Session 4112 BW NLS Data Archiving: Keeping BW in Tip-Top Shape for SAP HANA. Sandy Speizer, PSEG SAP Principal Architect

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1 Session 4112 BW NLS Data Archiving: Keeping BW in Tip-Top Shape for SAP HANA Sandy Speizer, PSEG SAP Principal Architect

2

3 Public Service Enterprise Group

4 PSEG SAP ECC (R/3) Core Implementation

5 SAP BW PSEG Between 2001 and 2009, BW was used for 7 ECC Reporting Applications. All are very stable and small. Size remained < 1 TB. Extract and Load window: Between midnight and 5am No issues meeting SLA s. Revenue and Statistics was created for Utility Monthly Closing Reports. Largest application in BW. Custom application based on Legacy Billing Application.

6 SAP IS/U Billing PSEG Project began in 2007 to bring legacy Utility Billing application into SAP. IS/U Billing went live April, Caused explosive growth on transactional side and cascaded into BW. Revenue and Statistics (Legacy) application completed with March, 2009 Billing Cycle. Data becomes static at.8tb. Sales Statistics started with April, 2009 Billing Cycle. With ipower Project, BW adds 10 new applications. Sales Statistics quickly grows into largest application in BW.

7 SAP BW Business Warehouse: 2009 BW Applications Utility (PSE&G) Applications PSEG Applications Sales Statistics Human Resources Service Management IT Toolkit CO Scorecard Holdings Financials TPS (3 rd party suppliers) Mat. Mgmt / Supply Chain Customer & CRM Data Env. Health & Safety FICA (Utility Finance) Finance (FICO) Device Management Work Management Billing & Invoicing Data Campaign Management Revenue & Stats (older) Solar Loan

8 SAP BW Issues For most of 2009, project team was focused on ECC System stabilization. By end of 2009, BW was also experiencing explosive growth: There are performance issues with daily data loads and running queries We are not meeting our SLA s, primarily for the Utility applications Increased storage allocations to accommodate growth Many changes were made to: Stabilize the system Improve performance load time However, we were still not meeting the SLA s

9 BW Assessment Assessment project initiated in Q1, 2010 and initial findings were completed early June, 2010 Short term (2010) recommendations will enable us to meet SLA s 2010 PSE&G Sales Stats remediation 2011 PSE&G Sales States re-architecture Process automation, maintenance, monitoring, and transaction optimization Long Term (2011 and beyond) recommendations to develop BW into a stable, supportable environment to enable expansion and growth: Archiving Remediate other troubled applications Install best practices for future applications

10 Archiving Analysis Sales Stats is our largest application in BW, with over 3.5TB: Data Storage Dates Rev & Stats.8 TB 1/2006 3/2009 Old Sales Stats 2+ TB 4/2009 8/2010 Old Sales Stats A.9 TB 9/2010 3/2011 New Sales Stats.5 TB (per year) 3/ current Data must be kept for 7 years and is used to reconcile monthly closing. Objectives: Implement PBS, Sybase IQ and NLS for BW Archive old Rev and Stats data Archive Old Sales Stats and Old Sales Stats A data Set up archiving for New Sales Stats data (to keep stable growth)

11 SAP BW Data Aging Strategies Data Category Online Database Near-Line Storage (read only) Classic Archive (read only) Frequently read/ changed data (current/ hot ) Infrequently read data (mature/ warm ) Very rarely read data (aged/ cold )

12 Project Assessment RFI, research and discussion on technology PBS and Sybase IQ were selected as best fit for our needs. RFP for vendor selection Sigma Business Solutions selected Analysis and planning phase BW Compression was planned to be completed before project go lives (not part of project).

13 Column Based Architecture Row-based Database => ideal foroltp needs Column-based Database => ideal forolap needs B2 B5 A1 A2 A3 A4 A5 A... B3 B... B1 B2 B3 B4 B5 B... C1 C2 C3 C4 C5 C B1 B4 Data is stored horizontally in rows Reporting is very I/O-intensive No effective analyses / reporting Data is stored vertically Each column is stored separately The datais the index Retrieve only columns used in the query Reduce System I/O dramatically Allocate a thread for each column individually Process the query in parallel

14 Phase 1 End user query Nearline Repository - IQ, Vectorwise- SAP NLS Nearline Storage Interface PBS CBW Nearline Services BW Database SAP HANA Classical ADK Storage SAP Archive Files PBS Indexes/Aggregates

15 BW / NLS Hardware SAP BW Server PBS CBW NLS Server NearlineRepository SAP Gateway CBW NLS PBS CBW NLS Server Instance Sybase IQ Database Instance CBW NLS supports: AIX, HP-UX, Linux, Solaris, Windows Server Sybase IQ 15.x Minimum Test Hardware 4 Core CPU 16 GB RAM* 130 GB+ HDD Minimum PROD Hardware 8 Core CPU 32 GB RAM* 130 GB+ HDD Minimum PROD Hardware For PSEG 8 Core CPU 32 GB RAM* 130 GB+ HDD

16 PSEG NLS IQ Landscape PRD System DEV & QA Systems

17 Implementation Strategy Project planned as 4 phases: Phase 1: Install PBS/Sybase and archive Rev & Stats to NLS Phase 2: Archive Old Sales Stats to NLS Phase 3: Archive Old Sales Stats A to NLS Phase 4: Implement setup for archiving of New Sales Stats to NLS

