Presentacion sobre Analysis Services en SQL Server 2008 Ing. Eduardo Castro Martinez, PhD Microsoft SQL Server MVP http://ecastrom.blogspot.com http://comunidadwindows.org

1. Analysis Services in SQL Server® 2008 Eduardo Castro SQL MVP,MCDBA , MCSE, MCAD, MCSD ecastro@mswindowscr.org Comunidad Windows Costa Rica

2. Data Warehouse System Components Data Warehouse User Data Access Data Sources Staging Area Data Marts Data Input Data Access

3. Understanding Data Warehouse Design The Star Schema Fact Table Components Dimension Table Characteristics The Snowflake Schema

4. The Star Schema Employee_Dim EmployeeKey EmployeeID ... Product_Dim Time_Dim ProductKey TimeKey ProductID ... TheDate ... Shipper_Dim Customer_Dim ShipperKey CustomerKey ShipperID ... CustomerID ... Dimension Table Fact Table Sales_Fact TimeKey EmployeeKey ProductKey CustomerKey ShipperKey Sales Amount Unit Sales ...

5. Fact Table Components Measures time_dim 134 1/1/2000 DimensionTables sales_fact Table customer_dim Foreign Keys 201 ALFI Alfreds customer_key product_key time_key quantity_sales amount_sales product_dim 201 25 134 400 10,789 25 123 Chai The grain of the sales_fact table is defined by the lowest level of detail stored in each dimension

6. Dimension Table Characteristics Describes Business Entities Contains Attributes That Provide Context to Numeric Data Presents Data Organized into Hierarchies

7. The Snowflake Schema Defines Hierarchies by Using Multiple Dimension Tables Is More Normalized than a Single Table Dimension Is Supported within Analysis Services

8. Defining OLAP Solutions OLAP Databases Common OLAP Applications Relational Data Marts and OLAP Cubes OLAP in SQL Server 2005

9. OLAP Databases Optimized Schema for Fast User Queries Robust Calculation Engine for Numeric Analysis Conceptual, Intuitive Data Model Multidimensional View of Data Drill down and drill up Pivot views of data

10. OLAP in SQL Server Microsoft Is One of Several OLAP Vendors Analysis Services Is Bundled with Microsoft SQL Server 2005 and SQL Server 2008 Analysis Services Include OLAP engine Data Mining technology

11. Dimension Fundamentals

12. Cube Measures Are the Numeric Values of Principle Interest Correspond to Fact Table Facts Intersect All Dimensions at All Levels Are Aggregated at All Levels of Detail

13. Relational Data Sources Star and Snowflake Schemas Are required to build a cube with Analysis Services Fact Table Contains measures Contains keys that join to dimension tables Dimension Tables Must exist in same database as fact table Contain primary keys that identify each member

14. Applying OLAP Cubes Defining a Cube Querying a Cube Defining a Cube Slice Working with Dimensions and Hierarchies Visualizing Cube Dimensions Connecting to an OLAP Cube

15. Defining a Cube Atlanta Chicago Market Dimension Denver Grapes Cherries Detroit Melons Apples Products Dimension Q4 Q1 Q2 Q3 Time Dimension

16. Fact Sales Atlanta Chicago MarketsDimension Denver Grapes Cherries Dallas Melons ProductsDimension Apples Q4 Q1 Q2 Q3 TimeDimension Querying a Cube

17. SQL Server 2008 Analysis Services Enhancements Multidimensional Analysis with SQL Server Analysis Services Data Mining with SQL Server Analysis Services

18. Multidimensional Analysis with SQL Server Analysis Services Cube Wizard Dimension Wizard The Attribute Relationship Designer The Aggregation Designer AMO Warnings

19. Cube Wizard More efficient interface Create a cube based on a single de-normalized table Create a cube based on a data source that has only linked dimensions

20. Dimension Wizard Create dimensions more efficiently Automatically detect parent-child hierarchies Provide safer default error configuration Set member properties while creating the dimension

21. The Attribute Relationship Designer Flexible relationship Rigid relationship Manage Relationship through right-click options in the Attribute Relationship pane

22. The Aggregation Designer New Aggregation Designer: Aggregations designs are shown grouped by measure group New view available for manual aggregation design Improved Usage-Based Optimization Wizard: Ability to append new aggregations to an existing aggregation design Ability to modify storage settings for one or more partitions simultaneously Improved Aggregation Design Wizard

23. AMO Warnings Uses familiar symbols such as the wavy underline and a yellow triangle with an exclamation point Non-intrusive warning messages appear when you pause the mouse over a warning Available for logical errors in database design, when users depart from design best practices, and for non-optimal aggregation designs

24. Data Mining with SQL Server Analysis Services Improved Data Mining Wizard Separating Test and Training Data Filtering Model Cases Cross Validation of Mining Models Data Mining Add-Ins for Microsoft Office Systems

25. Data Mining Enhancements Improve both short-term and long-term predictions with the Microsoft Time Series algorithm, which has been enhanced to support both ARTXP and ARIMA algorithms Enable drillthrough on a mining structure to allow queries about the cases used for both training and testing Create column aliases to make it easier to understand column content. In the Data Mining Designer, the alias appears in parentheses next to the column usage label Separate test and training data Improve performance and analyze different scenarios by using Model Case Filtering Create cross-validation reports Utilize Data Mining Add-ins for Office

26. Separating Test and Training Data Three ways to partition data into training and test sets: The Data Mining Wizard Modifying the properties of the mining structure: If you did not create a test partition when you created the structure, you can modify the HouldoutMaxCases, HoldoutMaxPercent, and HoldoutSeed properties DMX statement, AMO, or XML DDL

29. Data Mining Add-Ins for Microsoft Office System Data Mining Client for Excel – create, test, and manage data mining projects within Excel 2007 Table Analysis Tools for Excel – use powerful analysis tools such as analyzing key influencers, highlighting exceptions, and forecasting for data stored in spreadsheets Data Mining Templates for Visio – render decision trees, regression trees, cluster diagrams, and dependency nets in diagrams created in Visio 2007 Important: Data Mining Add-ins for Microsoft Office System will be available for SQL Server 2008 when it releases to manufacturing.

30. Demonstration Implementing Multidimensional Analysis

31. Optimizing Storage What Are Sparse Columns? How to Compress Data and Backups

32. What Are Sparse Columns? Eliminate limitation of 1024 columns. Efficient way to manage object models that frequently contain numerous NULL values. CREATE TABLE products (product_numint, item_numint, price decimal(7,2), ..., color char(5) SPARSE, width float SPARSE...)

33. How to Compress Data and Backups Data compression can be enabled on tables or views Different compression types can be configured on a per-partition basis The following data compression types can be defined: Row compression Page compression Backup compression: Normally decreases time required to perform a backup Managed with Transact-SQL, Backup Task, Maintenance Plan Wizard, and Integration Services Backup Database task

34. Contact Information Eduardo Castro ecastro@mswindowscr.org http://ecastrom.blogspot.com