2. Microsoft Data Warehousing Vision SQL Server 2008 R2 DW enhancements High speed connectors Change Data Capture Star join enhancement features Partition table performance Data compression Backup compression Resource management Summary

3. HR Financial/ Accounting ERP CRM and eCRM Internet Procurement CallCenter Inventory Islands of information

4. Enterprise Data Warehouse HR Financial/ Accounting ERP CRM and eCRM Internet Procurement CallCenter Inventory

5. Complete Data Warehouse Solution Flexibility and Choice Massive Scalability at a Low Cost Make SQL Server the fastest and most affordable database for customers of all sizes Simplified Data Warehouse Management

6. Tier 1 Offerings Tier 1 Services and Support EnterpriseEnterprise Data Center Fast Track Data Warehouse Parallel Data Warehouse Scalable and reliable platform for data warehousing on any hardware Reference architectures offering best price performance for data warehousing Appliance for high-end data warehousing requiring highest scalability, performance, or complexity Ideal for data marts or small to mid-sized enterprise data warehouses (EDWs) Ideal for data marts or small to mid-sized EDWs Ideal for data marts or small to mid-sized data warehouses with scan-centric workloads Offers flexibility in hardware and architecture Software only Reference architecture (software and hardware) Data warehouse appliance (fully integrated software and hardware) Scale up data warehousing Scale out data warehousing with massively parallel processing (MPP) 10s of terabytes 4 – 48 terabytes 10s–100s of terabytes $28.8K per processor $9.9K per server $162 per CAL $57.5K per Proc only $107K – $683K (2–4 Procs; includes hardware) $1.5–$1.7 million per rack (includes hardware) $13K – 44K per terabyte

7. Tier 1 Offerings Tier 1 Services and Support EnterpriseEnterprise Data Center Fast Track Data Warehouse Parallel Data Warehouse Scalable and reliable platform for data warehousing on any hardware Reference architectures offering best price performance for data warehousing Appliance for high-end data warehousing requiring highest scalability, performance, or complexity Ideal for data marts or small to mid-sized enterprise data warehouses (EDWs) Ideal for data marts or small to mid-sized EDWs Ideal for data marts or small to mid-sized data warehouses with scan-centric workloads Offers flexibility in hardware and architecture Software only Reference architecture (software and hardware) Data warehouse appliance (fully integrated software and hardware) Scale up data warehousing Scale out data warehousing with massively parallel processing (MPP) 10s of terabytes 4 – 48 terabytes 10s–100s of terabytes $28.8K per processor $9.9K per server $162 per CAL $57.5K per Proc only $107K – $683K (2–4 Procs; includes hardware) $1.5–$1.7 million per rack (includes hardware) $13K – 44K per terabyte Integrated ETL and Reporting tools Integrated ETL and Reporting tools Simplified management Simplified management Predictable response Predictable response Lower storage costs Lower storage costs Integrated Master Data Management tool Integrated Master Data Management tool Ability to scale up to 256 logical processors Ability to scale up to 256 logical processors Continuous loading using StreamInsight Continuous loading using StreamInsight Integrated ETL and Reporting tools Integrated ETL and Reporting tools Simplified management Simplified management Predictable response Predictable response Lower storage costs Lower storage costs Integrated Master Data Management tool Integrated Master Data Management tool Ability to scale up to 256 logical processors Ability to scale up to 256 logical processors Continuous loading using StreamInsight Continuous loading using StreamInsight

8. Provide a centralized repository of consistent data Provide up-to-date data to all employees to enable intelligent decision-making Process large amounts of data in a fast, efficient, and affordable manner Answer complex queries quickly Guarantee predictable performance Aggregate data from multiple sources Store data efficiently Guarantee predictable performance Aggregate data from multiple sources Store data efficiently Guarantee predictable performance High-speed connectors Star join query optimizations Data and backup compression Change Data Capture Policy based management Table partitioning Partitioned table parallelism Resource Governor

