Deliver Rich Analytics with Analysis Services SQL Server Donald Farmer Group Program Manager Microsoft Corporation

Agenda  Analysis Services Overview and What’s new  Demo  Data Mining  Demo  Managing and Deploying Analysis Services  Scalability, Availability, Serviceability, Manageability  Summary

Analysis Services Why OLAP and Data Mining Matter  Powerful business information modeling  Cross platform data integration  Integrated Relational & OLAP views  The best of MOLAP to ROLAP  Data enrichment and advanced analytics  Key Performance Indicators and Perspectives  Real-time, high performance  Real-time data in OLAP Cubes  Very fast and flexible analytics  XML standards for Data Access and Web Services integration  Cost and time savings for customers integrating with other systems

What Is SQL Server 2005 Analysis Services? Dashboards Rich Reports BI Front Ends Spreadsheets Ad Hoc Reports SQLServer Teradata OracleDB2 LOB DW Datamart Analysis Services  Data enrichment and advanced analytics  Real-time and high performance  Mission critical

Dashboards Rich Reports BI Front Ends Spreadsheets Ad Hoc Reports Analysis Services Cache XML/A or ODBO UDM SQLServer Teradata OracleDB2 LOB DW Datamart Analysis Services High-level Architecture

SQLServer Teradata OracleDB2 LOB DW Datamart Analysis Services Cache UDM XML/A or ODBO

 Business Intelligence Enhancements  Auto generation of time and other dimensions based on type  KPIs, MDX scripts, translations, currency…  Data Mining  10 Mining Algorithms  Smart applications  XML standards for Data Access & Web services integration  $$ saving for customers integrating our solution with other systems  Unified Dimensional Model  Powerful business information modeling  Cross platform data integration  Integrated Relational & OLAP views  KPIs & Perspectives  Proactive caching  Real-time data in OLAP Cubes  Very fast and flexible analytics SQL Server Analysis Services New Paradigm for the Analytics Platform

The Unified Dimensional Model

Value of Data Mining  8 new algorithms, 10 in total  Graphical tools/wizards  12 embeddable viewers  SQL Server 2005 makes it easier  Tightly integrated with AS, DTS, Reporting  Integration with Web/Office apps SQL Server 2005 OLAP Reports (Ad Hoc) Reports (Static) Data Mining Business Knowledge Easy Difficult Usability Relative New Business Insight

Complete Set of Algorithms Decision Trees Clustering Time Series Sequence Clustering Association Naïve Bayes Neural Net Introduced in SQL Server 2000 LogisticRegression Linear Regression Text Mining

Putting Data Mining to Work Task Microsoft Algorithms Predicting a discrete attribute. For example, to predict whether the recipient of a targeted mailing campaign will buy a product. Decision Trees Naive Bayes Clustering Neural Network Logistic Regression Linear Regression Predicting a continuous attribute. For example, to forecast next year's sales. Decision Trees Time Series Predicting a sequence. For example, to perform a clickstream analysis of a company's Web site. Sequence Clustering Finding groups of common items in transactions. For example, to use market basket analysis to suggest additional products to a customer for purchase. Association Rules Decision Trees Finding groups of similar items. For example, to segment demographic data into groups to better understand the relationships between attributes. Clustering Sequence Clustering

Mining for Meaning

BI App lifecycle Dev Server Test Server BI Dev Studio Management Studio Dev/TestProductionDeployInspectAutomate: data updates data updates permission updates permission updatesMonitorVersion DesignDevelop Debug Build DeployVersion Prod Server

Scalability  Fully centralized calculations on the server  No calculations done on client  No excess data transported to the client  Cached calculation  Ability to cache calculation on disk  Disk based dimension storage  Dimension size is not constrained by memory limits  150 million members already tested  Role playing dimensions remove need for duplicating dimension storage  Attribute-based hierarchies  Remove need for duplicate info among hierarchies sharing common attributes

Availability  Failover Clustering  Multi-Instances  Very easy deployment – no registry entries needed  Server Synching  Designed for dual machines configurations – number cruncher machine and end-user facing machine  Allow:  Processing the calculations isolated from user interactions  Isolated verification of the results  Incremental and transactional synching of the query machine with the new results  Enhanced backup and restore  Unlimited partition sizes

Serviceability  Trace events (with Profiler Integration)  Flight recorder (repro-less diagnostics)  Records server activity and metrics at all times  Provides diagnostics in the event of system failure  Allows replay of a failure condition  ON by default  Capture and Replay  Customers use to diagnose performance  Provides PSS a simple means to repro problems  Test exploit in Labs  Dr Watson

Manageability  Integrated management experience with SQL Server  Single management shell  SQL profiler support  Query analyzer support  Strong integration with IS (DTS) for management tasks automation  Deployment packages to manage system life cycle  Dev → Test → Production  Source control integration for system versioning  Team development facilities  Fine grain administration roles  Database level administration  Permissions: Creation, R/O, Processing  XML-based DDL for easy scripting  Auto referential integrity handling for dealing with dirty input data

Summary  Large investment in abilities in SSAS:  Manageability  Supportability  Security  Availability  Unified tools with SQL Server  SQL DBAs skill set can be applied to administering Analysis Server  BI platform for 24/7 operations

© 2005 Microsoft Corporation. All rights reserved. This presentation is for informational purposes only. Microsoft makes no warranties, express or implied, in this summary.