Introduction to Microsoft SQL Server 2008 R2 Analysis Service


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  • Key Points: Integration Services (SSIS) provides a scalable enterprise data integration platform with exceptional Extract, Transform, Load (ETL) and integration capabilities, enabling organizations to more easily manage data from a wide array of data sourcesMaster Data Services (MDS) enables organizations to start with simple solutions for analytic or operational requirements, and then adapt the solutions to additional requirements incrementallyThe latest version of SQL Server from Microsoft SQL Server 2008 offers hundreds of new DBMS features that boost the productivity of database administrators and developers, improve support for larger databases, and enhance securityReporting Services (SSRS) provides a full range of ready-to-use tools and services to help you create, deploy, and manage reports for your organization, as well as programming features that enable you to extend and customize your reporting functionalityAnalysis Services (SSAS) delivers online analytical processing (OLAP) and data mining functionality for business intelligence applicationsConclusion: With SQL Server 2008 R2 customers get all the technologies needed to build a reliable and secure BI platform. SQL Server 2008 R2 has the strongest combination of price/performance, manageability, security, and DBA productivity.
  • Key Points: Store - The SQL Server 2008 R2 Database Engine provides a high-performance, scalable storage solution for enterprise-scale data warehouses.Integrate – SQL Server Integration Services provides a comprehensive set of ETL capabilities that you can use to build and maintain a data warehouse that consolidates business data from across the enterprise.Analyze – SQL Server Analysis Services provides powerful OLAP analysis and data mining functionality to help your users gain deep insights into your business data.Report – SQL Server Reporting Services is an enterprise-scale reporting solution that you can use to create and deliver reports throughout the organization and to external partners and customersStewardship – SQL Server Master Data Services enables organizations to start with simple solutions for analytic or operational requirements, and then adapt the solutions to additional requirements incrementally.Conclusion: SQL Server 2008 R2 provides a full, end-to-end platform for Business Intelligence solutions.
  • Introduction to Microsoft SQL Server 2008 R2 Analysis Service

    1. 1. SQL Server 2008 R2Understanding SQL Server Analysis Services
    2. 2. SQL Server 2008 R2 BI Technologies
    3. 3. SQL Server 2008 R2 BI Technologies
    4. 4. Contents• Understand the Analysis Services 2008 R2• Understand the OLAP and OLAP database• Understand the dimensional OLAP• Understand the multidimensional data analysis• Understand dimensional data warehouse
    5. 5. SQL Server 2008 R2 BI Structure Reporting and Visualization Tools (Dashboard, KPI, Presentation Layer Scorecard,…) Turn data into information (analysis) Analytical Layer Multidimensional OLAP DatabaseData Storage and Retrieval Layer Data Warehouse in RDBMS 1. Extract the data from the multiple sources Data Transformation Layer 2. Modify the data to consistent 3. Load the data into Data Storage system Data Source Layer Text, MS Excel, MS Access, MS SQL, Oracle,…| External Sources
    6. 6. Microsoft Business Intelligence Platform Analytic Scorecards, Analytics, Planning Applications (PerformancePoint Service) Portal (SharePoint) Data Delivery Report Builder End-user Analysis SSRS (Excel) Integrate Analyze Report (SQL Integration Services) (SQL Analysis Services) (SQL Reporting Services) Infrastructure Platform Data Warehouse, Data Marts, Operational Data (SQL Server 2008 R2) Office SQL
    7. 7. Analysis ChallengesHow Do You Deal With: Data stored in The cost of developing The costs of multiple data sources analytical solutions learning new tools Deploy for today’s problem but scale ‘Real-Time’ data over time access Multiple Users, Diverse analytical Inconsistent data Multiple Tools needs
    8. 8. Analysis Services 2008 R2 Design Scalable Solutions Productivity enhancing designers Scalable Infrastructure Superior Performance Extend Beyond OLAP Unified meta data model Central KPI manageability Predictive Analysis Deliver Pervasive Insight Optimized Office interoperability Rich partner extensibility Open, embeddable architecture
    9. 9. Design Scalable Solutions Productivity Enhancing Designers Optimized design experience Best Practice Design Alerts Project Lifecycle support Scalable Infrastructure Heterogeneous data Integration Robust Scale-Out Configuration Advanced Resource Monitoring User-differentiated perspectives Superior Performance Market leading MOLAP Engine Near real-time data access Subspace computation optimization MOLAP enabled write-back
    10. 10. Extend Beyond OLAP Unified Metadata Model One consolidated business view Integrated relational & OLAP analysis Business information modeling Time- and financial intelligence Central KPI Manageability Server based KPI framework Centrally managed repository Pervasive end-user accessibility Predictive Analytics Complete data mining framework Embeddable viewers Predictive capabilities available to everyone through Microsoft Office
    11. 11. Predictive AnalysisBring Data Mining to the Masses through Microsoft Office Enable easy to use predictive analysis At every desktop For every information worker Through three powerful add-ins to Microsoft Office Predictive capabilities readily available for business users in Excel Data mining client for building data mining models in Excel Data mining templates for project visualization in Visio “What Microsoft has done is to make data mining available on the desktop to everyone” (David Norris, Associate Analyst, Bloor Research).
