1. Chapter 6 SAS ® OLAP Cube Studio

2. Section 6.1 SAS OLAP Cube Studio Architecture

3. 3 What Is OLAP? OLAP (Online Analytical Processing) is a software technology that enables users to dynamically analyze data that is stored in multidimensional database tables. Because of the way the data and its relationships are stored, they are readily available for detailed queries and analysis.

4. 4 What Is OLAP? Central to the OLAP storage process are cubes. A cube is a set of data that is organized and structured in a hierarchical, multidimensional arrangement, often with numerous dimensions and levels of data.

5. 5 What Is OLAP? The classification information in the data is organized into dimensions. For example, a TIME dimension might have all input columns related to time (date, month, year) grouped as levels. Dimension levels can then be organized into a hierarchy. The hierarchy determines the logical order of the levels in a dimension. The logical order makes it possible for users to drill down into the cube data.

6. 6 What Is OLAP? Cubes also contain measures, which are based on numeric analysis columns in the input tables. These numeric values are summarized and stored in the cube for quick access and analysis during a query. Examples of measures include sales figures and operational costs.

7. 7 What Is SAS OLAP Cube Studio? The SAS OLAP Cube Studio is a Java interface for defining and building OLAP cubes in SAS ® 9. The Cube Designer wizard, which guides the user through the process of creating and registering a cube, is the main feature of SAS OLAP Cube Studio and is also available in SAS ETL Studio.

8. 8 What Is SAS OLAP Cube Studio? Other tasks that can be performed using the SAS OLAP Cube Studio include defining OLAP schemas in a SAS Metadata Repository registering library definitions for the tables used to build cubes registering the tables used to build cubes editing cube structure deleting cubes adding and deleting aggregations viewing cube properties.

9. 9 Interaction with SAS Application Servers SAS OLAP Cube Studio uses different types of application servers: SAS Metadata Server Required for purposes of creating and managing metadata for both the cube and the processes for building the cube. SAS Workspace Server Used to generate and submit SAS code, and to access information about SAS resources (that is, source tables) for the cube....

10. 10 NWAY Aggregation Concept (Optional) OLAP cubes usually contain an NWAY aggregation. The NWAY aggregation can be thought of as a table that contains the crossing of all dimension levels of the OLAP cube. Example:Examine the concept of the NWAY aggregation by discussing a summary table created from the SUMMARY procedure.

11. 11 SalesPersonMonthUnitsSalesAmount Jodi Lee Jodi Lee Jodi Lee Jodi Lee Jodi Lee Jodi Lee Todd Munch Todd Munch Todd Munch Todd Munch Todd Munch Todd Munch 112233112233112233112233 647586735795647586735795 300 200 350 250 400 300 350 150 250 350 450 250 NWAY Aggregation Concept (Optional) SalesData table:

12. 12 NWAY Aggregation Concept (Optional) The SalesData table can be summarized using the SUMMARY procedure, and the summary information output to a new table called SalesSummary. proc summary data=SalesData; class SalesPerson Month; var Units SalesAmount; output out=SalesSummary sum=TotalUnits TotalSales; run;

13. 13 NWAY Aggregation Concept (Optional) A simple listing of the new summary table, SalesSummary, can be created with a PROC PRINT step. proc print data=SalesSummary; run;

14. 14 SalesTotalTotal Obs PersonMonth_TYPE_ _FREQ_UnitsSales JLee TMunch JLee JLee JLee TMunch TMunch TMunch 011122333333011122333333 12 4 4 4 6 6 2 2 2 2 2 2 72 20 24 28 36 36 10 12 14 10 12 14 1 2 3 4 5 6 7 8 9 10 11 12.123..123123.123..123123 3600 1000 1200 1400 1800 1800 500 600 700 500 600 700 NWAY Aggregation Concept (Optional) SalesSummary table:

15. 15 NWAY Aggregation Concept (Optional) The _TYPE_ variable values display different types of summarizations. 1 0 12 72 3600 Review _TYPE_=0 (observation 1) in the SalesSummary table. This observation displays summarizations for no particular SalesPerson OR Month ; therefore, this summarization is overall summarizations for the entire data set. SalesTotalTotal Obs PersonMonth_TYPE_ _FREQ_UnitsSales

16. 16 Review _TYPE_=1 (observations 2 - 4). These observations display summarizations for each distinct Month across all SalesPerson values. 2 1 1 4 20 1000 3 2 1 4 24 1200 4 3 1 4 28 1400 NWAY Aggregation Concept (Optional) SalesTotalTotal Obs PersonMonth_TYPE_ _FREQ_UnitsSales

17. 17 Review _TYPE_=2 (observations 5 - 6). These observations display summarizations for each distinct SalesPerson across all Month values. 5 JLee 26361800 6 TMunch 2 636 1800 NWAY Aggregation Concept (Optional) SalesTotalTotal Obs PersonMonth_TYPE_ _FREQ_UnitsSales

18. 18 Review _TYPE_=3 (observations 7 - 12). These observations display summarizations for each distinct SalesPerson broken down by Month values. 7JLee13210500 8JLee23212600 9JLee33214700 10TMunch13210500 11TMunch2 3212600 12TMunch33214700 NWAY Aggregation Concept (Optional) SalesTotalTotal Obs PersonMonth_TYPE_ _FREQ_UnitsSales

19. 19 NWAY Aggregation Concept (Optional) For the _TYPE_=3 observations, notice that observations 7 through 9 can be summarized to obtain the values for observation 5. Similarly, observations 10 through 12 can be summarized to obtain the values for observation 6.

