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.

1. SAP TechEd Amsterdam, EXP11013
Shrink your SAP BW database by 40-50%
Gregor Stoeckler, DataVard

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
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3. Typical distribution of data in a BW system
Comments:
Master data
5%
Temporary data
5%3% 5%
15%
Other data
PSA data
Changelog data
ODS data
15%
32%
Cube E data
Cube F data
Cube D data
© 2013 DataVard GmbH
9%
11%
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§ 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.

4. SAP BW Information Lifecycle Management
The art of managing your data in line with its business value
USER
current
0-2 years
>2 years
© 2013 DataVard GmbH
HOT
BW Accelerator /
SAP HANA
WARM
Nearline Storage
§ Data stored in a cost
optimized way
§ 95% compression
§ Data remains readily
available
COLD
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5. The OutBoard™ effect in BW – a real life example
3500
3000
2500
Total DB
space saved
0
of 43%!
312
650
-50%
OutBoard
2000
Cube data
-50%
48.1
156
325
918
1500
ODS data
Temporary data
-15%
1000
Master data
780.3
998
500
0
-68%
© 2013 DataVard GmbH
321
183
183
Heute
mit OutBoard und ERNA
Before
After
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Other data

6. Housekeeping activities
Scope of Housekeeping
Business Warehouse
n
n
n
n
n
n
n
n
n
n
n
n
n
n
n
n
PSAs & Change Logs
Request logs & tables (RSMON*
and RS*DONE)
Unused dimension entries
Unused master data
Cube & Aggregate compression
Temporary database objects
NRIV buffering
Table buffering
BI-Statistics
Process Chain Log
Errorlogs
Unused Queries
Empty partitions
BI Background processes
Bookmarks
Web templates
© 2013 DataVard GmbH
ERP
n
n
n
n
Unused customers
Unused vendors
Phantom change documents
Phantom texts
Netweaver
n
n
n
n
n
n
n
n
n
n
n
n
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Application log
Batch log
IDoc tables (EDI40, EDIDS)
qRFC, tRFC
Job-Tables (TBTCO, TBTCP etc.)
Change & Transportsystem
Spool data (TST03)
Table Change Protocols
Batch Input Folders
Alert Management Data (SALRT*)
Old short dumps
Batch input data

7. ERNA cockpit
© 2013 DataVard GmbH
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8. Value of the Recycle Bin for PSA
ERNA Example
15 days- 6monhts
compresed in recycle bin
Quick Restore possible
6 months PSA
Same retention time
uses less space
Automated deletion
© 2013 DataVard GmbH
Benefits:
Faster loading
through smaller table
14 days
PSA
Today
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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
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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
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11. Thank You!
talktogregor@datavard.com
www.nls.datavard.com
Gregor Stoeckler
Managing Partner
DataVard GmbH
© 2013 DataVard GmbH
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12. Copyright
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.
© 2013 DataVard GmbH
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