data). It enables the user to store and process large amounts of data storage on commodity hardware instead of expensive, high-performance hardware. It provides a way to pool computer resources so that the time taken to process a given amount of data goes down dramatically. 

Hadoop has three major components: HDFS, MapReduce, and YARN. Additionally, there can be other sub-components that are part of the Hadoop distribution but are not in the core components. For example, Hive and Pig are part of the distributions, but they sit on top of Hadoop, rather than being part of its architecture.

What are the advantages of using Hadoop?

It is free to download and use can handle large volumes of data, allowing it to span across multiple machines. It has a built-in redundancy mechanism that allows you to keep duplicates of your files on different nodes in case one of them goes down. 

Hadoop provides a way for programmers to write their application code only once, and then run it on large clusters of computers.

What are the different nodes in a Hadoop Cluster?

The two kinds of nodes in a Hadoop Cluster are Data Nodes and Name Nodes. Data nodes store the actual data and perform all read-write operations on it. They also perform computations on the data. Name nodes store all of the file system metadata, where files are physically located.

What is shuffling in MapReduce?

Shuffling is a special operation that takes place after the reduce-operations have been done and only in the map-reduce paradigm. It is important to understand that it does not shuffle data, like for example, the sort algorithm, but only shuffles data between mapper nodes and reducer nodes.

What is InputSplit?

InputSplit is the default input format for Hadoop. An InputSplit consists of a sequence of logical records that are contiguous to one another in the physical file, but may not be contiguous in memory or on disk. It can further distinguish the logical records by their size and count.

What is the difference between HDFS and Hbase?

HDFS and Hbase (Hadoop distributed file system and Hadoop database, respectively) are 2 components of the framework. HDFS is a distributed file system that provides reliable storage for data-intensive applications through data replication. 

In contrast, Hbase is a distributed open-source non-relational database modeled after Google’s Bigtable. Unlike HDFS, Hbase does not use the traditional concepts of the filesystem. Instead, it is based on tables, rows, and columns similar to relational databases.

What is speculative execution?

Speculative execution is a feature that helps improve job performance and reduce query latency, especially on large clusters. Instead of waiting for the slowest task to finish, MapReduce tries to launch another copy at the same time. 

If multiple copies are running at once, they all have different input splits. The first copy of a task to finish also finishes its input split, which means that the next task for that input split will launch on another node where the data is already available.

What is a combiner?

A combiner in Hadoop is a function that takes two values, performs some operation on them, and returns another value. The ‘combined’ value may be the same type as the original values or different. Combiners are used to simplify the MapReduce task before it is executed. They can reduce the size of an input split. Thus, the amount of data that needs to be transferred over the network; they also produce output more quickly because there is less data and it has been processed already. 

What is the role of a JobTracker?

The JobTracker service is responsible for assigning user tasks to TaskTrackers, negotiating map and reduce slots and monitoring the progress and status of all jobs. It keeps track of where each task is executed and facilitates recovery upon failure. The Job Tracker node will also collect information from each failed task attempt and send it to the Job History Server for further analysis.

What are the differences between MapReduce and Pig?

A MapReduce program processes input splits in parallel on a cluster of nodes to produce output data. The framework takes care of scheduling tasks, monitoring them as they run, handling failures, and retrying work when necessary. 

Pig is an open-source platform for expressing data flows in procedural or declarative Java code. Pig provides a compiler that turns procedural or declarative code into pipelines of MapReduce jobs, and an execution environment to run these pipelines in a distributed computing system like Hadoop.

Final Thoughts

For those looking for a career in Big Data, Hadoop is one of the best places to start. In order to land your dream job as a data scientist or developer specializing in this field, you’ll need an impressive set of skills and knowledge about how it works. 

We hope you’ve found your answers regarding Hadoop in this blog helpful. Remember, these are only some of the questions so be sure to tailor your own list of what is relevant for your position. If you have any additional questions about these topics or would like to learn more, feel free to reach out anytime. We’d love to help.

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