28 May 2017

What is Hadoop?




Introduction

Hadoop is an open supply, java-based definitely programming framework that facilitates the processing and garage of extremely huge records devices in a disbursed computing surroundings.It is part of the apache undertaking sponsored by using the apache software program basis.

Hadoop makes it viable to run packages on structures with hundreds of commodity hardware nodes, and to address thousands of terabytes of records. Its disbursed record device allows rapid information switch costs amongst nodes and permits the system to keep working in case of a node failure. This technique lowers the chance of catastrophic system failure and surprising records loss, even supposing a extensive variety of nodes emerge as inoperative. Therefore, hadoop fast emerged as a basis for large records processing obligations, which includes medical analytics, business and income making plans, and processing significant volumes of sensor facts, which include from net of things sensors.

Hadoop changed into created by way of pc scientists doug cutting and mike cafarella in 2006 to help distribution for the nutch seek engine. It was stimulated by means of google's mapreduce, a software program framework wherein an software is damaged down into numerous small elements. Any of these elements, that are also called fragments or blocks, can be run on any node within the cluster. After years of improvement inside the open source network, hadoop 1.0 have become publically available in november 2012 as part of the apache undertaking sponsored by using the apache software basis.

Following are the challenges with dealing bigdata:

High capital investment in procuring a server with high processing capacity with Hadoop.

Difficult in program query for building.
Enormous Time Taken
 High capital investment in procuring a server with high processing capacity with    Hadoop. : Hadoop  work on normal commodity of hardware and keep multiple copy to ensure reliability of data. A maximum of 4500 machines can be connected together using Hadoop.

Enormous time taken :The process is broke down into such pieces and execute in parallel manner. A maximum of 25 Petabyte (1 PB = 1000 TB) data can be processed using Hadoop.

Difficulty in program query building  : Query in Hadoop are  simple as coding in any language. We just need to change the way of think around building a query to enable parallel processing.



Framework of Hadoop Processing

Draw a analogy from our daily life to understand the working of Hadoop. The bottom of the pyramid of any firm are the people who are individual contributors.  They can be analyst, programmers, etc. Managing their work is the project manager. The project manager is responsible for a successful completion of the task.  

Hadoop works in a similar format. On the bottom we have machines arranged in parallel Manner. 

These machines are analogous to individual contributor in  analogy. Every machine have a data node and  task tracker. Data node is also known as HDFS (Hadoop Distributed File System) and Task tracker is also known as map-reducers.

Data node contains the entire set of data and Task tracker do all the operations. 

Enables you to do a task and data node as your brain, which contains all the information which you want to process. These machines are working in silos and it is very essential to coordinate them.

Job Tracker make sure that each operations is completed and not, if there is a process failure at any node, it needs to assign a duplicate task to some task tracker. Job tracker also distributes the entire task to all the machines.

A name node on the other hand coordinates all the data nodes. It governs the distribution of data to each machine. 

it finds the duplicate data which was sent to other data node and duplicates it again.

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