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Module 1: Introduction Deriving Business Information Deriving - PDF document

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Raw Data vs. Business Information Capturing Raw Data Gathering data recorded in everyday operations Module 1: Introduction Deriving Business Information Deriving meaningful information from raw data to Data Warehousing Turning

  1. Raw Data vs. Business Information � Capturing Raw Data � Gathering data recorded in everyday operations Module 1: Introduction � Deriving Business Information � Deriving meaningful information from raw data to Data Warehousing � Turning Data into Information and OLAP � Implementing a decision support system OLTP Source Systems Data Warehouse Characteristics � OLTP System Characteristics � Provides Data for Business Analysis Processes � Processes real-time transactions of a business � Integrates Data from Heterogeneous Source Systems � Contains data structures optimized for entries and edits � Combines Validated Source Data � Provides limited decision support capabilities � Organizes Data into Non-Volatile, Subject-Specific Groups � OLTP Examples � Stores Data in Structures that Are Optimized for � Order tracking � Service-based sales Extraction and Querying � Banking functions � Customer service � Point-of-sales � Defining OLAP Solutions Data Warehouse System Components Data Warehouse User Data Data Marts Data Access Staging Sources � OLAP Databases Area � Common OLAP Applications � Relational Data Marts and OLAP Cubes � OLAP in SQL Server 2000 Data Input Data Access 1

  2. OLAP Databases Common OLAP Applications � Executive Information Systems � Financial Applications � Optimized Schema for Fast User Queries � Performance measures � Reporting � Robust Calculation Engine for Numeric Analysis � Exception reporting � Planning � Analysis � Conceptual, Intuitive Data Model � Multidimensional View of Data � Sales/Marketing Applications � Operations Applications � Drill down and drill up � Booking/billing � Manufacturing � Product analysis � Customer service � Pivot views of data � Customer analysis � Product cost Relational Data Marts and OLAP Cubes OLAP in SQL Server 2000 Relational Relational Relational OLAP Cube OLAP Cube � Microsoft Is One of Several OLAP Vendors OLAP Cube Data Mart Data Mart Data Mart � Analysis Services Is Bundled with Microsoft SQL Server Relational N-dimensional Relational N-dimensional Data Storage 2000 Data Storage Data Structure Data structure Data Structure Data structure � Analysis Services Include Detailed and Detailed and Data Content Summarized Data Data Content Summarized Data Summarized Data Summarized Data � OLAP engine Relational and Relational and Relational and Relational and Data Sources � Data mining technology Data Sources Non-relational Sources Non-relational Sources Non-relational Sources Non-relational Sources Fast Performance for Faster Performance for Fast Performance for Faster Performance for Data Retrieval Data Retrieval Data Extract Queries Data Extract Queries Data Extract Queries Data Extract Queries � Understanding Data Warehouse Design The Star Schema Employee_Dim Employee_Dim Employee_Dim EmployeeKey EmployeeKey EmployeeID EmployeeID ... ... � The Star Schema Dimension Table � Fact Table Components Fact Table Time_Dim Time_Dim Time_Dim Product_Dim Product_Dim Product_Dim � Dimension Table Characteristics TimeKey Sales_Fact Sales_Fact ProductKey TimeKey ProductKey TheDate TimeKey TheDate TimeKey ProductID ProductID � The Snowflake Schema ... EmployeeKey ... EmployeeKey ... ... ProductKey ProductKey CustomerKey CustomerKey ShipperKey ShipperKey Sales Amount Sales Amount Unit Sales ... Unit Sales ... Shipper_Dim Shipper_Dim Shipper_Dim Customer_Dim Customer_Dim Customer_Dim ShipperKey CustomerKey ShipperKey CustomerKey ShipperID ShipperID CustomerID CustomerID ... ... ... ... 2

