A Hybrid Technology Platform for Increasing the Speed of Operational Analytics

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Track 1 d a hybrid technology platform for increasing the speed of operational analytics - ed lynch

Track 1 d a hybrid technology platform for increasing the speed of operational analytics - ed lynch

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  • 1. Ed Lynch – Executive Client Technical ProfessionalData Warehousing and Business Analytics for System z10 October 2012A Hybrid Technology Platform for Increasingthe Speed of Operational Analytics © 2009 IBM Corporation
  • 2. Speaker Biography Ed Lynch is an Executive Client Technical Professional specializing in IBM’s System z Data Warehousing, Business Analytics, and Information Integration software products. Ed’s twenty- eight year career with IBM has spanned many areas of IBM, and has always involved IBMs Information Management (IM) products. His previous roles have included DB2 for z/OS Development and Management, DB2 Technical Marketing, Development and Delivery of IM Product Education, a Principal of Information Integration Design and Implementation Consulting Services, and DB2 Tools & Information Integration Technical Sales. Currently, Ed is the lead Technical Specialist for North America’s System z Data Warehousing and Business Analytics, and Information Integration Software Technical Sales team. Ed has worked extensively with DB2 across the various operating system platforms, InfoSphere Data Replication, InfoSphere Classic Replication Server, DB2 Analytics Accelerator, DB2 DataPropagator, InfoSphere Federation Server, InfoSphere Classic Federation Server, DB2 Connect, IBM Information Server, and IBM’s Business Analytics solutions. In his current role, he frequently works with product development in identifying and prioritizing product requirements, and developing product strategy. He also provides software technical sales support and works extensively with customers to create architectures using these products. edlynch@us.ibm.com 1-972-561-9975 © 2009 IBM Corporation
  • 3. Abstract With the wealth of data available today, organizations are no longer willing to relegate information to the back office. Modern organizations are demanding access to information. However, it is not enough to capture information, users must be quickly able to sift through these massive amounts of data, extract information and transform it into actionable knowledge. Systems today are enabling organizations to anticipate risk, identify threats, assess readiness, and match the risk assessment to the resources required to address them; all at the time of decision. They use a platform that provides the ability to react to changes decisively, based upon the facts of the situation, not in hours or days- but at the moment of opportunity. They optimize decisions based upon current weather conditions, past threats and behaviors and current resource availability to assure a successful operation.This session will review the architecture and benefits of a hybrid system of MPP and SMP technologies enabling the merging of fit for purpose and mixed workload capabilities into a single system. See how this hybrid system facilitates both transaction- oriented applications and analytics into a single platform for operational analytics. Find out why these enhancements are the next logical steps in creating a highly optimized environment, both in price and performance, that is designed to meet the wide range of analytic workloads that todays organizations need to accommodate. © 2009 IBM Corporation
  • 4. DB2 Analytics Accelerator V3 More insight from your dataFurther extending the features • Unprecedented response times for “right-time” analysis • Complex queries in seconds rather than hours • Transparent to the application • Inherits all System z DB2 attributes • No need to create or maintain indices • Eliminate query tuning Blending System z and Netezza • Fast deployment and time-to-value technologies to deliver unparalleled, mixed workload performance for complex analytic business needs. 4 © 2009 IBM Corporation
  • 5. DB2 Analytics Accelerator Train-of-thought Analytics FAST Cost Saving Appliance Complex queries run Eliminate costly No applications to up to 2000x faster query tuning while change, just plug it while retaining single offloading complex in, load the data, record lookup speed query processing and gain the value5 © 2009 IBM Corporation
