1. ISQS 3358, Business Intelligence Creating Data Marts Zhangxi Lin Texas Tech University 1

2. Outline Illustrative Example : Adventure Works Cycles (AWC) Illustrative Example : Adventure Works Cycles (AWC) Data Warehousing with Microsoft SQL Server 2008 Exercise 2 Types of Dimension Tables Hands-on Case: Maximum Miniature Manufacturing Exercise 3

3. ILLUSTRATIVE EXAMPLE : ADVENTURE WORKS CYCLES (AWC) 3

4. Adventure Works Cycles (AWC) A fictitious multinational manufacturer and seller of bicycles and accessories Based on Bothell, Washington, USA and has regional sales offices in several countries http://www.msftdwtoolkit.com/ ISQS 6339, Data Mgmt & BI, Zhangxi Lin4

5. Basic Business Information Product orders by category Product Orders by Country/Region Product Orders by Sales Channel Customers by Sales Channel Snapshot ISQS 6339, Data Mgmt & BI, Zhangxi Lin5

6. Business Processes Purchase Orders Distribution Center Deliveries Distribution Center Inventory Store Deliveries Store Inventory Store Sales ISQS 6339, Data Mgmt & BI, Zhangxi Lin6

7. Analytic Themes See the Excel file \\TechShare\coba\d\isqs3358\Repository\AWC\ AW_Analytic_Themes_List.xls\Repository\AWC\ AW_Analytic_Themes_List.xls SQL Server 2008 R2 – Data Warehousing Scaling and Performance 41’28” SQL Server 2008 R2 – Data Warehousing Scaling and Performance ISQS 6339, Data Mgmt & BI, Zhangxi Lin7

8. AWC’s Bus Matrix Dimensions Business Process Date Product Employee Customer (Reseller) Customer (Internet) Sales Territory Currency Channel Promotion Call Reason Facility Sales ForecastingXXXXXXX OrdersXXXXXXXXX Call TrackingXXXXXX X ReturnsXX XXXXX X ISQS 6339, Data Mgmt & BI, Zhangxi Lin8

9. 9 Prioritization Grid Orders Forecast Call Tracking Exchange Rates Returns Manufacturing Costs Customer Profitability Product Profitability Feasibility HighLow High Low Business Value / Impact

10. Unified Dimensional Model (UDM) A SQL Server 2008 technology A UDM is a structure that sits over the top of a data mart and looks exactly like an OLAP system to the end user. Advantages ◦ No need for a data mart. ◦ Can be built over one or more OLTP systems. ◦ Mixed data mart and OLTP system data ◦ Can include data from database from other vendors and XML- formatted data ◦ Allows OLAP cubes to be built directly on top of transactional data ◦ Low latency ◦ Ease of creation and maintenance Features ◦ Data sources ◦ Data views ◦ Proactive caching for preprocessed aggregates ISQS 6339, Data Mgmt & BI, Zhangxi Lin10

11. DATA WAREHOUSING WITH MICROSOFT SQL SERVER 2008 ISQS 6339, Data Mgmt & BI, Zhangxi Lin11

12. Microsoft BI Toolset Relational engine (RDBMS) ◦ T-SQL ◦.NET Framework Command Language Runtime (CLR) SQL Server Integration Services (SSIS) – ETL ◦ Data Transformation Pipeline (DTP) ◦ Data Transformation Runtime (DTR) SQL Server Analysis Service (SSAS) – queries, ad hoc use, OLAP, data mining ◦ Multi-Dimensional eXpressions (MDX) – a scripting language for data retrieval from dimensional database ◦ Dimension design ◦ Cube design ◦ Data mining SQL Server Reporting Services (SSRS) – ad hoc query, report building Microsoft Visual Studio.NET is the fundamental tool for application development Design Facts, Dimensions and Transformation/Load Processes 3’46” Design Facts, Dimensions and Transformation/Load Processes 12

13. 13 Structure and Components of Business Intelligence SSMS SSIS SSAS SSRS SAS EM SAS EM SAS EG SAS EG MS SQL Server 2008 BIDS

14. Disadvantages of OLAP Complexity to administer Requires data mart Latency Read-only

15. Understanding the Cube Designer Tabs Cube Structure: Use this tab to modify the architecture of a cube. Dimension Usage: Use this tab to define the relationships between dimensions and measure groups, and the granularity of each dimension within each measure group. Calculations: Use this tab to examine calculations that are defined for the cube, to define new calculations for the whole cube or for a subcube, to reorder existing calculations, and to debug calculations step by step by using breakpoints. KPIs: Use this tab to create, edit, and modify the Key Performance Indicators (KPIs) in a cube. Actions: Use this tab to create or modify drillthrough, reporting, and other actions for the selected cube.. Partitions: Use this tab to create and manage the partitions for a cube. Partitions let you store sections of a cube in different locations with different properties, such as aggregation definitions. Perspectives: Use this tab to create and manage the perspectives in a cube. A perspective is a defined subset of a cube, and is used to reduce the perceived complexity of a cube to the business user. Translations: Use this tab to create and manage translated names for cube objects, such as month or product names. Browser: Use this tab to view data in the cube. ISQS 6339, Data Mgmt & BI, Zhangxi Lin15

