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    What is Apache Hadoop framework and its usage?

    Apache Hadoop is an open source platform which provides data processing of distributed, large and scalable datasets in a reliable manner using simple programming models. It is built upon clusters of computers hence does data processing in a cost effective fashion. The data can be structured, semi structured or completely unstructured. These features make Apache Hadoop an ideal candidate for making large data lakes supporting big data analytics. 

    Apache Hadoop framework is consisted of the following modules:

    a. Hadoop Common – it contains libraries and utilities needed by other Hadoop modules

    b. Hadoop Distributed File System (HDFS) – it is a distributed file-system that stores data on commodity machines, providing very high aggregate bandwidth across the cluster;

    c. Hadoop YARN – it is a platform responsible for managing computing resources in clusters and using them for scheduling users' applications

    d. Hadoop MapReduce – it is an implementation of the MapReduce programming model for large-scale data processing.

    The intent of this framework is to distribute large chunks of data in different computers that are part of cluster and also handle any hardware failures in an automated fashion. 

    From Hadoop's usage/usecases perspective, it has an ability to provide an access to real time large datasets for making data driven decisions for your data scientist, line of business (LOB) owners and developers. Hadoop is helping in data science growth which combines machine learning, statistics, advanced analysis and programming.  Hadoop optimizes and streamlines costs in enterprise data warehouse by moving “cold” data not currently in use to a Hadoop-based distribution. 

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