  • 6. Introducing DB2 Analytics Accelerator V3 Reducing the Cost of High Speed Analytics Improve Productivity Lower Host Costs Consolidate Eliminate query tuning Reduce storage costs Reduced complexity Eliminate table indexing Offload query processing Reduced software costs Minimize storage admin Defer system upgrades Reduced hardware costs6 © 2009 IBM Corporation
  • 7. Fast Time to Value IBM DB2 Analytics Accelerator Production ready - 1 person, 2 days Table Acceleration Setup 2 Hours – DB2 “Add Accelerator” – Choose a Table for “Acceleration” – Load the Table (DB2 copy to Netezza) – Knowledge Transfer – Query Comparisons Initial Load Performance 400 GB “Loaded” in 29 Min 570 million rows (Loads of 800GB to 1.3TB/Hr) Actual Query Acceleration 1908x faster 2 Hours 39 Minutes to 5 Seconds CPU Utilization Reduction 35% to ~0%Actual customer results, October 2011 © 2009 IBM Corporation
  • 8. Performance & Savings DB2 w ith Times Faster Queries run faster DB2 O nly IDAA Total Total • Save CPU resources Row s Rows Query Reviewed Returned Hours Sec(s) Hours Sec(s) • People time Query 1 2,813,571 853,320 2:39 9,540 0.0 5 1,908 Query 2 2,813,571 585,780 2:16 8,220 0.0 5 1,644 • New Business Query 3 8,260,214 274 1:16 4,560 0.0 6 760 opportunities Query 4 2,813,571 601,197 1:08 4,080 0.0 5 816 Query 5 3,422,765 508 0:57 4,080 0.0 70 58 Query 6 4,290,648 165 0:53 3,180 0.0 6 530 Query 7 361,521 58,236 0:51 3,120 0.0 4 780 Query 8 3,425.29 724 0:44 2,640 0.0 2 1,320 Query 9 4,130,107 137 0:42 2,520 0.1 193 13 DB2 Analytics Accelerator: “we had this up and running in days with queries that ran over 1000 times faster” DB2 Analytics Accelerator: “we expect ROI in less than 4 months” Advance to 32 minute mark for DB2 Analytics Accelerator section of keynote Accelerating decisions to the speed of business8 12 October 2012 Actual customer results, October 2011 © 2009 IBM Corporation
  • 9. IBM DB2 Analytics Accelerator V3 Product Components Netezza zEnterprise Technology CLIENT Data Studio Foundation DB2 Analytics Accelerator Network Admin Plug-in OSA- Primary Express3 10Gb 10 GbE Backup IBM DB2 Data Warehouse application Analytics DB2 for z/OS enabled for IBM Users/ Acelerator DB2 Analytics Accelerator Applications9 © 2009 IBM Corporation
  • 10. Deep DB2 Integration within zEnterprise Applications DBA Tools, z/OS Console, ... Application Interfaces Operational Interfaces (standard SQL dialects) (e.g. DB2 Commands) DB2 for z/OS IBM Data Buffer Log DB2 Manager Manager ... IRLM Manager Analytics Accelerator Superior availability Superior reliability, security, z/OS on performance on Workload management System z analytic queries Netezza10 © 2009 IBM Corporation
  • 11. TMAccelerator powered by Netezza 1000 Appliance Slice of User Data Swap and Mirror partitions High speed data streaming High compression rate EXP3000 JBOD Enclosures 12 x 3.5” 1TB, 7200RPM, SAS (3Gb/s) Disk Enclosures max 116MB/s (200-500MB/s compressed data) e.g. 1000-12: 8 enclosures → 96 HDDs(32/128 TB) Accelerator Server SMP Hosts SQL Compiler, Query Plan, Optimize, Administration 2 front/end hosts, IBM 3650M3 or 3850X5 clustered active-passive 2 Nehalem-EP Quad-core 2.4GHz per host Snippet BladesTM (S-Blades, SPUs) Processor & streaming DB logic High-performance database engine streaming joins, aggregations, sorts, etc. © 2009 IBM Corporation
  • 12. S-Blade™ Components Dual-Core FPGA 8 FPGA Processors/Blade Netezza DB Accelerator Intel Quad-Core 8 Cores/Blade IBM BladeCenter Server © 2009 IBM Corporation
  • 13. Eliminating the I/O BottleneckMove the SQL to the hardware to where the data lives “Just send the Answer, not Raw Data” © 2009 IBM Corporation
  • 14. select DISTRICT,PRODUCTGRP,The Key to the Speed sum(NRX) from MTHLY_RX_TERR_DATA where MONTH = 20091201 and MARKET = 509123 and SPECIALTY = GASTRO FPGA CPU Core Core Zone Map Complex ∑ Restrict, Project Joins, Aggs, etc. Uncompress Visibility Slice of table MTHLY_RX_TERR_DATA (compressed) sum(NRX) where MONTH = 20091201 select DISTRICT, and MARKET = 509123 PRODUCTGRP, and SPECIALTY = GASTRO sum(NRX) © 2009 IBM Corporation
