Growth of Hadoop Industry over the past few years

In this Hadoop tutorial on ‘What is Hadoop?,’ we shall be learning Big Data Hadoop in detail. We will also be looking at the problems that the traditional or legacy systems had and how Hadoop solved the puzzle of big data. Finally, we will also see how Uber managed to handle big data using Hadoop.

What is Hadoop?

Big Data Hadoop is the best data framework that provides utilities, which help several computers solve queries involving huge volumes of data, e.g., Google Search. It is based on the MapReduce pattern, in which you can distribute a big data problem into various nodes and then consolidate the results of all these nodes into a final result. Big Data Hadoop is written in Java programming language. Because of the robustness of Java, Apache Hadoop ranks among the highest level Apache projects. It is designed to work on a single server with thousands of machines each one providing local computation, along with storage. It supports a huge collection of datasets in a computing environment.
Hadoop is basically licensed under Apache v2 license. It was developed based on a paper presented by Google on the MapReduce system, and hence it applies all the concepts of functional programming.
Since the biggest strength of Apache Hadoop is its scalability, it has upgraded itself from working on a single node to seamlessly handling thousands of nodes, without making any issues.

This list of Hadoop interview questions has been prepared with extensive inputs from industry experts to give you a clear advantage in your job interview. You will understand what Hadoop applications are, how Hadoop is different from other parallel processing engines, Hadoop running modes, NameNode, DataNode, JobTracker, TaskTracker, debugging Hadoop code, and more.

1. 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 click stream, 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, cyber security, 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.

2. What is distributed cache? What are its benefits?

Distributed cache in Hadoop is a service by MapReduce framework to cache files when needed.

Once a file is cached for a specific job, Hadoop will make it available on each DataNode both in system and in memory, where map and reduce tasks are executing. Later, you can easily access and read the cache file and populate any collection (like array, hashmap) in your code.

Benefits of using distributed cache are as follows:

• It distributes simple, read-only text/data files and/or complex types such as jars, archives, and others. These archives are then un-archived at the slave node.
• Distributed cache tracks the modification timestamps of cache files, which notify that the files should not be modified until a job is executed.

The term Big Data refers to all the data that is being generated across the globe at an unprecedented rate. This data could be either structured or unstructured. Today’s business enterprises owe a huge part of their success to an economy that is firmly knowledge-oriented.

Data drives the modern organizations of the world and hence making sense of this data and unraveling the various patterns and revealing unseen connections within the vast sea of data becomes critical and a hugely rewarding endeavor indeed. There is a need to convert Big Data into Business Intelligence that enterprises can readily deploy. Better data leads to better decision making and an improved way to strategize for organizations regardless of their size, geography, market share, customer segmentation and such other categorizations. Hadoop is the platform of choice for working with extremely large volumes of data.