Top Hadoop Interview Questions and Answers

by John Smith Big Data Trainer

Big Data Hadoop professionals are among the highest-paid IT professionals in the world today. Besides, the demand for these professionals is only increasing with each passing day since most organizations receive large amounts of data on a regular basis. In this Big Data Hadoop Interview Questions blog, you will come across a compiled list of the most probable Big Data Hadoop questions that recruiters ask in the industry.

What are the real-time industry applications of Hadoop?

Hadoop, well known as Apache Hadoop, is an open-source software platform for scalable and distributed computing of large volumes of data. It provides rapid, high performance, and cost-effective analysis of structured and unstructured data generated on digital platforms and within the enterprise. It is used in almost all departments and sectors today.

Here are some of the instances where Hadoop is used:

Managing traffic on streets
Streaming processing
Content management and archiving e-mails
Processing rat brain neuronal signals using a Hadoop computing cluster
Fraud detection and prevention
Advertisements targeting platforms are using Hadoop to capture and analyze clickstream, transaction, video, and social media data
Managing content, posts, images, and videos on social media platforms
Analyzing customer data in real-time for improving business performance
Public sector fields such as intelligence, defense, cybersecurity, and scientific research
Getting access to unstructured data such as output from medical devices, doctor’s notes, lab results, imaging reports, medical correspondence, clinical data, and financial data

How is Hadoop different from other parallel computing systems?

Hadoop is a distributed file system that lets you store and handle massive amounts of data on a cloud of machines, handling data redundancy.

The primary benefit of this is that since data is stored in several nodes, it is better to process it in a distributed manner. Each node can process the data stored on it instead of spending time moving the data over the network.

On the contrary, in the relational database computing system, we can query data in real-time, but it is not efficient to store data in tables, records, and columns when the data is huge.

Hadoop also provides a scheme to build a column database with Hadoop HBase for runtime queries on rows.

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Explain the major difference between HDFS block and InputSplit.

In simple terms, a block is the physical representation of data while a split is the logical representation of data present in the block. Split acts as an intermediary between the block and the mapper.
Suppose we have two blocks:

Block 1: ii nntteell

Block 2: Ii ppaatt

Now considering the map, it will read Block 1 from ii to ll but does not know how to process Block 2 at the same time. Here comes Split into play, which will form a logical group of Block 1 and Block 2 as a single block.

It then forms a key-value pair using InputFormat and records the reader and sends a map for further processing with InputSplit. If you have limited resources, you can increase the split size to limit the number of maps. For instance, if there are 10 blocks of 640 MB (64 MB each) and there are limited resources, you can assign ‘split size’ as 128 MB. This will form a logical group of 128 MB, with only 5 maps executing at a time.

However, if the ‘split size’ property is set to false, the whole file will form one InputSplit and is processed by a single map, consuming more time when the file is bigger.

Define DataNode. How does NameNode tackle DataNode failures?

DataNode stores data in HDFS; it is a node where actual data resides in the file system. Each DataNode sends a heartbeat message to notify that it is alive. If the NameNode does not receive a message from the DataNode for 10 minutes, the NameNode considers the DataNode to be dead or out of place and starts the replication of blocks that were hosted on that DataNode such that they are hosted on some other DataNode. A BlockReport contains a list of the all blocks on a DataNode. Now, the system starts to replicate what was stored in the dead DataNode.

The NameNode manages the replication of the data blocks from one DataNode to another. In this process, the replication data gets transferred directly between DataNodes such that the data never passes the NameNode.

What is a SequenceFile in Hadoop?

Extensively used in MapReduce I/O formats, SequenceFile is a flat-file containing binary key-value pairs. The map outputs are stored as SequenceFile internally. It provides Reader, Writer, and Sorter classes. The three SequenceFile formats are as follows:

Uncompressed key-value records
Record compressed key-value records—only ‘values’ are compressed here
Block compressed key-value records—both keys and values are collected in ‘blocks’ separately and compressed. The size of the ‘block’ is configurable

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