shrink your sap bw by 40-50%
DESCRIPTION
Rapid need for more data makes it challenging to keep the data growth under control. Learn how to shrink SAP BW by half and keep data growth under control in the long term.TRANSCRIPT
Shrink your SAP BW database by 40-50% Gregor Stoeckler, DataVard
SAP TechEd Amsterdam, EXP11013
© 2013 DataVard GmbH # 2
What you can expect
During this presentation I will: § Give you tangible ideas, hints and
tips that will help you get your system in shape
§ Ask you for your thoughts and ideas
§ Make use of some German humor.
© 2013 DataVard GmbH # 3
5%
15%
15%
9% 11%
32%
5% 5% 3% Master data
Temporary data Other data PSA data Changelog data ODS data Cube E data Cube F data Cube D data
Typical distribution of data in a BW system
Comments:
§ Data you report on is only 13-17% of the system size
§ Temporary data is subject to housekeeping (BALDAT, RS*DONE, ...)
§ Use the HANA sizing report as a 1st indication (OSS note 1736976).
§ Create a plan from data load to leave.
© 2013 DataVard GmbH # 4
USER
SAP BW Information Lifecycle Management
BW Accelerator / SAP HANA
HOT
WARM
COLD
current
0-2 years
>2 years
Nearline Storage § Data stored in a cost
optimized way § 95% compression § Data remains readily
available
The art of managing your data in line with its business value
© 2013 DataVard GmbH # 5
The OutBoard™ effect in BW – a real life example
183 183
998
321
918
780.3
650
325
312
156
0
48.1
0
500
1000
1500
2000
2500
3000
3500
Heute mit OutBoard und ERNA
OutBoard Cube data ODS data Other data Temporary data Master data
Before After
-68%
-15%
-50%
-50%
Total DB space saved
of 43%!
© 2013 DataVard GmbH # 6
Housekeeping activities
n Application log n Batch log n IDoc tables (EDI40, EDIDS) n qRFC, tRFC n Job-Tables (TBTCO, TBTCP etc.) n Change & Transportsystem n Spool data (TST03) n Table Change Protocols n Batch Input Folders n Alert Management Data (SALRT*) n Old short dumps n Batch input data
Netweaver
n Unused customers n Unused vendors n Phantom change documents n Phantom texts
ERP
n PSAs & Change Logs n Request logs & tables (RSMON*
and RS*DONE) n Unused dimension entries n Unused master data n Cube & Aggregate compression n Temporary database objects n NRIV buffering n Table buffering n BI-Statistics n Process Chain Log n Errorlogs n Unused Queries n Empty partitions n BI Background processes n Bookmarks n Web templates
Business Warehouse
Scope of Housekeeping
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ERNA cockpit
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Value of the Recycle Bin for PSA
Faster loading through smaller table
Same retention time uses less space
Today
6 m
onth
s P
SA
14 d
ays
PS
A
15 d
ays-
6m
onht
s
com
pres
ed in
recy
cle
bin
Qui
ck R
esto
re p
ossi
ble
Benefits: ERNA Example
Automated deletion
© 2013 DataVard GmbH # 9
How the best manage data growth
From cradle to grave Manage cold and old data using Nearline Storage
Biggest potential in DSOs, but also helpful in Cubes
Build a detailed housekeeping plan and adhere to it. If possible automate.
© 2013 DataVard GmbH # 10
Conclusion
Always travel lightweight
Manage data actively in your production and non-production systems
Plan and do housekeeping, if possible automate this process and ensure central governance.
Whether you plan to go to HANA or not: the preparation brings many benefits already
..
Don’t be shy and ask for a personal fitness coaching at booth E13 in Hall 10.
© 2013 DataVard GmbH # 11
Thank You!
Managing Partner
DataVard GmbH
Gregor Stoeckler
www.nls.datavard.com
© 2013 DataVard GmbH # 12
Copyright DataVard GmbH. All rights reserved.
No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of DataVard GmbH. The information contained herein may be changed without prior notice. DataVard and OutBoard are trademarks or registered trademarks of DataVard GmbH and its affiliated companies. SAP, R/3, SAP NetWeaver, SAP BusinessObjects, SAP MaxDB, SAP HANA and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG in Germany and other countries. All other product and service names mentioned are the trademarks of their respective companies. Data contained in this document serves informational purposes only. National product specifications may vary. These materials are provided by DataVard GmbH and its affiliated companies (“DataVard") for informational purposes only, without representation or warranty of any kind, and DataVard shall not be liable for errors or omissions with respect to the materials. The only warranties for DataVard products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty.
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