Introduction to Hadoop

Today's varied business around the world is very likely to produce the unstructured Big Data which needs to be stored, managed and utilized effectively. That led to the evolution of Big Data frameworks such as Hadoop.
Hadoop is an open-source distributed data processing framework that contains effective programming models to process big data. The framework is known for its cost-effective approach where clusters of cheap commodity hardware are used.

Evolution of Hadoop

Traditionally, Relational Database Management Systems (RDBMS) like MS SQL Server and Oracle Database are used to store the organizational data and which are processed with the help of complex software that interacts with the respective databases. While the advent of Information Technology led to enormous data produced by the organization made the traditional approaches ineffective, the different types of unstructured data that cannot be accommodated into relational databases become a challenge among the organizations.

Open Source Project - Hadoop

When the traditional systems failed to address the need for processing huge data volumes, Google had found a solution to all the above problems. They used an algorithm known as MapReduce that follows a unique approach that divides the data processing over the network connected with computers.
Inspired by this idea followed by the Google, Doug Cutting and Mike Cafarella gathered a team in 2015 to work on a similar project. They declared the project as an open-source resource and named it as Hadoop. Now, the open-source framework is managed by a non-profit organization called Apache Software Foundation.
The framework is written in java programming language with simple programming models. It enables data processing in a distributed environment by using MapReduce algorithm that controls clusters of commodity computers over the network. One of the most important advantages of using Hadoop is that it is fast and cost-efficient. By scaling up from a single server machine to thousands of cheap commodity hardware, it effectively achieves distributed data processing, computation, and storage.