Hadoop Explained An Overview

Hadoop – An Overview 

Hadoop is an open source project that was created to make storage and processing of data in large amounts affordable to a big community of users. This is probably the first of projects that were designed to allow users to use cheap hardware and free open source software to utilize parallelized processing. Companies right now are using Hadoop as an integration point for existing data sources and they bring data from the relational databases or work files and all the data sources they can find inside the company. One of the common use cases for Hadoop is processing clickstream data from the websites to analyze the behavior of the users from the website and to gain some useful insights from that.

Hadoop is an open source project that was created to make storage and processing of data in large amounts affordable to a big community of users. This is probably the first of projects that were designed to allow users to use cheap hardware and free open source software to utilize parallelized processing. Companies right now are using Hadoop as an integration point for existing data sources and they bring data from the relational databases or work files and all the data sources they can find inside the company. One of the common use cases for Hadoop is processing clickstream data from the websites to analyze the behavior of the users from the website and to gain some useful insights from that.

 

Hadoop consists of two main components: that first one is Hadoop Distributed File System or HDFS. HDFS is designed in such a way so as to chunk the large files into smaller pieces and copy these pieces across multiple servers in the cluster. This makes HDFS scalable because you can plug in servers to the clusters and it makes the disk space instantly available to the users. Another property of HDFS is redundancy since it can tolerate a failure of one node and can still provide the data to the users.

 

The second component of Hadoop is MapReduce computational framework. MapReduce allows you to use various programming languages to process your data inside Hadoop; you can do different computations and aggregations and any other thing want to do with your data. And Hadoop will take care of parallelizing these jobs for you and run it through multiple machines across the cluster.

 

Besides these two components, there’s a whole ecosystem of projects surrounding Hadoop and these projects are intended to make using and processing data easier. Projects like Hive and Pig are designed to bring high-level languages like SQL and other procedural languages to make it easier to process your data inside Hadoop. They’re little efforts right now being done to implement real-time processing in Hadoop and projects like Impala and Spark are designed to achieve this goal. Hadoop helps companies to know about data that was previously out of their reach either due to data size or due to the data’s nature itself. If a company is in retail, finance or logistics business Hadoop can really help the company to get valuable insights into the data, increase revenue and improve customer service.

Updated: May 3, 2015 — 4:19 pm

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