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  • The Analysis Services technology remains at the heart of Microsoft’s BI capabilities. It is now in its third generation and has delivered a wide set of new options – both functional & management/scalability focused. We’ll focus on three key areas: The UDM – a new approach to modeling the inputs to the OLAP capabilities of SQL Server Analysis Services that can help eliminate costly data staging areas and business-function specific data marts KPI – a new architecture for delivering goal-based metrics to the organization Deep Data Mining – moving beyond slice & dice and drilldown to provide tools that help you catch complex relationships and patterns in your data and predict outcomes based on past results.
  • The Analysis Services technology remains at the heart of Microsoft’s BI capabilities. It is now in its third generation and has delivered a wide set of new options – both functional & management/scalability focused. We’ll focus on three key areas: The UDM – a new approach to modeling the inputs to the OLAP capabilities of SQL Server Analysis Services that can help eliminate costly data staging areas and business-function specific data marts KPI – a new architecture for delivering goal-based metrics to the organization Deep Data Mining – moving beyond slice & dice and drilldown to provide tools that help you catch complex relationships and patterns in your data and predict outcomes based on past results.
  • There are aspects of Analysis Services that you can take advantage of immediately: 64-bit, Failover Clustering… Review the slide at the beginning if you want to talk to these points. However, the key point about Analysis Services is that it’s been significantly improved – in both areas OLAP and Data Mining – and it’s at the core of BI in general. The most important reason why you care – “one version of the truth.”
  • ODBO = OLEdb for OLAP XMLA = XML for Analysis
  • ODBO = OLEdb for OLAP
  • UDM: SQL Server 2005 introduces the Universal Data Model – this technology provides the mechanism to describe data sources in a BI-friendly manner without requiring changes to the source data. This can eliminate the need for staging areas as data can be consumed directly from the source systems. The models can then be used to drive multiple cubes (or live caches of the underlying data). By looking at the cubes as a series of high-performance caches the best of the OLAP & OLTP worlds can be combined. Cache: The cache is a MOLAP datastore that manages the retrieval of data from backend data sources. You can control how frequently the multidimensional cache is rebuilt, if stale data can be queried while the cache is being refreshed, and whether data is retrieved to a schedule or when it changes in the database. Business Intelligence Smarts: SQL Server 2000 included time dimension awareness – this is extended in SQL Server 2005 to cover autogeneration of several key calculations that really help you to jumpstart any BI system: Time – adds the following calculations: Period to date, Period over period growth, Moving average and Parallel period comparisons. Accounting – cost, balance & other accounting calculations Dimension – choose from a list of known dimension types and also automatically set additional attributes to autogenerate appropriate calculations Set your own aggregation operator or calculation and control updateability Data Mining: We’ll cover more on this later XMLA: SQL Server 2005 implements the open standard: XML for Analysis – optimized for scalable web access to access & define OLAP data.
  • BUILD {BASE} Data mining has long been regarded as extremely hard to do properly (the domain of high-paid Wall Street “rocket scientist” statisticians) but conversely as having huge potential business value because of its abilities to spot clusters and trends in large data volumes that a human would miss. {STATIC REPORTS} Are easy, predefined, canned reports with no inputs – they are a great operational tool but are unlikely to deliver new business insight {AD HOC REPORTS} Add more value as start to introduce drill-down-like capabilities, empowering data exploration, however they still tend to be limited in terms of navigating predefined relationships {OLAP} Represents a big leap in business value – drill through, slicing and dicing to compare data and the new capabilities of SQL Server 2005 provide real business insight {DATA MINING, pause then click to “move the arrow”} Long regarded as hard, SQL Server 2005 provides multiple technologies to ease its development, deployment and use
  • All data mining tools, including Microsoft SQL Server 2005 Analysis Services, use multiple algorithms. Analysis Services, of course, is extensible; third party ISVs can develop algorithms that snap in seamlessly to the Analysis Services data mining framework. Depending on the data and the goals, different algorithms are preferred, and each algorithm can be used for multiple problems.