  • 15. Bringing Netezza AMPPTM Architecture to DB2 for z/OSAMPP = Asymmetric Massively Parallel Processing CPU FPGAAdvanced MemoryAnalytics BI SMP CPU FPGA Host DB2 for z/OS Memory LegacyReporting CPU FPGA DBA Memory Network Disk Fabric S-Blades™ Enclosures IBM DB2 Analytics Accelerator © 2009 IBM Corporation
  • 16. Query Execution Process Flow Application Optimizer Interface SPU CPU FPGA Memory Accelerator DRDA Requestor SPU CPU FPGA SMP Host MemoryApplication SPU Query execution run-time for CPU FPGA queries that cannot be or should Memory not be off-loaded to Accelerator SPU CPU FPGA Memory DB2 for z/OS DB2 Analytics Accelerator Queries executed without DB2 Analytics Accelerator Queries executed with DB2 Analytics Accelerator © 2009 IBM Corporation
  • 17. Workload-Optimized Query Execution • Single and unique system DB2 for z/OS and for mixed query workloads IBM DB2 Analytics Accelerator • Dynamic decision for most OLTP-like query OLTP-like query efficient execution platform User control and DB2 heuristic • New special register DB2 Native QUERY ACCELERATION DB2 Native Light ODS- Light ODS- Processing Processing – NONE query query – ENABLE – ENABLE WITH FAILBACK • New heuristic in DB2 Light BI Query Light BI Query optimizer Heavy BI Query Heavy BI Query Optimized processing for BI Workload © 2009 IBM Corporation
  • 18. Accelerator Data Load DB2 for z/OS Accelerator Table A Table B CPU FPGA Part 1 Unload USS Pipe Accelerator Administrative Stored Memory Table CAccelerator CPU FPGA Procedures Studio Table D Part 2 Unload USS Pipe Coordinator Memory Part 1 . . . CPU FPGA . . . Memory Part 2 . . . CPU FPGA Part 3 Unload USS Pipe Part m Memory • 1 TB / h – can vary, depending on CPU resources, table partitioning, • Update on table partition level, concurrent queries allowed during load • V2.1 & V3 unload in DB2 internal format, single translation by accelerator © 2009 IBM Corporation
  • 19. DB2 Analytics Accelerator V3Lowering the Costs of Trusted Analytics What’s New? • zEnterprise EC12 Support • High Performance Storage Version 3 will support the zEnterprise Saver EC12, z196 and z114 System z platforms Store a DB2 table or partition of data solely on the Accelerator. Removes • Query Prioritization the requirement for the data to be Brings System z workload replicated on both DB2 and the management down to the individual Accelerator query being routed to the Accelerator • Incremental Update • High Capacity Enables tables within the Accelerator Support has been extended to include to be continually updated throughout the entire Netezza 1000 line (1.28 PB) the day. • UNLOAD Lite Reduces z/OS MIPS consumption, by moving the preparation off System z.19 © 2009 IBM Corporation
  • 20. Build a System z Trusted Analytic SystemReduce the cost of host storage for historical data by 95%!Historical High Performance Low Latency DataMost data in an analytic All aggregate queries run Tables and partitions thatsystem is historical and not at the same high speed as require updating will besubject to change. Most any accelerator supported able to be updated bydata can be in a Storage query incremental update, tableSaver and maintain trusted load or partition loadperformance and security © 2009 IBM Corporation
  • 21. High Performance Storage SaverReducing the cost of high speed storage Store historic data on the Accelerator only Applications Tables can be resident on: 1. DB2 Only 2. DB2 and Accelerator 3. Accelerator Only SQL When data no longer requires updating, reclaim DB2 DB2 the DB2 storage Accelerator Table A Table A Table A Special Registers control behavior High speed High speed CURRENT QUERY ACCELERATION indexed aggregate lookups, best Accelerator lookups, best for CURRENT GET_ACCEL_ARCHIVE for OLTP Table A complex DSS type type queries queries Managed by zParms21 Mixed workload type queries © 2009 IBM Corporation
  • 22. Save Over 95% of Host Disk Space for Historical Data Historical Data Year Year -1 Year -2 Year -3 Year -4 Year -5 Year -7 1Q 1Q 1Q 1Q 1Q 1Q 1Q 2Q 2Q 2Q 2Q 2Q 2Q 2Q 3Q 3Q 3Q 3Q 3Q 3Q 3Q 4Q 4Q 4Q 4Q 4Q 4Q Current Data 4Q One Quarter = 3.57% of 7 years of data One Month = 1.12% of 7 years of data One month = 2.78% of 3 years of data © 2009 IBM Corporation