18 Phase 1: Install PBS and Archive Rev & Stats to NLS (0.8 TB) Installation was successful. The infrastructure is distributed between Production and all other systems. Initially, Dev, QA and Pre-Prod Systems were not optimally set up for our systems. A QA or Pre-Prod refresh would not be able to be done without destroying the other systems. For economies of scale, we wanted all 3 systems on 1 server. Initially, they were set up as 1 database. Revised later on to handle refresh activities and were set up as 3 separate databases on 1 server. Lesson Learned: Review architecture in detail before beginning build and think through the architecture for the long term. Phase 1

19 Phase 1 Phase 1: Install PBS and Archive Rev & Stats to NLS (cont d) Discovery of technology differences in field definitions. Floating Point fields have different algorithms in Oracle and Sybase. Older custom applications may use floating point field definitions. No impact on financials and operations. Very minor decimal point differences.

20 Phase 1 Phase 1: Install PBS and Archive Rev & Stats to NLS (cont d) Testing Process PBS enables Queries written against MP to be automatically directed to both NLS and BW Databases. Queries written against Cubes are NOT automatically directed to NLS and BW Databases. Need to have the Read Near-Line Storage Data As Well checkbox enabled. For us, this was a security issue and we redid the security profile to allow access to RSRT transaction.

21 Transaction: RSRT

22 Phase 1 Phase 1: Install PBS and Archive Rev & Stats to NLS (cont d) Production Go Live: Transports moved successfully and archiving processing was running as per plan. However, backups and Logs were defined on same physical device. Logs filled all available space and corrupted Sybase database due o one corrupted InfoCube. Latest backup was corrupted. Examined our options and found best resolution for us was to restore all archived data back to BW Oracle database. Restore corrupted cube manually. Then re-archive cubes and DSO s. Sigma s expertise and guidance enabled a smooth reload and re-archive

23 Phase 1

24 From BW Home use transaction: /n/pbs/nlsa_monitor If all is well, both services: NLS database and NLS Interface will show green lights: NLS Monitoring

25 Phase 2 Phase 2: Archive Old Sales Stats to NLS Testing of large volumes of data: QA archiving of 1 month of data (400+ mil records) was unable to be completed until additional storage and memory was allocated. Even though we scaled the test down to match size of system, we had issues completing the archiving jobs in time for testing. Be sure to validate your test data sizes to ensure you can meet project schedule and include enough test data to test all scenarios.

26 Phase 2 Phase 2: Archive Old Sales Stats to NLS Testing Process: Testing with standard cube provided inaccurate results. Patch was provided by PBS to resolve issue. Not all cubes are treated the same by NLS and PBS. Check your test query output from standard cubes extra carefully to ensure the data is returned correctly.

27 Phase 3 Phase 3: Archive Old Sales Stats A to NLS Testing was successful. Go Live was successful. There is less data in SS-A cubes. Performance issue with Query Run-time in Production. Analysis showed performance was degraded due to the fact that transaction data in NLS was looking at master data in online BW system. PBS enables replication of master data to NLS and this resolved the performance issue. Master data tables are still being updated for current data.

28 Phase 4 Phase 4: Implement setup for archiving of New Sales Stats to NLS Testing was successful. Go Live was successful. New Sales Stats was designed with archiving in mind.

29 New Sales Stats Archiving

30 LEARNING POINTS Key Points: NLS is much more effective than standard (ADK) archiving for BW. Project has to be planned and executed well to meet objectives and to stay on target. Archiving is a useful tool to slow down growth of BW systems. Once growth is stabilized, BW is easier to maintain and moving BW to HANA will be easier and less expensive (less HANA to purchase.)

31 Sales Stats Statistics Average Compression 85% - for some InfoCubes 90% - Based on 2,298,052,528 rows archived Query Performance on the archived data is very good and overall query Query Performance on the archived data is very good and overall query performance continues to improve as more data is archived.

32 Next Steps Next Steps Complete archiving in production. Continue focus on clean up. Re-design other large applications and archive where possible. Upgrade BW to 7.3 to prepare for HANA.

33 BEST PRACTICES Design new BW applications with archiving in mind. Logical and physical partitioning. Make archiving part of the project, if possible. Implement standard processes from SAP s Data Management Guide. ag.de/~form/sapnet?_scenario= &_shortkey= & _OBJECT= E Or Download the "Data Management Guide" from -> Data Archiving Not everything needs to be archived, a lot can be deleted. Clean up as much as possible. Set up standard housekeeping jobs on weekly/monthly basis. Determine SLA s and work towards them methodically.

34 TAKE AWAY POINTS 9 Points to Take Home 1. Validate queries during project planning phase. 2. Best Practices should include archiving strategy. 3. Incorporate manual validation processes to ensure current data is transferred properly. 4. Archive during times of low activity in system. 5. Ensure project team is comprised of the right skill mix to execute deliverables and address issues as they arise. 6. With new technology, ensure everyone is on same page. 7. Ensure Project Team resources are focused on project deliverables. 8. Get a knowledgeable partner, both in BW and in NLS Archiving. 9. Be extra generous with hardware sizing.

35 THANK YOU FOR PARTICIPATING Please provide feedback on this session by completing a short survey via the event mobile application. SESSION CODE: 4112 For ongoing education on this area of focus, visit

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