  • The BI development workbench, integrated with Visual Studio, is a powerful tool for designing, developing, debugging BI applications in a team based environment. Developers usually work with a dev server. Once they have completed the BI application, it can be deployed using the workbench to the test server. After the application has gone through testing that tries to emulate the real user environment, we are entering the production part of the application lifecycle. This is something that is more familiar to our DBAs in the audience. This is what we will focus on in this presentation. The first task is to deploy from the test server to the production server (or multiple servers). Typically you want to do full copy of metadata, metadata and data, or incremental update of the definitions and data on the production server. Sometimes during incremental deployment, you need to resolve conflicts between the test and the production server, for example – you may want to preserve the partitions defined on the production server, but bring in the new calculations to the cube. In a moment we will show you several new features that will help you with deployment. After the application has been deployed, you may want to inspect the state of the application by browsing the objects on the production server and sending some ad-hoc queries. SQL Workbench is the new management shell that provides unified management experience for both SQL and Analysis servers. Usually in the production environment, you want to automate as many tasks as possible, for example – periodic processing of the data, creation of partitions, automatic update of security permissions on the cubes. As users are hitting the server, you want to monitor the state of the server – to see who is connected, which queries are running. Again we have made major improvements in this area. Finally, an important part of managing a production system is the ability to version the metadata. This is very useful in cases when recent modifications cause change in the system behavior. The ability go back in time and see what changed can save you lots of time in finding out what is causing the new issues. Transition: At this moment, after telling you many things that you already know and making lots of promises, let us show you a demo that demonstrates some of the improvements that we have made
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    1. 1. Deliver Rich Analytics with Analysis Services SQL Server Jason Carlson Product Unit Manger Microsoft Corporation
    2. 2. Agenda <ul><li>Analysis Services Overview and What’s new </li></ul><ul><li>Demo </li></ul><ul><li>Data Mining </li></ul><ul><li>Demo </li></ul><ul><li>Managing and Deploying Analysis Services </li></ul><ul><ul><li>Scalability, Availability, Serviceability, Manageability </li></ul></ul><ul><li>Summary </li></ul>
    3. 3. Analysis Services Why OLAP and Data Mining Matter <ul><li>Powerful business information modeling </li></ul><ul><li>Cross platform data integration </li></ul><ul><li>Integrated Relational & OLAP views </li></ul><ul><li>The best of MOLAP to ROLAP </li></ul><ul><li>Data enrichment and advanced analytics </li></ul><ul><li>Key Performance Indicators and Perspectives </li></ul><ul><li>Real-time, high performance </li></ul><ul><ul><li>Real-time data in OLAP Cubes </li></ul></ul><ul><ul><li>Very fast and flexible analytics </li></ul></ul><ul><li>XML standards for Data Access and Web Services integration </li></ul><ul><li>Cost and time savings for customers integrating with other systems </li></ul>
