Aggregating Knowledge in a Data Warehouse and Multidimensional Analysis

Transcription

1 Aggregating Knowledge in a Data Warehouse and Multidimensional Analysis Rafal Lukawiecki Strategic Consultant, Project Botticelli Ltd rafal@projectbotticelli.com

2 Objectives Explain the basics of: 1. Data Warehousing 2. ETL 3. OLAP/Multidimensional Data Relate the theory to SQL Server 2008 SSAS and SSIS This seminar is based on a number of sources including a few dozen of Microsoft-owned presentations, used with permission. Thank you to Marin Bezic, Kathy Sabourin, Aydin Gencler, Bryan Bredehoeft, and Chris Dial for all the support. Thank you to Maciej Pilecki for assistance with demos. The information herein is for informational purposes only and represents the opinions and views of Project Botticelli and/or Rafal Lukawiecki. The material presented is not certain and may vary based on several factors. Microsoft makes no warranties, express, implied or statutory, as to the information in this presentation. Portions 2009 Project Botticelli Ltd & entire material 2009 Microsoft Corp. Some slides contain quotations from copyrighted materials by other authors, as individually attributed or as already covered by Microsoft Copyright ownerships. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Project Botticelli Ltd as of the date of this presentation. Because Project Botticelli & Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft and Project Botticelli cannot guarantee the accuracy of any information provided after the date of this presentation. Project Botticelli makes no warranties, express, implied or statutory, as to the information in this presentation. E&OE. 2

3 1. Data Warehouse 3

4 Rich Connectivity Data Providers SQL Server SAP NetWeaver BI ODBC SQL Server Report Server Models SQL Server Integration Services Teradata XML OLE DB DB2 MySAP SQL Server Data Mining Models Oracle SQL Server Analysis Services Hyperion Essbase 4

5 Let s Store the Intelligence: DW SQL Server Analysis Services server is a logical endpoint for data being aggregated with SSIS But do not store actual data in it Data physically rests in another database called a Data Warehouse You can manipulate it directly, or build in parallel with OLTP processing Modelling of data stored in DW and analysed using SSAS is at the heart of good Data Warehouse design 5

6 Star Schema 6

7 Star Schema Benefits Transforms normalized data into a simpler model Delivers high-performance queries Delivers higher performing queries using Star Join Query Optimization Uses mature modeling techniques that are widely supported by many BI tools Requires low maintenance as the data warehouse design evolves 7

8 Snowflake Dimension Tables Define hierarchies using multiple dimension tables Support fact tables with varying granularity Simplify consolidation of data from multiple sources Potential for slower query performance in relational reporting No difference in performance in Analysis Services database 8

9 Hierarchies Benefits View of data at different levels of summarization Path to drill down or drill up Implementation Denormalized star schema dimension Normalized snowflake dimension Self-referencing relationship 9

10 Fact Table Fundamentals Collection of measurements associated with a specific business process Specific column types Foreign keys to dimensions Measures numeric and additive Metadata and lineage Consistent granularity the most atomic level by which the facts can be defined 10

11 Fact Table Examples Reseller sales data by: Product Order Date Reseller Employee Sales Territory Sales quota data by: Employee Time Quarter Grain Day Grain 11

12 Date Dimension Table Most common dimension used in analysis (aka Time dimension) Used consistently with all facts for efficient and flexible analysis Useful common attributes Year, Quarter, Month, Day Time series analysis support Navigation and summarization enabled with hierarchies, such as calendar or fiscal Single table design (typically not snowflake design) Tip: Format the key of the dimension as yyyymmdd (e.g ) to make it readily understandable 12

13 Parent-Child Hierarchy A dimension that contains a parent attribute A parent attribute describes a self-referencing relationship, or a self-join, within a dimension table Common examples Organizational charts General Ledger structures Bill of Materials 13

14 Parent-Child Hierarchy Example Brian Amy Stephen Syed Stacia Shu Michael José Peter 14

15 Slowly Changing Dimensions Support primary role of data warehouse to describe the past accurately Maintain historical context as new or changed data is loaded into dimension tables Implement changes by Slowly Changing Dimension (SCD) type Type 1: Overwrite the existing dimension record Type 2: Insert a new versioned dimension record Type 3: Track limited history with attributes 15

16 SCD Type 1 Existing record is updated History is not preserved 16

17 SCD Type 2 Existing record is expired and new record inserted History is preserved Most common form of SCD 17

18 SCD Type 3 Existing record is updated Limited history is preserved Implementation is rare SalesTerritoryKey update to

19 Let s Get the Data We would like to populate facts and dimensions in our Data Warehouse from OLTP data... 19

20 2. Integration and ETL 20

21 Let s do ETL with SSIS SQL Server Integration SSIS object model Two distinct runtime Control flow Data flow 32-bit and 64-bit Services (SSIS) service engines: editions 21

