Next Generation Data Warehouse Design with Big data for Big Analytics and Better Insights

Next Generation Data Warehouse Design with Big data for Big Analytics and Better Insights

P Renjith Kumar Business Process Consultant

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Lt. Dr. Santhosh Baboo & P Renjith Kumar

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α Manonmaniam Sundaranar University

Next Generation Data Warehouse Design with Big data for Big Analytics and Better Insights

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Abstract

Traditionally organizations invested more in decision support systems. With the evolution of business intelligence tools many organizations were able to get analytical reports based on OLAP systems. Now with the frequently changing trends in customer behaviour and customer markets there is a huge necessity for enterprises to get analytical reports beyond OLAP system based analysis. There is huge innovation in the area of hardware and software which helps enterprises to gain advantage of all available formats of data and help enterprise to get business insights based on that data. Big data is one of the key factors to be focused which can help to get real time analytics on all available formats of data. This document presents the overview of the next generation data warehouse architecture based on Big data for better business insights.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

P Renjith Kumar. 2013. \u201cNext Generation Data Warehouse Design with Big data for Big Analytics and Better Insights\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 13 (GJCST Volume 13 Issue C7): .

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GJCST Volume 13 Issue C7
Pg. 19- 23

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

big data big data analytics business i ntelligence real time analytics

Submission ReceivedDecember 10, 2012
Peer Review Double Blind
Handling Editor
Accepted January 2, 2013
Published January 15, 2013

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v1.2

Issue date

July 2, 2013

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Article Matrices

Total Score: 136

Country: United States

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: Lt. Dr. Santhosh Baboo & P Renjith Kumar (PhD/Dr. count: 1)

View Count (all-time): 268

Total Views (Real + Logic): 9734

Total Downloads (simulated): 2615

Publish Date: 2013 07, Tue

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Total Views: 9734

Total Downloads: 2615

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