    12. 12. Deliver Pervasive Insight Optimized Office Interoperability Massive data analysis for everyone with PowerPivot for Excel 2010 Team Collaboration through PowerPivot for SharePoint 2010 Corporate performance management through PerformancePoint Services 2010 Rich Partner Ecosystem Extensibility Vertically specialized solutions Packaged applications API support from all major BI vendors Open, embeddable architecture Open API’s and XML/A based protocols Native web service functionality Close loop analysis
    13. 13. Office 2010 Integration Excel 2010 Great cross product investments optimizing Excel 2010 as analytical client for Analysis Services Enhancements around local cubes Significant performance and functionality investments Data Mining Add-Ins for predictive analysis PowerPivot for massive data analysis on the desktop PerformancePoint Services 2010 Great cross product investments for thin analytic client for Analysis Services Rich web capabilities for data exploration. Guided and contextual analysis through integrated dashboards Predictive analytics by integrating with SQL Server Data Mining
    14. 14. Understanding SQL Server Analysis ServicesUNDERSTANDING OLAP
    15. 15. What is OLAP Online Analytical • Benefits Processing – Consistently fast responseOnline Transaction Processing 1993. – Metadata-based queries 1985. OLAP – Spreadsheet-style formulas OLTP
    16. 16. Consistently Fast Response• Calculating and storing aggregate values and the results of formulas when a cube is loaded (calculation in advance)• Aggregate tables can be created to provide fast query results
    17. 17. Metadata-Based Queries SQL Query• SQL is suitable for SELECT transaction system [Store].[Store Country].[Canada].[Vancouver] ON COLUMNS, not for reporting [Product].[All Products].[Clothing].[Mittens] applications ON ROWS FROM [Sales]• Query language for WHERE ([Measures].[Unit Sales], [Date].[2010].[February]) OLAP data source MDX Query – Multidimensional SELECT SUM(Sales.[Unit Sales]) expression FROM (Sales INNER JOIN Stores ON Sales.StoreID = Stores.StoreID) INNER JOIN Products – MDX ON Sales.ProductID = Products.ProductID WHERE Stores.StoreCity = Vancouver AND Products.ProductName = Mittens AND Sales.SaleDate BETWEEN 01-02-2010 AND 28-02-2010
    18. 18. Spreadsheet-Style Formulas• MDX formulas use named references – C14/D14 (Spreadsheet) | [Actual]/[Budget] (MDX)• MDX formulas are easy to manage• MDX formulas are multidimensional – Spreadsheet is two dimensional• MDX formulas take advantage of metadata (its relationship) – There is no relationship in cells on the sheet.