  • 23. High Performance Storage Saver Reducing the cost of high speed storage Time-partitioned tables where: – only the recent partitions are used in a transactional context (frequent data changes, short running queries) – the entire table is used for analytics (data intensive, complex queries). DB2 partitions are deleted after the High Performance Storage Saver are created on the accelerator DB2 No longer present on DB2 StorageQuery fromApplication #1 Or Accelerator Accelerator Accelerator Accelerator Accelerator Accelerator Accelerator #1 #2 #3 #4 #5 #6 #7 23 © 2009 IBM Corporation
  • 24. The Evolution of a High Performance Storage SaverHigh Speed Access to Historical Data Table / Data Accelerator Accelerator Archive Creation Load / Update /IU Only Only DB2 DB2 Accelerator Table A Table A Table A Accelerator Table A Backup Backup24 © 2009 IBM Corporation
  • 25. Storage options to match data needsOptimized in both price and performance for differing workloads High Performance Storage Saver Database Resident Partitions Single Disk Store Dual Disk Store • Only stored on Accelerator storage (Less • Stored on both DB2 and Accelerator Cost) storage • Optimized performance for • Mixed query workload with transactions, deep analytics, multifaceted, reporting single record queries and record updates and complex queries with deep analytics, multifaceted, • Only full table update or full partition reporting and complex queries. update from backup • Full table, full partition update, Incremental • Same high speed query access update from DB2 data transparently through DB2 • Same high speed query access transparently through DB2 Cost The right mix of cost and functionality Functionality © 2009 IBM Corporation25
  • 26. The zEnterprise Hybrid SolutionMixed Workloads for Next Generation Business Analytics Operational Analytic Mixed Workload Applications Applications Applications Transaction Processing Data warehousing Operational BI Shared Everything DB Shared Nothing DB Hybrid DB High volume business Low volume complex High volume business transactions and batch queries context switching transactions and batch reporting running reporting running concurrently concurrently with complex queries26 © 2009 IBM Corporation
  • 27. Incremental Update Table or ELT or ETL Partition Update OLTP Data DB2 Analytics Application Warehouse Accelerator Data Incremental Replication Update Synchronizing data to lower data latency from days to minutes/seconds27 © 2009 IBM Corporation
  • 28. Option 1: Full Table Refresh Changes in data warehouse tables typically driven by scheduled (nightly or more frequently) ETL process Data used for complex reporting based on consistent and validated content (e.g., weekly Operational Analytics, Reports, OLAP, Operational Analytics, Reports, OLAP, transaction reporting to the central bank) Multiple sources or complex transformations Continuous Continuous Query prevent propagation of incremental changes Query Processing Processing Full table refresh triggered through DB2 stored procedure (scheduled, integrated into ETL DB2 z/OS Query Optimizer DB2 z/OS Query Optimizer process or through GUI) DB2 native DB2 native Accelerator Accelerator processing processing processing processing ETL Process Queries may continue ETL Process during full table refresh Full table refresh for accelerator DB2 for z/OS database DB2 for z/OS database Changes / Replacement © 2009 IBM Corporation
  • 29. Option 2: Table Partition Refresh Changes in data warehouse table typically driven by “delta” ETL process (considering only changes in source tables compared to previous runs) or by more frequent changes to most recent data Optimization of Option 1 when target data warehouse table is partitioned and most recent updates are only applied to the latest partition Operational Analytics, Reports, OLAP, Operational Analytics, Reports, OLAP, Table partition refresh triggered through DB2 stored procedure (scheduled, integrated into Continuous Continuous ETL process or through GUI) Query Query Processing Processing Maintains snapshot DB2 z/OS Query Optimizer DB2 z/OS Query Optimizer semantics for consistent reports Queries may continue DB2 native DB2 native Accelerator Accelerator Replication Replication processing processing processing processing during table partition refresh for accelerator January February March ETL Process ETL Process April May Partition refresh Changes DB2 for z/OS database © 2009 IBM Corporation DB2 for z/OS database