    4. 4. <ul><li>Unified Dimensional Model </li></ul><ul><li>Pro-active caching </li></ul><ul><li>Advanced Business Intelligence </li></ul><ul><ul><li>KPI/Perspectives </li></ul></ul><ul><ul><li>Custom/Limited Aggregations and Semi-Additive Measures </li></ul></ul><ul><li>Web services </li></ul><ul><li>Data Mining in the platform </li></ul><ul><li>Integrated Developer Tools </li></ul><ul><li>Failover Clustering </li></ul>Analysis Services Enhanced OLAP and Data Mining Capabilities Decision Trees Clustering Naïve Bayes Introduced in SQL Server 2000 <ul><li>plus… </li></ul><ul><li>Logistic Regression </li></ul><ul><li>Linear Regression </li></ul><ul><li>Text Mining </li></ul>Time Series Sequence Clustering Association Neural Net
    5. 5. What Is SQL Server 2005 Analysis Services? Dashboards Rich Reports BI Front Ends Spreadsheets Ad Hoc Reports SQL Server Teradata Oracle DB2 LOB DW Datamart Analysis Services <ul><li>Data enrichment and advanced analytics </li></ul><ul><li>Real-time and high performance </li></ul><ul><li>Mission critical </li></ul>
    6. 6. Analysis Services High-level Architecture Dashboards Rich Reports BI Front Ends Spreadsheets Ad Hoc Reports Analysis Services Cache SQL Server Teradata Oracle DB2 LOB DW Datamart XML/A or ODBO UDM
    7. 7. Analysis Services High-level Architecture SQL Server Teradata Oracle DB2 LOB DW Datamart Analysis Services Cache UDM XML/A or ODBO
    8. 8. <ul><li>Business Intelligence Enhancements </li></ul><ul><ul><li>Auto generation of time and other dimensions based on type </li></ul></ul><ul><ul><li>KPIs, MDX scripts, translations, currency… </li></ul></ul><ul><li>Data Mining </li></ul><ul><ul><li>10 Mining Algorithms </li></ul></ul><ul><ul><li>Smart applications </li></ul></ul><ul><li>XML standards for Data Access & Web services integration </li></ul><ul><ul><li>$$ saving for customers integrating our solution with other systems </li></ul></ul><ul><li>Unified Dimensional Model </li></ul><ul><ul><li>Powerful business information modeling </li></ul></ul><ul><ul><li>Cross platform data integration </li></ul></ul><ul><ul><li>Integrated Relational & OLAP views </li></ul></ul><ul><ul><li>KPIs & Perspectives </li></ul></ul><ul><li>Proactive caching </li></ul><ul><ul><li>Real-time data in OLAP Cubes </li></ul></ul><ul><ul><li>Very fast and flexible analytics </li></ul></ul>SQL Server Analysis Services New Paradigm for the Analytics Platform
    9. 9. The Unified Dimensional Model
    10. 10. Value of Data Mining <ul><li>8 new algorithms, 10 in total </li></ul><ul><li>Graphical tools/wizards </li></ul><ul><li>12 embeddable viewers </li></ul><ul><li>SQL Server 2005 makes it easier </li></ul><ul><li>Tightly integrated with AS, DTS, Reporting </li></ul><ul><li>Integration with Web/Office apps </li></ul>SQL Server 2005 OLAP Reports (Ad Hoc) Reports (Static) Data Mining Business Knowledge Easy Difficult Usability Relative New Business Insight
    11. 11. Complete Set of Algorithms Introduced in SQL Server 2000 Linear Regression Text Mining Decision Trees Clustering Time Series Sequence Clustering Association Naïve Bayes Neural Net Logistic Regression
    12. 12. Putting Data Mining to Work Clustering Sequence Clustering Finding groups of similar items. For example, to segment demographic data into groups to better understand the relationships between attributes. Association Rules Decision Trees Finding groups of common items in transactions. For example, to use market basket analysis to suggest additional products to a customer for purchase. Sequence Clustering Predicting a sequence. For example, to perform a clickstream analysis of a company's Web site. Decision Trees Time Series Predicting a continuous attribute. For example, to forecast next year's sales. Decision Trees Naive Bayes Clustering Neural Network Logistic Regression Linear Regression Predicting a discrete attribute. For example, to predict whether the recipient of a targeted mailing campaign will buy a product. Microsoft Algorithms Task
    13. 13. Mining for Meaning
