Traditional Approach:
In this approach, an endeavor will have a PC to store and process huge information. Here information will be put away in a RDBMS like Oracle Database, MS SQL Server or DB2 and advanced programming projects can be composed to collaborate with the database, handle the required information and present it to the clients for investigation reason.
This methodology functions admirably where we have less volume of information that can be suited by standard database servers, or up to the furthest reaches of the processor which is handling the information. Be that as it may, with regards to managing colossal measures of information, it is truly a repetitive errand to process such information through a customary database server.
Google's Solution:
Google tackled this issue utilizing a calculation called MapReduce. This calculation isolates the undertaking into little parts and allots those parts to numerous PCs associated over the system, and gathers the outcomes to frame the last result dataset.
Above graph demonstrates different ware fittings which could be single CPU machines or servers with higher limit.
Hadoop:
Doug Cutting, Mike Cafarella and group took the arrangement gave by Google and began an Open Source Project called HADOOP in 2005 and Doug named it after his child's toy elephant. Presently Apache Hadoop is an enrolled trademark of the Apache Software Foundation.
Hadoop runs applications utilizing the MapReduce calculation, where the information is handled in parallel on various CPU hubs. To put it plainly, Hadoop system is capabale enough to create applications equipped for running on bunches of PCs and they could perform finish measurable investigation for an enormous measures of information.
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