20. 20 5 JLee2636 1800 6 TMunch26 36 1800 7 JLee13210500 8 JLee23212600 9 JLee33214700 10 TMunch13210500 11 TMunch23212600 12 TMunch33214700... SalesTotalTotal Obs PersonMonth_TYPE_ _FREQ_UnitsSales

21. 21 5 JLee26361800 6 TMunch26361800 7 JLee13210500 8 JLee23212600 9 JLee33214700 10 TMunch13210500 11 TMunch23212600 12 TMunch33214700... SalesTotalTotal Obs PersonMonth_TYPE_ _FREQ_UnitsSales

22. 22 5 JLee 26361800 6 TMunch26361800 7 JLee13210500 8 JLee23212600 9 JLee33214700 10 TMunch13210500 11 TMunch23212600 12 TMunch33214700 SalesTotalTotal Obs PersonMonth_TYPE_ _FREQ_UnitsSales...

23. 23 NWAY Aggregation Concept (Optional) In addition to the previous example, notice that the _TYPE_=3 observations 7 and 10 can be summarized to obtain the values for observation 2. Similarly, observations 8 and 11 can be summarized to obtain the values for observation 3, and observations 9 and 12 can be summarized to obtain the values for observation 4.

24. 24 2 1 1 4 20 1000 3 2 1 4 24 1200 4 3 1 4 28 1400 7 JLee13 2 10500 8 JLee232 12600 9 JLee332 14700 10 TMunch132 10500 11 TMunch232 12600 12 TMunch332 14700 SalesTotalTotal Obs PersonMonth_TYPE_ _FREQ_UnitsSales...

25. 25 2 1 1 4 20 1000 3 2 1 4 24 1200 4 3 1 4 28 1400 7 JLee13210500 8 JLee23212600 9 JLee33214700 10 TMunch13210 500 11 TMunch23212600 12 TMunch33214700 SalesTotalTotal Obs PersonMonth_TYPE_ _FREQ_UnitsSales...

26. 26 2 1 1 4 20 1000 3 2 1 4 24 1200 4 3 1 4 28 1400 7 JLee 1 3 2 10 500 8 JLee 2 3 2 12 600 9 JLee 3 3 2 14 700 10 TMunch 1 3 2 10 500 11 TMunch 2 3 2 12 600 12 TMunch 3 3 2 14 700 SalesTotalTotal Obs PersonMonth_TYPE_ _FREQ_UnitsSales...

27. 27 2 1 1 4 20 1000 3 2 1 4 24 1200 4 3 1 4 28 1400 7JLee13210500 8JLee23212600 9JLee33214700 10TMunch13210500 11TMunch23212600 12TMunch33214700 SalesTotalTotal Obs PersonMonth_TYPE_ _FREQ_UnitsSales...

28. 28 proc summary data=SalesData NWAY; class SalesPerson Month; var Units SalesAmount; output out=SalesSummary sum=TotalUnits TotalSales; run; NWAY Aggregation Concept (Optional) If the NWAY option is used in the PROC SUMMARY statement... continued...

29. 29 NWAY Aggregation Concept (Optional)... the generated output table contains only the values where _TYPE_=3. 1JLee13210500 2JLee23212600 3JLee33214700 4TMunch13210500 5TMunch23212600 6TMunch33214700 SalesTotalTotal Obs PersonMonth_TYPE_ _FREQ_UnitsSales

30. 30 NWAY Aggregation Concept (Optional) The NWAY aggregation in an OLAP cube is similar to the output table that is generated from a PROC SUMMARY step using the NWAY option. All other aggregations in an OLAP cube can be generated from the NWAY aggregation.

31. Section 6.2 The SAS OLAP Cube Studio Interface

32. 32 SAS OLAP Cube Studio: The Interface When connected to a SAS Metadata Server, the main window of the SAS OLAP Cube Studio displays cube information specific to that metadata server. All cube administration tasks, such as creating and updating cubes, are initiated from this window. The main window has six main visual components: the menu bar the toolbar the shortcut bar the navigation tree the status bar the messages window.

