Analysis Services en SQL Server 2008

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    Analysis Services en SQL Server 2008 - Presentation Transcript

    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
    27. Filtering Model Cases
      • Limit the cases used in a model based on any attribute included in the model
      • Use to compare subsets of your data such as different regions
    28. Cross-Validation of Mining Models
      Additional method to test and view mining model accuracy
      Data is partitioned into cross-sections which are used to train and test models against each of the other cross sections
      One portion of the data is used to test, the remaining data is used to train the model
      To define a cross-validation report, you must configure:
      The Fold Count, which specifies the number of folds or partitions that the data is broken into for testing
      The Max Cases, which specifies the maximum number of cases to use (a value of 0 specifies that all cases will be used)
      The Target Attribute, which defines the column or attribute that you want to predict
      The Target State, which defines a particular value that you want to analyze within the target attribute
    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

    + Eduardo CastroEduardo Castro, 3 months ago

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