  • 30. Option 3: Incremental Update Changes in data warehouse tables typically driven by replication or manual updates – Corrections after a bulk-ETL-load of a data warehouse table – Continuously changing data (e.g. trickle-feed updates from a transactional system to an ODS) Reporting and analysis based on most recent Operational Analytics, Reports, OLAP, Operational Analytics, Reports, OLAP, data May be combined with Option 1 & 2 (first table Continuous Continuous refresh and then continue with incremental Query Query Processing Processing updates) DB2 z/OS Query Optimizer DB2 z/OS Query Optimizer Application Application DB2 native DB2 native Accelerator Accelerator Incremental update can be processing processing processing processing configured per database table Replication Replication Incremental Update Changes DB2 for z/OS database DB2 for z/OS database © 2009 IBM Corporation
  • 31. Now expandable to 960 cores and 1.28 petabytes 1 10 ....... 002 005 010 015 020 030 040 060 060 100 Cabinets 1/4 1/2 1 1 1/2 2 3 4 6 8 10 Processing Units 24 48 96 144 192 288 384 576 768 960 Capacity (TB) 8 16 32 48 64 96 128 192 256 320 Effective 32 64 128 192 256 384 512 768 1024 1280 Capacity (TB)* PureData System for Analytics Predictable, Linear Scalability throughout entire family Capacity = User Data space Effective Capacity = User Data Space with compression *: 4X compression assumed Low Latency, High Capacity Update © 2009 IBM Corporation
  • 32. Connectivity Options DB2 DB2 Multiple DB2 systems can connect to a single Accelerator A single DB2 system can connect to multiple Accelerators DB2 Multiple DB2 systems can connect to multiple Accelerators DB2 DB2 The same table can be stored in the multiple Accelerators (except High Performance Storage Saver tables) Full flexibility for DB2 systems: • residing in the same LPAR Better utilization of Accelerator resources • residing in different LPARs • residing in different CECs Scalability • being independent (non-data sharing) High availability • belonging to the same data sharing group • belonging to different data sharing groups © 2009 IBM Corporation32
  • 33. Analytics Accelerator Table Definition and Deployment IBM Data Studio Client DB2 for z/OS DB2 Analytics Accelerator Accelerator Accelerator Administrative Netezza Catalog Studio Stored Procedures DB2 Catalog The tables need to be defined and deployed to the Accelerator before data is loaded and queries sent to it for processing. Definition: identifying tables for which queries need to be accelerated Deployment: making tables known to DB2, i.e. storing table meta data in the DB2 and Netezza catalog. IBM DB2 Analytics Accelerator Studio guides you through the process of defining and deploying tables, as well as invoking other administrative tasks. IBM DB2 Analytics Accelerator Stored Procedures implement and execute various administrative operations such as table deployment, load and update, and serve as the primary administrative interface to the Accelerator from the outside world including Accelerator Studio.33 © 2009 IBM Corporation
  • 34. Shielding Against Disk Failures Primary Mirror Temp • All user data and temp space mirrored • Disk failures transparent to queries and transactions • Failed drives automatically regenerated • Bad sectors automatically rewritten or relocated © 2009 IBM Corporation
  • 35. Shielding Against S-BladeTM Failures . . . . . . . . . . . . . . . S-Blades • S-Blade failure is automatically detected © 2009 IBM Corporation
  • 36. Shielding Against S-BladeTM Failures . . . . . . . . . . . . . . . S-Blades • Drives automatically reassigned to active S-Blades within a chassis • Read-only queries (that have not returned data yet) automatically restarted • Transactions and loads interrupted • Loads automatically restarted from last successful checkpoint © 2009 IBM Corporation
  • 37. Disaster Recovery Option 1 – Table Loaded in One Accelerator (1 of 2) SYSPLEXApp 1 DSG Member 1 DSG Member 2 Tables Tables App 4 of App 4 of App 5App 2 Tables Tables Tables App 5App 3 of App 1 of App 2 of App 3 Short Range Short Range Long Switch Range Switch Short Range Short Range Accelerator Instance 1 Accelerator Instance 2 Tables Created but Not Loaded Tables Tables Tables of App 1 of App 2 of App 3 Tables Tables Tables Tables Tables of App 1 of App 2 of App 3 of App 4 of App 5 © 2009 IBM Corporation