33. 33 Tools, Menus, and Online Help The toolbar and pull-down menus provide fast access to a set of selected commands. The icons available on the toolbar depend on which window is active from within the interface. Menus and Tools

34. 34 The Shortcut Bar The Shortcut bar is populated with icons for each task an OLAP user would typically perform: Shortcut Bar...

35. 35 The Shortcut Bar The Shortcut bar is populated with icons for each task an OLAP user would typically perform: Options Used to change the selected SAS Application Server and to test the connection to a logical SAS Workspace Server. Create OLAP Schema Used for creating new OLAP schemas. Source Designer Used to design new source tables in a metadata repository. Cube Designer Used to define and build a new cube....

36. 36 Navigation Tree The navigation tree displays an organized list of the cubes, OLAP schemas, and tables registered in the selected SAS Metadata Repository. Navigation Tree

37. 37 Status Bar The status bar is located at the bottom of the SAS OLAP Cube Studio application window. It displays short status messages for the application. Status Bar

38. 38 Messages Window The Messages window displays application messages such as: Application Server Error: Attempted connection to Application Server failed. Verify that the server is running. To resize the Messages window, use your mouse to drag the top edge of the window up or down. When you close the window, your resized setting is saved. To toggle the display of the Messages window, select View  Message Window.

39. 39 Messages Window

40. 40 Using the Cube Designer Wizard The Cube Designer wizard is used to create and edit cube definitions that are stored in the active metadata repository build cubes based on the stored definitions.

41. 41 Using the Cube Designer Wizard The General window is used to specify the cube’s name and description. You also select a storage location for the cube’s definition and a storage location for the physical cube.

42. 42 Using the Cube Designer Wizard On the Input window, you specify the data source that provides the input data for your cube by selected an already registered data source or by defining a new data source.

43. 43 Using the Cube Designer Wizard The Drill-Through window is used to specify an optional drill-through table. Drill-through tables can be used by client applications to provide a view from processed data into the underlying data source.

44. 44 Using the Cube Designer Wizard From this wizard window you launch the Dimension Designer wizard to define the cube’s dimensions and their associated hierarchies and levels....

45. 45 Dimensions, Hierarchies, and Levels Dimension a group of closely related hierarchies. Hierarchy an arrangement of members of a dimension into levels that are based on parent-child relationships. Each hierarchy provides a navigational path that enables users to drill down to increasing levels of detail. Level an element of a dimension hierarchy. Levels describe the dimension from the highest (most summarized) level to the lowest (most detailed) level....

46. 46 Using the Cube Designer Wizard The Member Property window is where you can add, modify, and delete member properties for the levels in the cube.

47. 47 Using the Cube Designer Wizard In the Generated Aggregations window, you define aggregations to be generated for the cube in addition to whether the NWAY aggregation (the crossing of all dimension levels) is automatically generated.

48. 48 Using the Cube Designer Wizard The Finish window is where you review the details of the cube that you just defined and choose whether to only save the cube’s definition to the active metadata repository, or to save the cube's definition and build the cube....

49. 49 SAS OLAP Cube Studio Case Study Tasks Goal: Build a cube in order to examine trends in length of delivery over time and for different types of orders. OrderFact OrionStar Time Dimension Order_Year Order_Qtr Order_Month Product Dimension Order_Type Product_ID Measures Delivery_Time: Average Minimum Maximum Quantity: Average Total_Retail_Price: Sum...

50. 50 This demonstration illustrates building a cube using SAS OLAP Cube Studio. Building a Cube Register Source Tables Define Data Libraries Create ETL Jobs Define Target Tables Create OLAP Cubes View and Analyze Data Create Stored Processes Create Reports Create Information Maps Use the Information Delivery Portal Metadata

51. 51 This exercise reinforces the concepts discussed previously. Exercises Register Source Tables Define Data Libraries Create ETL Jobs Define Target Tables Create OLAP Cubes View and Analyze Data Create Stored Processes Create Reports Create Information Maps Use the Information Delivery Portal Metadata

52. Section 6.3 SAS OLAP Cube Studio and Microsoft Excel

53. 53 Viewing Cube Data In SAS ® 9 there are several options for viewing cube data including: SAS Enterprise Guide 3 the SAS Information Delivery Portal’s Visual Data Explorer portlet the SAS Web OLAP Viewer for Java Microsoft Excel Pivot Tables. This section shows how to use Microsoft Excel to view a SAS OLAP cube.

54. 54 Setting Cube Permissions While the metadata about the cube is available to all users by default, the data in the cube itself is not. Permissions must be added manually for the desired group of users to be able to display the cube data. These permissions are granted using SAS Management Console.

55. 55 This demonstration illustrates setting the appropriate permissions on a cube using the SAS Management Console so that users can view the cube data. Setting Cube Permissions

56. 56 This demonstration illustrates the necessary steps so users can view the cube data via Microsoft Excel. Importing the Cube into Microsoft Excel

57. 57 This exercise reinforces the concepts discussed previously. Exercises