9. High speed connectors Attunity high speed connectors for Oracle and Teradata Deliver unparalleled throughput for extracting and loading data to and from Oracle and Teradata. Change data capture Enables tracking changes to the data in tables Provides relatively low impact on performance Speed updates to data warehouses by capturing net changes Data Warehouse CHANGECHANGE INSERTINSERT UPDATEUPDATE 11001 01001 01001 01001 ETL Evidence The CDC feature gives us the information we need and frees us from the task of creating and testing triggers.” — Gerald Schinagl, Project Manager and Systems Architect for the Sports Database, Austrian Broadcasting Corporation Radio & Television (ORF) The CDC feature gives us the information we need and frees us from the task of creating and testing triggers.” — Gerald Schinagl, Project Manager and Systems Architect for the Sports Database, Austrian Broadcasting Corporation Radio & Television (ORF) “ “

10. Star join optimizations Process more data in a shorter time by optimizing common join scenarios in a data warehouse Significantly reduce the amount of processing for star schema queries Faster join processing speeds up lookups during data load, which shortens load windows and enables more frequent updates for better reporting Evidence In addition to faster query processing, ORF has found an immediate improvement of 15 percent in data loading. We consider that a great advantage when you can get 15 percent faster data loading without having to change a line of our own code.” — Gerald Schinagl, Project Manager and Systems Architect, ORF In addition to faster query processing, ORF has found an immediate improvement of 15 percent in data loading. We consider that a great advantage when you can get 15 percent faster data loading without having to change a line of our own code.” — Gerald Schinagl, Project Manager and Systems Architect, ORF DIMENSION TABLE FACT TABLE Rows Returned 1,000,000623,194 “ “

11. Table Partitioning Manage and access subsets of data quickly and efficiently Reduce time spent troubleshooting storage allocation issues Speed data load and maintenance operations Take advantage of all CPUs in the machine to complete operations more quickly Evidence Enhancements in partition query dramatically reduce the effects of lock escalation on systems that have to process hundreds and thousands of transactions per second, improving availability and improv[ing] query response time. —Randy Dyess, SQL Server Mentor, TechNet Article Enhancements in partition query dramatically reduce the effects of lock escalation on systems that have to process hundreds and thousands of transactions per second, improving availability and improv[ing] query response time. —Randy Dyess, SQL Server Mentor, TechNet Article “ “

12. Partitioned Tables Parallelism Reduce access times for large amounts of data by querying all partitions in parallel Take advantage of all CPUs in the machine to give results more quickly Evidence Enhancements in partition query dramatically reduce the effects of lock escalation on systems that have to process hundreds and thousands of transactions per second, improving availability and improv[ing] query response time. —Randy Dyess, SQL Server Mentor, TechNet Article Enhancements in partition query dramatically reduce the effects of lock escalation on systems that have to process hundreds and thousands of transactions per second, improving availability and improv[ing] query response time. —Randy Dyess, SQL Server Mentor, TechNet Article “ “

13. Data compression 20% to 60% compression ratios 1 Save disk storage Provides more room to store more data, which allows more instances to share disk resources Reduced data size can increase performance 1001010 0101001 0100001 1111011 0101001 1 Stated percentages are typical but not guaranteed Evidence Our initial testing shows we’ll see 50 percent to 60 percent data compression using SQL Server 2008...we will also benefit from faster query performance.” — Mazal Tuchler, BI Manager, Clalit Health Services Our initial testing shows we’ll see 50 percent to 60 percent data compression using SQL Server 2008...we will also benefit from faster query performance.” — Mazal Tuchler, BI Manager, Clalit Health Services “ “

14. Backup compression 50% to 90% compression ratios 1 Reduce the cost for disks and tapes used to backup data Smaller backups can be taken offsite more easily to protect data Increase administrator productivity Smaller backups usually increase backup and restore speed resulting in higher availability 1 Stated percentages are typical but not guaranteed Evidence We’re anticipating an 80 percent reduction in our backup file sizes using backup compression on SQL Server 2008.” — Peter Hammond, President, CyberSavvy We’re anticipating an 80 percent reduction in our backup file sizes using backup compression on SQL Server 2008.” — Peter Hammond, President, CyberSavvy “ “