    19. 19. Understanding SQL Server Analysis ServicesMULTIDIMENSIONAL DATA ANALYSIS
    20. 20. Measure and Metadata• Measure: A summarizable numerical value – Sales Dollars, Shipment Units,...• Metadata: Data about data – Label, Order by,... Metadata Units Sold 70 70 Measure Adventure Works Sales Adventure Works Sales
    21. 21. Unit sold by Product and Month reportProduct Jan 2011 Feb 2011 Mar 2011 Apr 2011Mountain-500 Black, 40 1 3 1 2Mountain-500 Black, 44 2 1Mountain-500 Black, 48 1 2 1Mountain-500 Silver, 40 1 2 1Mountain-500 Silver, 44 1 1 1Mountain-500 Silver, 48 2Road-750 Black, 44 10 7Road-750 Black, 48 5 9Hitch Rack 1 6 6 3
    22. 22. Grouping/Aggregating/Attribute/Member • Grouping – Aggregating: is theProduct Model Color Size way humans deal with too muchMountain-500 Black, 40 Mountain- Black 40 detail 500Mountain-500 Black, 44 Mountain- Black 44 – Ex: group Products by model, 500 subcategory, and category groups Attribute: Product (Key), Model,Mountain-500 Black, 48 Mountain- Black 48 500 •Mountain-500 Silver, 40 Mountain- Silver 40 Color, Size 500Mountain-500 Silver, 44 Mountain- Silver 44 • Member 500 – Model, Mountain-500, Road-Mountain-500 Silver, 48 Mountain- Silver 48 750… 500Road-750 Black, 44 Road-750 Black 44 – Color: Black, SilverRoad-750 Black, 48 Road-750 Black 48Hitch Rack Hitch Rack – Size: 40, 44, 48 Product Attributes
    23. 23. Hierarchy: Model  Product Jan 2011 Feb 2011 Mar 2011 Apr 2011Mountain-500 3 8 6 6 Mountain-500 Black, 40 1 3 1 2 Mountain-500 Black, 44 2 1 Mountain-500 Black, 48 1 2 1 Mountain-500 Silver, 40 1 2 1 Mountain-500 Silver, 44 1 1 1 Mountain-500 Silver, 48 2Road-750 15 16 Road-750 Black, 44 10 7 Road-750 Black, 48 5 9Hitch Rack 1 6 6 3 Hitch Rack 1 6 6 3Units Sold by Model, Product and Month
    24. 24. Hierarchy• Hierarchy is created by arranging related attributes into levels• Hierarchy level: 2, 3,…n• Hierarchy type: – Balance (Date) – Unbalance (Organization)
    25. 25. Dimensions Jan Feb Mar Apr 2011 2011 2011 2011Mountain- 3 8 6 6500Road-750 15 16Hitch Rack 1 6 6 3 Units Sold by Model and Month• Attribute: – Model (3) – Month (4)• Potential number of values: 12 = 3x4
    26. 26. Dimensions Jan 2011 Feb 2011 Mar 2011 Apr 2011 Units $ Units $ Units $ Units $WA Hitch Rack 4 $480 3 $360 2 $240 Mountain- 2 $1.105 6 $3.256 5 $2.775 5 $2.750 500 Road-750 9 $4.860 10 $5.400OR Hitch Rack 2 $240 3 $360 1 $120 Mountain- 1 $120 2 $1.105 1 $540 1 $540 500 Road-750 1 $565 6 $3.240 6 $3.240• Attribute: – State (2), Model (3), Month (4), Measure (2: Units sold, Sales dollars)• Potential number of values: 2x3x4x2 = 48
    27. 27. Dimensions• Examples: – State attribute belongs to the Geography dimension – Model attribute belongs to the Product dimension – Month attribute belongs to the Date dimension – Units sold and Sale Dollars belongs to the Measure dimension
    28. 28. Dimensions• The independent attributes and hierarchies are the dimension• A dimension may contain more than one attributes – Ex: Product dimension contain Color and Size attribute• Dimension also contain hierarchies – Ex: Product by Model hierarchy is composed of attributes contained in the Product dimension, so the hierarchy also belongs in the Product dimension• Measure dimension are displayed on columns
    29. 29. Understanding SQL Server Analysis ServicesDIMENSIONAL DATA WAREHOUSE
    30. 30. Dimension Data Warehouse• Dimension Data Warehouse is the data storage and retrieval layer of BI system• In dimension data warehouse: – Dimension are stored in dimension tables – Measure are called facts and are stored in fact tables