    14. 14. BI App lifecycle Dev Server Test Server BI Dev Studio Management Studio Dev/Test <ul><li>Deploy </li></ul><ul><li>Inspect </li></ul><ul><li>Automate: </li></ul><ul><ul><li>data updates </li></ul></ul><ul><ul><li>permission updates </li></ul></ul><ul><li>Monitor </li></ul><ul><li>Version </li></ul>Design Develop Debug Build Deploy Version Production Prod Server Prod Server Prod Server
    15. 15. Scalability <ul><li>Fully centralized calculations on the server </li></ul><ul><ul><li>No calculations done on client </li></ul></ul><ul><ul><li>No excess data transported to the client </li></ul></ul><ul><li>Cached calculation </li></ul><ul><ul><li>Ability to cache calculation on disk </li></ul></ul><ul><li>Disk based dimension storage </li></ul><ul><ul><li>Dimension size is not constrained by memory limits </li></ul></ul><ul><ul><li>150 million members already tested </li></ul></ul><ul><li>Role playing dimensions remove need for duplicating dimension storage </li></ul><ul><li>Attribute-based hierarchies </li></ul><ul><ul><li>Remove need for duplicate info among hierarchies sharing common attributes </li></ul></ul>
    16. 16. Availability <ul><li>Failover Clustering </li></ul><ul><li>Multi-Instances </li></ul><ul><ul><li>Very easy deployment – no registry entries needed </li></ul></ul><ul><li>Server Synching </li></ul><ul><ul><li>Designed for dual machines configurations – number cruncher machine and end-user facing machine </li></ul></ul><ul><ul><li>Allow: </li></ul></ul><ul><ul><ul><li>Processing the calculations isolated from user interactions </li></ul></ul></ul><ul><ul><ul><li>Isolated verification of the results </li></ul></ul></ul><ul><ul><li>Incremental and transactional synching of the query machine with the new results </li></ul></ul><ul><li>Enhanced backup and restore </li></ul><ul><ul><li>Unlimited partition sizes </li></ul></ul>
    17. 17. Serviceability <ul><li>Trace events (with Profiler Integration) </li></ul><ul><li>Flight recorder (repro-less diagnostics) </li></ul><ul><ul><li>Records server activity and metrics at all times </li></ul></ul><ul><ul><li>Provides diagnostics in the event of system failure </li></ul></ul><ul><ul><li>Allows replay of a failure condition </li></ul></ul><ul><ul><li>ON by default </li></ul></ul><ul><li>Capture and Replay </li></ul><ul><ul><li>Customers use to diagnose performance </li></ul></ul><ul><ul><li>Provides PSS a simple means to repro problems </li></ul></ul><ul><ul><li>Test exploit in Labs </li></ul></ul><ul><li>Dr Watson </li></ul>
    18. 18. Manageability <ul><li>Integrated management experience with SQL Server </li></ul><ul><ul><li>Single management shell </li></ul></ul><ul><ul><li>SQL profiler support </li></ul></ul><ul><ul><li>Query analyzer support </li></ul></ul><ul><ul><li>Strong integration with IS (DTS) for management tasks automation </li></ul></ul><ul><li>Deployment packages to manage system life cycle </li></ul><ul><ul><li>Dev -> Test -> Production </li></ul></ul><ul><ul><li>Source control integration for system versioning </li></ul></ul><ul><ul><li>Team development facilities </li></ul></ul><ul><li>Fine grain administration roles </li></ul><ul><ul><li>Database level administration </li></ul></ul><ul><ul><li>Permissions: Creation, R/O, Processing </li></ul></ul><ul><li>XML-based DDL for easy scripting </li></ul><ul><li>Auto referential integrity handling for dealing with dirty input data </li></ul>
    19. 19. Summary <ul><li>Large investment in abilities in SSAS: </li></ul><ul><ul><li>Manageability </li></ul></ul><ul><ul><li>Supportability </li></ul></ul><ul><ul><li>Security </li></ul></ul><ul><ul><li>Availability </li></ul></ul><ul><li>Unified tools with SQL Server </li></ul><ul><li>SQL DBAs skill set can be applied to administering Analysis Server </li></ul><ul><li>BI platform for 24/7 operations </li></ul>
    20. 20. © 2005 Microsoft Corporation. All rights reserved. This presentation is for informational purposes only. Microsoft makes no warranties, express or implied, in this summary.

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