15. Central management servers Manage Relational Databases, Analysis Services, Reporting Services, Notification Services, SQL Server Mobile Edition using one management tool Simplify maintenance by executing commands simultaneously on multiple servers See integrated results when you query data from a group of servers Policy-based management Easily create policies that control security, database options, object naming conventions, and other settings at a highly granular level Evidence Policy-based Management gives us the ability to enforce naming standards, security settings, memory settings, and other elements to simplify database management —Glenn Berry Database Architect, NewsGator Technologies Policy-based Management gives us the ability to enforce naming standards, security settings, memory settings, and other elements to simplify database management —Glenn Berry Database Architect, NewsGator Technologies “ “

16. Resource Governor Prevent runaway queries that hold resources for extended periods of time Allow OLTP and data warehouse workloads on the same server while limiting the impact of large data warehouse queries on OLTP Provide consistent user experience, which can result in fewer service calls about slow systems Applications & Business Logic POOL 1 POOL 2 POOL 0 LIMIT 50% LIMIT 30% LIMIT 20% 1100101 00101 1100101 00101 110010 LOAD 25% 1100101 00101 1100101 00101 110010 LOAD 45% 1100101 00101 1100101 00101 110010 LOAD 15% Evidence We deal with a lot of large data feeds—both coming from manufacturers as data updates, and going out to our subscribers. Resource Governor allows us to control the percent[age] of total resources any operation can consume so that they don’t adversely impact our real-time data access.” — Michael Steineke, Vice President, Information Technology, Edgenet We deal with a lot of large data feeds—both coming from manufacturers as data updates, and going out to our subscribers. Resource Governor allows us to control the percent[age] of total resources any operation can consume so that they don’t adversely impact our real-time data access.” — Michael Steineke, Vice President, Information Technology, Edgenet “ “

17. Maximum number of processors Support for most powerful servers, scaling up to OS maximum Can execute parallel index and consistency check operations Query optimizer makes operations parallel when there is a benefit Standard only uses single CPU for index and consistency check operations Evidence “For most large queries SQL Server generally scales linearly or nearly linearly. For speed up, this means that if we double the number of CPUs, we see the response time drop in half.” —Craig Freedman, Coauthor, Inside Microsoft SQL Server 2005: Query Tuning and Optimization “For most large queries SQL Server generally scales linearly or nearly linearly. For speed up, this means that if we double the number of CPUs, we see the response time drop in half.” —Craig Freedman, Coauthor, Inside Microsoft SQL Server 2005: Query Tuning and Optimization “ “

18. 2008Beyond2010 Enterprise ETL Services Star Join Query Optimizations Data Compression Partitioned table parallelism Scale up to 256 logical processors Data compression for Unicode columns Master Data Management (Stratature Integration) Continuous Loading Preliminary Information Subject to Change Data Quality Services (Zoomix) Enhanced ETL capabilities vNext

19. Learn More: Visit the Microsoft Data Warehousing PortalMicrosoft Data Warehousing Portal Visit the Fast Track and Parallel Data Warehouse web pagesFast TrackParallel Data Warehouse Visit the SQL Server DW Portal on TechNetDW Portal Try Now: DownloadDownload SQL Server 2008 R2 Talk to your Microsoft representative about scheduling: Data Warehouse roadmap service SQL Server 2008 R2 POC

20. Microsoft SQL Server provides a comprehensive, scalable data warehouse platform that enables organizations to: Build data warehouses faster on the data integration platform. Manage growing data volumes with an enterprise-ready relational database. Deliver actionable, integrated insights with the Microsoft Business Intelligence platform.

21. © 2009 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

22. Heterogeneous Connectivity & Workloads Data Integrity & Quality Compliance & Security Data Warehouse Scale Data Warehouse Management 2005 2008 Futures PB Warehouses >64 Core Processing Scale out through MPP Perf. Management Tools BI Resource Governance Improved Predictability Mixed workload support Continuous Loading Master Data Management (Stratature Integration) Integrated DQ Services (Zoomix) Rights Management 10s of TB Warehouses Parallel partitioning Data compression New Reference Architectures Policy Based Admin. DB Resource Governance High Perf. Connectors (Oracle, Teradata, SAP BW) Data Profiling Policy based auditing Multi TB Warehouses Enterprise scalability DW Reference Architectures Unified manageability Enterprise class ETL tool Data Cleansing (Fuzzy lookup/matching) Data Protection & Tracing