Storage and Management Techniques for Large Databases

STORAGE AND MANAGEMENT TECHNIQUES FOR LARGE DATABASES

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Abstract
relational informationbases cater with the facility of dealing with great(p) data sets by storing data in two relational tables which are linked through keys. Since it handles large amount of data it requires complex hardware and software. It also faces the challenge of efficient terminus and convalescence mechanisms. This written report renders the challenges and issues faced in managing and storing large databases and also describe several methods used for this purpose. The main techniques described in this paper include partitioning, inverted indexing, compression, federated database servers and RAID.

Introduction
Relational databases store large sets of data within a tabular structure having tables. A relational database has everything related to and is thus prone to less redundancy. Relational databases firstly store the data, and devise a way for creating and retrieving the data, and a way for ensuring that the data is logically consistent. Relational databases store coordinated information containing hundreds of tables with thousands of rows per table.



The data stored by them usually consist of economic data, inventory data and census data, which is usually required by users for searching specific pieces of data quickly. They need to browse the related information stored in different tables present in the database. The nucleus challenge involved in storing and managing large databases involves catering the users in such a way, so that they can access the applicable information relevant to their query in a go around of time (Keogh et. al 2001). Several techniques like partitioning, clustering, compression, optimized indexing and so on have been developed in catering effective storage of large databases. This report explains in detail the challenges encountered in storing and...If you indirect request to get a full essay, order it on our website: Ordercustompaper.com



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