  • 38. Disaster Recovery Option 1 – Table Loaded in One Accelerator (2 of 2) App 1 SYSPLEXApp 1 DSG Member 1 DSG Member 2 App 2 Tables Tables of App 4 of App 5App 2 App 3 Tables Tables TablesApp 3 of App 1 of App 2 of App 3 App 4 Short Range Short Range Long App 5 Switch Range Switch Short Range Short Range Accelerator Instance 1 Accelerator Instance 2 Tables Tables Tables of App 1 of App 2 of App 3 Already Created Tables Tables Tables Must LOAD Tables Tables of App 1 of App 2 of App 3 of App 4 of App 5 © 2009 IBM Corporation
  • 39. Disaster Recovery Option 2– Table Loaded in Two Accelerators (1 of 2) SYSPLEX App 1 DSG Member 1 DSG Member 2 Tables Tables App 4 of App 4 of App 5 App 2 Tables Tables Tables App 5 App 3 of App 1 of App 2 of App 3 Short Range Short Range Long Switch Range Switch Short Range Short Range Accelerator Instance 1 Accelerator Instance 2 Tables Tables Tables Tables Tables Tables of App 1 of App 2 of App 3 of App 1 of App 2 of App 3 Tables Tables Tables Tables of App 4 of App 5 of App 4 of App 5 © 2009 IBM Corporation
  • 40. Disaster Recovery Option 2 – Table Loaded in Two Accelerators (2 of 2) App 1 SYSPLEX App 1 DSG Member 1 DSG Member 2 App 2 Tables Tables of App 4 of App 5 App 2 App 3 Tables Tables Tables App 3 of App 1 of App 2 of App 3 App 4 Short Range Short Range Long App 5 Switch Range Switch Short Range Short Range Accelerator Instance 1 Accelerator Instance 2 Data Already Available Tables Tables Tables Tables Tables Tables of App 1 of App 2 of App 3 of App 1 of App 2 of App 3 Tables Tables Tables Tables of App 4 of App 5 of App 4 of App 5 © 2009 IBM Corporation
  • 41. Why Both?Marrying the best of both worlds IBM IBM PureData N1001 System z Focused Appliance Mixed Workload System Capitalizing on the strengths of both platforms while driving to the most cost effective, centralized solution - destroying the myth that transaction and decision systems had to be on separate platforms Very focused workload Very diverse workload © 2009 IBM Corporation
  • 42. Tailored to your needsA Hybrid Solution IBM IBM System z with Netezza IBM DB2 Analytics Accelerator Focused Appliance Mixed Workload System • Mixed workload system z with operational • Appliance with a streamlined transaction systems, data warehouse, database and HW acceleration for operational data store, and consolidated performance critical functionality data marts. • Price/performance leader • Unmatched availability, security and recoverability • Speed and ease of deployment and • Natural extension to System z to enable administration pervasive analytics across the • Optimized performance for organization. deep analytics, multifaceted, reporting • Speed and ease of deployment and and complex queries administration True Appliance Flexible Integrated System Custom Solution Simplicity The right mix of simplicity and flexibility Flexibility © 2009 IBM Corporation
  • 43. Next Steps Opportunity Operational New Workload Data Reporting Consolidation Operational BI (Accelerator) or Winback (ISAS + (ISAS + (ISAS + OR Accelerator) Accelerator) Accelerator) Existing z Warehouse Warehouse Workload Collaboration Assessment Workshop Optional POC © 2009 IBM Corporation43
  • 44. The Ultimate Consolidation Platform Data Mart Data Mart Data Mart Data Mart Bringing it all together • Better Business Response Data Mart Consolidation • Reduced Costs System z PR/SM Recognized leader in mixed • More Available virtualization and workload isolation • More SecureTransaction Systems • Reduced Data Movement (OLTP) • Better Governance • Reduced Data Latency • Reduced Complexity Data Warehousing • Reduced Resources z/OS: Netezza: Business Intelligence Recognized leader in mixed Recognized leader in Predictive Analytics workloads with security, cost-effective high availability speed deep analytics and recoverability Together: Destroying the myth that transactional and decision support workloads have to be on separate platforms44 © 2009 IBM Corporation
  • 45. Learn More Visit the Data Warehousing & Business Analytics Webpage http://www.ibm.com/software/data/businessintelligence/systemz/ © 2009 IBM Corporation
  • 46. Ed Lynch System z Data Warehousing & Business Analytics edlynch@us.ibm.com46 © 2009 IBM Corporation