16. EXERCISE 2

17. AWC Business Requirements - Interview summary Interviewee: Brian Welker, VP of Sales Sales to resellers: $37 million last year 17 people report to him including 3 regional sales managers Previous problem: Hard to get information out of the company’s system Major analytic areas: Sales planning Growth analysis Customer analysis Territory analysis Sales performance Basic sales reporting Price lists Special offers Customer satisfaction International support Success criteria Easy data access, Flexible reporting and analyzing, All data in one place What’s missing? – A lot – No indication of business value ISQS 6339, Data Mgmt & BI, Zhangxi Lin17

18. Business Analytics Theme Sales performance ◦ By territory ◦ By product ◦ By customer ◦ By date

19. AWC Sale Database

20. AWC Sale Data Warehouse

21. Tables in AWC Sales DW

22. Types of Dimension Table Conformed dimensions ◦ Time, SaleTerritory Junk dimensions Role playing dimensions ◦ Time Slowly changing dimensions (SCD) Aggregate dimensions Aggregate dimensions Degenerate dimensions Degenerate dimensions Many-to-many or multivalued dimensions Many-to-many or multivalued dimensions

23. Select Measure Group Tables

25. Why DimEmployee disappeared?

28. Deliverable: AWC Sales Data Warehouse Screenshot Due: Feb 15, 2016, by email to isqs3358@gmail.com

29. TYPES OF DIMENSION

30. Types of dimension Types of dimension There are 7 types of frequently referred dimensions ◦ Conformed dimensions ◦ Junk dimensions ◦ Role playing dimensions ◦ Slowly changing dimensions (SCD) ◦ Aggregate dimensions ◦ Degenerate dimensions ◦ Many-to-many or multivalued dimensions  For more information about types of dimension, check  For more information about types of dimension, check The Microsoft Data Warehouse Toolkit, Joy Mundy and Warren Thornthwaite, Wiley, 2006

31. Conformed Dimensions A set of data attributes that have been physically implemented in multiple database tables using the same structure, attributes, domain values, definitions and concepts in each implementation. Dimension tables are not conformed if the attributes are labeled differently or contain different values. Conformed dimensions come in several different flavors. At the most basic level, conformed dimensions mean exactly the same thing with every possible fact table to which they are joined. E.g. The date dimension table connected to the sales facts and the one connected to the inventory facts.

32. Conformed Dimensions Dimensions are conformed when they are either exactly the same (including keys) or one is a perfect subset of the other. Most important, the row headers produced in the answer sets from two different conformed dimensions must be able to match perfectly. Conformed dimensions are either identical or strict mathematical subsets of the most granular, detailed dimension.

33. Junk Dimensions Also called miscellaneous or mystery dimensions They are miscellaneous attributes that don’t belong to any existing dimension. Typically flags or indictors that describe or categorize the transaction in some way. Contents are often important Four alternatives for dealing with them ◦ Leave them in the fact table ◦ Create a separate dimension for each attribute ◦ Omit them ◦ Group them into a single junk dimension 33

34. Degenerate Dimensions A degenerate dimension is a dimension key in the fact table that does not have its own dimension table, because all the interesting attributes have been placed in analytic dimensions. Features ◦ No description of its own ◦ No joining to an actual dimension table ◦ No attributes Example: transaction ID 34

35. Junk Dimension Example

36. Role-playing dimensions A table with multiple valid relationships between itself and a fact table is known as a role-playing dimension. For instance, a “Time" dimension can be used for “Order Day", as well as “Ship Date", or “Close Day".

37. Slowly Changing Dimensions The dimensions that have changeable attribute values are slowly changing dimensions (SCDs) The attribute values of SCD may change over time, which are critical to understand the dynamics of the business. The ability to track the changes of facts over time is critical to a DW/BI system. Examples ◦ Employees changed their departments ◦ Home moving (16.8% American moved per year) – zip code changes possible. More information 37

38. Three Types of SCD Type 1 SCD overwrites the existing attribute value with a new value. You don’t care about keeping track of historical values Type 2 SCD change tracking – ETL process creates a new row in the dimension table to capture the new values of the changed item Type 3 SCD – Similar to Type 2 SCD but only track current state and the original state; two additional attribute: SCD Start Date, SCD Initial Value