  3. Fact Table Components Dimension Table Characteristics Dimension Dimension Tables Tables sales_fact Table customer_dim customer_dim customer_dim 201 ALFI Alfreds Foreign Keys Foreign Keys Measures Measures 201 ALFI Alfreds customer_key product_key product_key time_key quantity_sales quantity_sales amount_sales customer_key time_key amount_sales product_dim product_dim product_dim 201 25 134 400 10,789 25 123 Chai 25 123 Chai � Describes Business Entities � Contains Attributes That Provide Context to Numeric time_dim time_dim time_dim Data 134 1/1/2000 134 1/1/2000 The grain of the sales_fact table is defined by the lowest � Presents Data Organized into Hierarchies level of detail stored in each dimension � Understanding OLAP Models The Snowflake Schema � OLAP Database Components � OLAP Dimensions vs. Relational Dimensions � Dimension Fundamentals � Dimension Family Relationships � Cube Measures � Defines Hierarchies by Using Multiple Dimension Tables � Relational Data Sources � Is More Normalized than a Single Table Dimension � Is Supported within Analysis Services OLAP Database Components OLAP Dimensions vs. Relational Dimensions OLAP Relational � Numeric Measures REGION REGION � Dimensions West West � Cubes East CA OR STATE REGION East CA West MA OR West NY MA East NY East 3

  4. Dimension Fundamentals Dimension Family Relationships USA is the parent of North West and � South West USA USA USA USA USA USA USA USA � North West and South West are North West North West North West North West North West North West North West North West children of USA Oregon Oregon Oregon Oregon Oregon Oregon Oregon Oregon Washington Washington Washington Washington Washington Washington Washington Washington � North West and California are South West South West South West South West South West South West South West South West descendants of USA California California California California California California California California � North West and USA are ancestors of Washington � North West and South West are siblings Oregon and California are cousins � � All are dimension members Cube Measures Relational Data Sources � Star and Snowflake Schemas � Are the Numeric Values of Principle Interest � Are required to build a cube with Analysis Services � Correspond to Fact Table Facts � Fact Table � Intersect All Dimensions at All Levels � Contains measures � Are Aggregated at All Levels of Detail � Contains keys that join to dimension tables � Form a Dimension � Dimension Tables � Must exist in same database as fact table � Contain primary keys that identify each member � Applying OLAP Cubes Defining a Cube � Defining a Cube � Querying a Cube Atlanta � Defining a Cube Slice n o i s � Working with Dimensions and Hierarchies n Chicago e m i D � Visualizing Cube Dimensions t Denver e Products Dimension k Grapes r � Connecting to an OLAP Cube a Cherries M Detroit Melons Apples Q4 Q1 Q2 Q3 Time Dimension 4

  5. Querying a Cube Defining a Cube Slice Sales Atlanta Atlanta n n Fact o o i i s s n n e Chicago e Chicago m m i i D D s s t t Denver Denver e e k k Grapes r r Grapes a a M Cherries M Products Dimension Products Dimension Cherries Dallas Detroit Melons Melons Apples Apples Q4 Q4 Q1 Q2 Q3 Q1 Q2 Q3 Time Dimension Time Dimension Working with Dimensions and Hierarchies Visualizing Cube Dimensions � Dimensions Allow You to � Slice � Dice � Hierarchies Allow You to � Drill Down � Drill Up Connecting to an OLAP Cube Review State USA Sales Units � Introducing Data Warehousing 6000 Level 02 5000 � Defining OLAP Solutions Year Quarter 4000 Sheri Now mer - 2001 - Quarter 4 � Understanding Data Warehouse Design Sheri Now mer - 2001 - Quarter 3 3000 Sheri Now mer - 2001 - Quarter 2 Sheri Now mer - 2001 - Quarter 1 � Understanding OLAP Models 2000 Sheri Now mer - 2000 - Quarter 4 Sheri Now mer - 2000 - Quarter 3 � Applying OLAP Cubes 1000 Sheri Now mer - 2000 - Quarter 2 Sheri Now mer - 2000 - Quarter 1 0 Bagels Muffins Sliced Bread Cheese Deli Meats Frozen Chicken Bread Dairy Meat Category Subcategory 5

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