    31. 31. Fact Table• Fact table: table that stores the detailed values for measures• Key Column: State, Product, Month• Fact Column: UnitsSold, SalesDollars State Product Month UnitsSol SalesDollar d s OR Hitch Rack Jan 2011 1 $120.00 OR Mountain-500 Silver, 40 Jan 2011 1 $565.00 OR Mountain-500 Silver, 48 Jan 2011 1 $552.50 WA Mountain-500 Silver, 48 Jan 2011 1 $552.50 OR Hitch Rack Feb 2011 2 $240.00 WA Hitch Rack Feb 2011 4 $480.00 FactSales table
    32. 32. Fact Table• The value in the key columns relate the facts in the fact table row to a row in each dimension table• Fact table may have other type of column for reference purposes• Fact table might contain one or more measure columns
    33. 33. Fact Table• The level of detail stored in a fact table is called granularity• The dimensions that a fact table is related to is called dimensionality of the fact table• Facts that have different granularity of different dimensionality must be stored in separate fact tables
    34. 34. Fact table: Dimension key• Actually a fact table almost always uses an integer, called a dimension key, for each State Product Month UnitsSold SalesDollars dimension member 1 483 201101 1 120.00 1 591 201101 1 565.00• There must be a dimension 1 594 201101 1 552.50 table for each dimension key 2 594 201101 1 552.50 in a fact table 1 483 201102 2 240.00 2 483 201102 4 480.00 FactSales table using Dimension key
    35. 35. Dimension Table• A dimension table contain one row for each member of the key attribute of the dimension ProductKey Product 596 Mountain-500 Black, 40• The key attribute has two column: 598 Mountain-500 Black, 44 599 Mountain-500 Black, 48 – Integer dimension key (PK) 591 Mountain-500 Silver, 40 – Attribute label 593 Mountain-500 Silver, 44 594 Mountain-500 Silver, 48• A dimension table may contain 604 Road-750 Black, 44 other columns for other attributes 605 Road-750 Black, 48 of the dimension 483 Hitch Rack DimProduct Dimension Table
    36. 36. Dimension tableProductKey Product SubCategory Category Color Size 596 Mountain-500 Black, 40 Mountain Bikes Bikes Black 40 598 Mountain-500 Black, 44 Mountain Bikes Bikes Black 44 599 Mountain-500 Black, 48 Mountain Bikes Bikes Black 48 591 Mountain-500 Silver, 40 Mountain Bikes Bikes Silver 40 593 Mountain-500 Silver, 44 Mountain Bikes Bikes Silver 44 594 Mountain-500 Silver, 48 Mountain Bikes Bikes Silver 48 604 Road-750 Black, 44 Road Bikes Bikes Black 44 605 Road-750 Black, 48 Road Bikes Bikes Black 48 483 Hitch Rack Bike Racks Accessories DimProduct Dimension Table
    37. 37. Aggregatable and Aggregate• Aggregatable: Attributes that can be used to create groups• Non aggregatable attributes are referred to as member properties – Ex: List Price, Telephone Number, Street Address…• Aggregate: Summary value in the group of aggregatable• Example: – Aggregatable: Category, Color… – Aggregate: Number of Units Sold for each Category
    38. 38. Understanding SQL Server Analysis ServicesMULTIDIMENSIONAL OLAP
    39. 39. Multidimensional OLAP• Multidimensional OLAP database resides between the data storage and retrieval layer and the presentation layer• It converts the relation data warehouse data into a fully implemented dimensional model for creating analytical reports and data visualizations
    40. 40. Measure Group and Cube• Measure group corresponds to a single fact table• Measure group may contains data for single level of detail and aggregated data for all higher levels of detail• Cube: Combination of several related measure groups and a set of dimensions State Product Date Units Sold Sales Amount All All All 70 31.305 WA All All 46 21.235 WA Bikes All 37 20.115 WA Road Bikes All 19 10.260
    41. 41. Understanding SQL Server Analysis ServiceDEMO