39. Aggregate Dimensions Situation: data at different levels of granularities Two resolutions ◦ Removing a dimension ◦ Rolling up a dimension’s hierarchy and provide a new, shrunken dimension at the aggregate level In the following case, the number of possible aggregates is the number of levels in each hierarchy of each dimension multiplied together. 39

40. Many-to-many or Multivalued Dimensions Relationship between a dimension table and fact table is called one-to-many: one row in the dimension table may join to many rows in the fact table. Many-to-many or Multivalued Dimensions are referred to as there are more than one row in a dimension table joining to multiple rows in a fact table Bridge table supports many-to-many relationship: ◦ fact-dimension ◦ dimension-dimension. 40

41. Many-to-many or Multivalued Dimensions A dimensional model for a sales fact that captures multiple sales reasons

42. HANDS-ON CASE: MAXIMUM MINIATURE MANUFACTURING

43. Maximum Miniatures Manufacturing – Designing Data Mart General business needs ◦ To analyze the statistics available from the manufacturing automation systems. The VP would like an interactive analysis tool, rather than printed reports, for the analysis. The manufacturing automation system controls all the machines to create figurines ◦ Filling a mold with the raw material ◦ Aiding the hardening of this materials ◦ Removal from the mod when hardening is complete ◦ Computerized painting of the figurines ◦ Curing the paint if necessary 43

44. Maximum Miniatures Manufacturing – Creating Data Mart Specific Business Needs ◦ Analyzing the following numbers  Dollar value of products sold  Number of products sold  Sale tax charged on products sold  Shipping charged on products sold ◦ These numbers should be viewable by:  Store  Sales Promotion  Product  Day, Month, Quarter, and Year  Customer  Sales Person 44

45. Data Requirements Number of accepted products by batch by product by machines by day Number of rejected products by batch by product by machines by day Elapsed time for molding and hardening by product by machine by day Elapsed time for painting and curing by curing type by product by machine by day Product rolls up into product subtype, which rolls up into product type Machine rolls up into machine type, which rolls up into country Day rolls up into month, which rolls up into quarter, which rolls up into year The information should be able to be filtered by machine manufacturer and purchase date of the machine 45

46. Business Need of Sales The VP of sales for Max Min, Inc. would like to analyze sales information. This information is collected by three OLTP systems: the Order Processing System, the Point of Sale (POS) system, and the MaxMin.com Online system. To analyze the following numbers ◦ Dollar value of products sold ◦ Number of products sold ◦ Sales tax charged on product sold ◦ Shipping charged on product sold These number should be viewable by: store, sales promotion, product, time, customer, sales person 46

47. 47 Snowflake Schema of the Data Mart Manufacturingfact DimProduct DimProductSubType DimProductType DimBatch DimMachine DimMachineType DimMaterial DimPlant DimCountry

48. EXERCISE 3

49. Exercise 3 – Creating a data mart with SSMS Learning Objectives ◦ How to design a dimensional model ◦ How to create a data mart with SSMS ◦ How to create a cube for a data mart. Tasks ◦ In SSMS, manually create the fact table and DimProduct table using SSMS (see the detailed information from file DW_MMM.PDF in the shared directory under ~\references) ◦ Import remaining tables from lin.mmm.empty to your own database ◦ Define the primary keys of tables and the relationships among them ◦ Create a ERD diagram with SSMS The primary key of the fact table is composed of three foreign keys plus one time dimension key: ProductCode, BatchNumber, MachineNumber, and DateOfManufacture. Deliverable: ◦ The printout of the screenshot of the ER diagram 49

50. Hints for Deploying the OLAP Cube Due to the security restrictions, you need to: ◦ Double click the entry in Data Source ◦ Type in your eRaider login information in the Impersonation Information panel ◦ Change the server to OREDB 50

51. The screenshot of impersonation information

52. The properties of the project After this step you can proceed to deploy the cube

53. Surrogate Key A natural key is a value that has meaning to the user, but ought to be unique for every row. A good example of a natural key would be a license plate number for a car. A surrogate key is an artificial value that has no meaning to the user, but is guaranteed to be unique by the database itself. Surrogate keys are created when doing data warehousing. They are new from the keys in original database They are also called meaningless keys, substitute keys, non-natural keys, artificial keys Specifically, surrogate keys are used in slowly changed dimensions (SCD) management 53

54. Benefits of surrogate keys Protect the DW/BI system from changes in the source system Allow the DW/BI system to integrate data from multiple source system Enable developers to add rows to dimensions that do not exist in the source system Provide the means for tacking changes in dimension Are efficient in the relational database and analysis services

55. Heterogeneous Products Several products with differentiated attributes Problem: sharing one dimension or use different dimension? Resolutions ◦ One family-oriented dimension with core fact and product tables plus specific information for each line of product 55