By Topic

Speeding ETL Processing in Data Warehouses Using High-Performance Joins for Changed Data Capture (CDC)

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Tank, D.M. ; Dept. of IT, Charotar Univ. of Sci. & Technol., Anand, India ; Ganatra, A. ; Kosta, Y.P. ; Bhensdadia, C.K.

In today's fast-changing, competitive environment, a complaint frequently heard by data warehouse users is that access to time-critical data is too slow. Shrinking batch windows and data volume that increases exponentially are placing increasing demands on data warehouses to deliver instantly-available information. Additionally, data warehouses must be able to consistently generate accurate results. But achieving accuracy and speed with large, diverse sets of data can be challenging. Various operations can be used to optimize data manipulation and thus accelerate data warehouse processes. In this paper we have introduced two such operations: 1. Join and 2. Aggregation-which will play an integral role during preprocessing as well in manipulating and consolidating data in a data warehouse. Our approach demonstrate how we can save hours or even days, when processing large amounts of data for ETL, data warehousing, business intelligence (BI) and other mission critical applications.

Published in:

Advances in Recent Technologies in Communication and Computing (ARTCom), 2010 International Conference on

Date of Conference:

16-17 Oct. 2010