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Next Generation Data Warehouse Design with OLTP and OLAP Systems Sharing same Database

by S. Santhosh Baboo, P. Renjith Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 13
Year of Publication: 2013
Authors: S. Santhosh Baboo, P. Renjith Kumar
10.5120/12557-9282

S. Santhosh Baboo, P. Renjith Kumar . Next Generation Data Warehouse Design with OLTP and OLAP Systems Sharing same Database. International Journal of Computer Applications. 72, 13 ( June 2013), 45-50. DOI=10.5120/12557-9282

@article{ 10.5120/12557-9282,
author = { S. Santhosh Baboo, P. Renjith Kumar },
title = { Next Generation Data Warehouse Design with OLTP and OLAP Systems Sharing same Database },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 13 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 45-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number13/12557-9282/ },
doi = { 10.5120/12557-9282 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:38:19.135818+05:30
%A S. Santhosh Baboo
%A P. Renjith Kumar
%T Next Generation Data Warehouse Design with OLTP and OLAP Systems Sharing same Database
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 13
%P 45-50
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Online Transaction Processing (OLTP) systems have been using the traditional relational Database management system for many years. Online Analytical Processing (OLAP) system for analytical reporting is optimized to aggregate many records which involve more read operations. Hence to enhance the performance in OLAP systems various modeling options like star schema, extended star schema was implemented. But with the latest innovations in hardware technologies like availability of multi core dual processors, availability of 64 bit processors, decreasing cost of main memory and software technology innovations like In-Memory computing, Columnar storage and other with various other new features now there is less concern towards the performance degradation and performance optimization in a data warehouse system. If there is an option to use same database for the OLTP and OLAP systems then the business intelligence system can get the real time data for the analytical query reporting. This document presents the overview of the next generation data warehouse architecture which shares the database along with OLTP and discuss how it is going to use the in memory capability to apply real time analytics.

References
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  2. Hasso Plattner, Hasso Plattner Institute for IT Systems Engineering, A Common Database Approach for OLTP and OLAP Using an In-Memory Column Database
  3. Jens Krueger, In-Memory Data Management for Enterprise Applications, Hasso Plattner Institute for Software Engineering
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  7. Jens Krueger, In-Memory Data Management for Enterprise Applications. Hasso Plattner Institute for Software Engineering
  8. J. Gray. Tape is Dead. Disk is Tape. Flash is Disk,RAM Locality is King. Storage Guru Gong Show,Redmon, WA, 2006.
  9. Jan Schaffner, Anja Bog, Jens Kr¨uger, and Alexander Zeier . A Hybrid Row-Column OLTP Database Architecture for Operational Reporting, Hasso Plattner Institute for IT Systems Engineering,
  10. M. Stonebraker et al. C-Store: A Column-oriented DBMS. In Proc. VLDB, 2005.
  11. Jens Krueger, Martin Grund, Johannes Wust, Alexander Zeier, Hasso Plattner, Merging Differential Updates in In-Memory Column Store, Hasso Plattner Institute for IT Systems Engineering
Index Terms

Computer Science
Information Sciences

Keywords

In-Memory analytics columnar data storage Next generation data warehouse Business intelligence Real time data analysis Row store

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