As the name says, Big Data is actually something huge, comprising of 3Vs ie, Volume, Variety and Velocity.
Big Data is a huge collection of both structured and unstructured data along with the collection of various tools, frameworks, audios, videos, stock exchanges, transport data, search data and so on and so forth. it is used by several corporate and educational institutions to analyze and predict the consequences of certain actions. Big Data has a wide scope and is used by almost all the sectors; banking, government, finance, airlines, and hospitality.
In order to manage such a huge amount of data, we need expert analysis and deep examination. This process of analyzing big data is termed as Big Data Analytics. It is a great helpful tool for market researchers and business users for predicting future consumer actions based on past consumer patterns. It is a secure step to find unknown correlations, cost reductions strategies, detecting faults and making faster and smarter decisions.
This huge amount of data requires efficient storage facilities which are secured and accessible without any hindrance. One such optimum tool for storing and managing Big Data is Hadoop. It is reliable in security issues, flexible, economical and capable in maintaining large amount of data with an ease. Hadoop was released as an open source framework in 2008 based on Java programming, maintained by Apache Software Foundation. It is architected in such a framework that it can handle most of the failures by maintaining multiple copies of the data automatically, breaking the data according to the cluster and deploying the data upon which it is intended to operate.
Hadoop comprises of following components:
· HDFS (Hadoop Distributed File System): It is designed to run on commodity hardware. It handles data effectively and efficiently, stores data in an accessible format and maintains records across multiple nodes over various machines and clusters. HDFS sends data to the server once and uses it as many times as it wants. HDFS comprises of 3 main components- Name Mode, Data Mode and secondary Name Mode wherein Name Mode is the master node which manages the entire Data Node which acts as a slave node that serve the given query.
· MapReduce: This application works as a parallel framework for scheduling and processing the data. The Mapper is responsible for data processing steps and Reducer receives the output from Mapper and sorts the data accordingly.
· Common: It provides a pre-defined set of utilities used by all other modules in Hadoop.
· YARN (Yet Another Resource Navigator): It is an improved version of MapReduce and has the efficiency of running various Hadoop applications without bothering for increasing work loads.
Hadoop is capable of storing and processing large amounts of data over several machines running parallel to each other. It enables streamlining the surplus data across clusters using simple programming models and serves multiple purpose for machine learning, image processing, web crawling, data analysis and study of statistical data. Although Hadoop is the most reliable tool for storing Big Data, yet it faces certain challenges such as automated testing of end to end solutions is practically not possible, it does not provide easy tools for removing noise from the data and maintaining such data requires a huge amount of knowledge and extensive internal resources.
Inspite of the few challenges, the benefits provided by Hadoop makes it the most preferred platform for Big Data analytics. It offers an array of tools that most of the data scientists need and makes machine learning algorithm an easy task.
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