22 The Package The basic unit of work, deployment, and execution An organized collection of: Connection managers Control flow components Data flow components Variables Event handlers Configurations Can be designed graphically or built programmatically Saved in XML format to the file system or SQL Server 22

23 Control Flow Control flow is a process-oriented workflow engine A package contains a single control flow Control flow elements Containers Tasks Precedence constraints Variables 23

24 Data Flow The Data Flow Task Encapsulates the data flow engine Exists in the context of an overall control flow Performs traditional ETL in addition to other extended scenarios Is fast and scalable Data Flow Components Extract data from Sources Load data into Destinations Modify data with Transformations Service Paths Connect data flow components Create the pipeline 24

25 Data Flow Sources Sources extract data from Relational tables and views Files Analysis Services databases 25

26 Data Flow Destinations Destinations load data to Relational tables and views Files Analysis Services databases and objects DataReaders and Recordsets Enterprise Edition only 26

27 Populating Fact Tables Fact source Transform Lookup dimension key Repeat for each dimension key Lookup failed? Y N Insert new dimension record Insert new record 27

28 Populating Dimension Tables Dimension source Transform Correlate records New record? Y N Type 1 change? Y Update changed column(s) N Type 2 change? Y Expire existing record Insert new record 28

29 Data Flow Transformations Aggregate, merge, distribute, or modify data Include error outputs in some cases Transformation Categories Row Rowset Split and Join Business Intelligence (BI) Script Other 29

30 Row Transformations Update column values or create new columns Transform each row in the pipeline input 30

31 Rowset Transformations Create new rowsets that can include Aggregated values Sorted values Sample rowsets Pivoted or unpivoted rowsets This is a heavy-weight performer of SSIS Are also called asynchronous components 31

32 Split and Join Transformations Distribute rows to different outputs Create copies of the transformation inputs Join multiple inputs into one output Perform lookup operations 32

33 Using SQL Server Integration Services for Aggregating and Deriving Data 33

34 3. OLAP/Multidimensional Data 34

35 SQL Server 2008 Analysis Services OLAP component Aggregates and organizes data from business data sources Performs calculations difficult to perform using relational queries Supports advanced business intelligence, such as Key Performance Indicators Data mining component Discovers patterns in both relational and OLAP data Enhances the OLAP component with discovered results 35

36 Cube = Unified Dimensional Model Multidimensional data Combination of measures and dimensions as one conceptual model Measures are sourced from fact tables Dimensions are sourced from dimension tables 36

37 Dimensions Members from tables/views in a data source view (based on a Data Warehouse) Contain attributes matching dimension columns Organize attributes as hierarchies One All level and one leaf level User hierarchies are multi-level combinations of attributes Can be placed in display folders Used for slicing and dicing by attribute 37

38 Hierarchy Defined in Analysis Services Ordered collection of attributes into levels Navigation path through dimensional space Very important to get right! Customers by Geography Country State City Customers by Demographics Marital Gender Customer Customer 38

39 Measure Group Group of measures with same dimensionality Analogous to a fact table Cube can contain more than one measure group E.g. Sales, Inventory, Finance Defined by dimension relationships 39

40 Dimension Measure Group Measure Group Sales Inventory Finance Customers X Products X X Time X X X Promotions X Warehouse X Department X Account X Scenario X 40

41 Dimension Relationships Define interaction between dimensions and measure groups Relationship types Regular Reference Fact (Degenerate) Many-to-many Data mining 41

42 Dimension Model Country Country State Marital City Gender Gender State Customer Customer Customer City Gender Marital Age Customer State Gender Customer City Country Marital Attributes Hierarchies 42

43 Calculations Expressions evaluated at query time for values that cannot be stored in fact table Types of calculations Calculated members Named sets Scoped assignments Calculations are defined using MDX MDX = MultiDimensional EXpressions 43

44 1. Using BIDS to Review Dimension Design 2. Cube Design and Functionality 44

45 Summary As a platform for enterprise Business Intelligence you should consider three things: A Data Warehouse Process of Data Integration (incl. ETL) Multidimensional Analysis (OLAP) = SQL Server 2008 Engine, SSIS, and SSAS Now you can support decision making and performance management through: Reports, dashboards, Excel integration, data mining, and better business software 45

46 2009 Microsoft Corporation & Project Botticelli Ltd. All rights reserved. The information herein is for informational purposes only and represents the opinions and views of Project Botticelli and/or Rafal Lukawiecki. The material presented is not certain and may vary based on several factors. Microsoft makes no warranties, express, implied or statutory, as to the information in this presentation. Portions 2009 Project Botticelli Ltd & entire material 2009 Microsoft Corp. Some slides contain quotations from copyrighted materials by other authors, as individually attributed or as already covered by Microsoft Copyright ownerships. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Project Botticelli Ltd as of the date of this presentation. Because Project Botticelli & Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft and Project Botticelli cannot guarantee the accuracy of any information provided after the date of this presentation. Project Botticelli makes no warranties, express, implied or statutory, as to the information in this presentation. E&OE. 46