Analytics: Server Architect (Siebel 7.7)

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1 Analytics: Server Architect (Siebel 7.7) Student Guide June 2005 Part # 10PO2-ASAS D44608GC10 Edition 1.0 D44917

2 Copyright 2005, 2006, Oracle. All rights reserved. Disclaimer This document contains proprietary information and is protected by copyright and other intellectual property laws. You may copy and print this document solely for your own use in an Oracle training course. The document may not be modified or altered in any way. Except where your use constitutes "fair use" under copyright law, you may not use, share, download, upload, copy, print, display, perform, reproduce, publish, license, post, transmit, or distribute this document in whole or in part without the express authorization of Oracle. The information contained in this document is subject to change without notice. If you find any problems in the document, please report them in writing to: Oracle University, 500 Oracle Parkway, Redwood Shores, California USA. This document is not warranted to be error-free. Restricted Rights Notice If this documentation is delivered to the United States Government or anyone using the documentation on behalf of the United States Government, the following notice is applicable: U.S. GOVERNMENT RIGHTS The U.S. Government s rights to use, modify, reproduce, release, perform, display, or disclose these training materials are restricted by the terms of the applicable Oracle license agreement and/or the applicable U.S. Government contract. Trademark Notice Oracle, JD Edwards, PeopleSoft, and Siebel are registered trademarks of Oracle Corporation and/or its affiliates. Other names may be trademarks of their respective owners.

3 Table of Contents Module i: Module 1: Module 2: Module 3: Module 4: Module 5: Module 6: Module 7: Module 8: Module 9: Module 10: Module 11: Module 12: Module 13: Module 14: Module 15: Module 16: Module 17: Course Introduction Building the Physical Layer of a Repository Building the Business Model Layer of a Repository Building the Presentation Layer of a Repository Testing and Validating a Repository Adding Multiple Sources to a Dimension Adding Calculations to a Fact Creating Dimensional Hierarchies and Level-Based Measures Using Aggregates Using Partitions or Fragments Using Repository Variables Modeling Time Series Data Modeling Slowly Changing Dimensions Modeling Extension Tables Analytics Security Cache Management Modeling Leading Practices Wrap-Up i

4 ii

5 Course Introduction i.1 i Module i: Course Introduction

6 Course Introduction i.2 Module Agenda This module provides an introduction to: Instructor and class participants Training site information Course: Audience Prerequisites Goal Objectives Methodology Materials Agenda Module i: Course Introduction 2of 14

7 Course Introduction i.3 Instructor and Class Participants i Who are you? Name Company Role What is your prior experience? Business intelligence Data warehouse design Database design Siebel applications How do you expect to benefit from this course? Module i: Course Introduction 3of 14

8 Course Introduction i.4 Training Site Information Bathrooms Class duration and breaks Telephones Meals and refreshments Fire Exits Questions? Module i: Course Introduction 4of 14

9 Course Introduction i.5 Course Audience i This course is designed for implementation teams Technical architects Technical business analysts Configurators/developers Application administrators Database administrators Module i: Course Introduction 5of 14

10 Course Introduction i.6 Course Prerequisites Required Analytics: Overview (Siebel 7.7) Recommended Domain experience in business intelligence, data warehouse design, data modeling, and database design Module i: Course Introduction 6of 14

11 Course Introduction i.7 Course Goal i Enable students to build and deploy a Siebel Analytics repository Module i: Course Introduction 7of 14

12 Course Introduction i.8 Course Objectives Build the Physical, Business Model and Mapping, and Presentation layers of a Siebel Analytics repository Test and validate a repository Add measures to a Fact table Build dimensional hierarchies and level-based measures Use partitions, fragments, and aggregates to enhance Analytics Server performance Model time series data and slowly changing dimensions Implement Analytics security to restrict access to sensitive data Manage Analytics cache Employ recommended implementation methodology and best practices Module i: Course Introduction 8of 14

13 Course Introduction i.9 Course Methodology i Subject matter is delivered through: Lecture and slide presentations Software demonstrations Class discussions Hands-on labs Module i: Course Introduction 9of 14

14 Course Introduction i.10 Course Materials Student Guide All slides presented during lecture Lab Guide Hands-on lab exercises and solutions Module i: Course Introduction 10 of 14

15 Course Introduction i.11 Course Agenda Day One Module i: Course Introduction Module 1: Building the Physical Layer of a Repository Module 2: Building the Business Model Layer of a Repository Module 3: Building the Presentation Layer of a Repository Module 4: Testing and Validating a Repository Module 5: Adding Multiple Sources to a Dimension i Module i: Course Introduction 11 of 14

16 Course Introduction i.12 Course Agenda Continued Day Two Module 6: Adding Calculations to a Fact Module 7: Creating Dimensional Hierarchies and Level-Based Measures Module 8: Using Aggregates Module 9: Using Partitions or Fragments Module 10: Using Repository Variables Module 11: Modeling Time Series Data Module i: Course Introduction 12 of 14

17 Course Introduction i.13 Course Agenda Continued Day Three Module 12: Modeling Slowly Changing Dimensions Module 13: Modeling Extension Tables Module 14: Analytics Security Module 15: Cache Management Module 16: Modeling Leading Practices Module 17: Wrap-Up i Module i: Course Introduction 13 of 14

18 Course Introduction i.14 Summary This module provided an introduction to the: Instructor and class participants Training site information Course: Audience Prerequisites Goal Objectives Methodology Materials Agenda Module i: Course Introduction 14 of 14

19 Building the Physical Layer of a Repository 1.1 Module 1: Building the Physical Layer of a Repository 1

20 Building the Physical Layer of a Repository 1.2 Module Objectives After completing this module you will be able to: Identify the objects in the Physical layer of a repository Define a System Data Source Name (DSN) for a data source Build the Physical layer Why you need to know: Enables you to perform the first step of creating a repository, building the Physical layer Provides the access to the data sources against which users will generate reports Module 1: Building the Physical Layer of a Repository 2of 29

21 Building the Physical Layer of a Repository 1.3 Physical Layer Contains objects representing the physical data sources to which the Siebel Analytics Server submits queries May contain multiple data sources Created using the Analytics Administration Tool Typically the first layer built in the repository 1 Data sources Module 1: Building the Physical Layer of a Repository 3of 29

22 Building the Physical Layer of a Repository 1.4 Physical Layer Objects Expand a data source object to display the objects it contains, such as: Connection pool Specifies connection between Siebel Analytics Server and data source Schema folder Contains physical schema of tables and columns of a data source Connection pool Catalog folder Data source Schema folder Tables Columns Key Module 1: Building the Physical Layer of a Repository 4of 29

23 Building the Physical Layer of a Repository 1.5 Data Sources Siebel Analytics Server can access data stored in: Normalized schema databases Star or snowflake schema databases Flat files Spreadsheets XMLA And so on 1 Module 1: Building the Physical Layer of a Repository 5of 29

24 Building the Physical Layer of a Repository 1.6 Data Source Connection Connectivity to the data source is established through: An ODBC Data Source (generic) driver Native drivers for Oracle and DB2 Data source to access an MS SQL Server database Module 1: Building the Physical Layer of a Repository 6of 29

25 Building the Physical Layer of a Repository 1.7 Connection Pool Defines how the Siebel Analytics Server connects to the data source Specifies the ODBC or native data source name Allows multiple users to share a pool of database connections May create multiple connection pools to improve performance for groups of users 1 ODBC Data Source name Module 1: Building the Physical Layer of a Repository 7of 29

26 Building the Physical Layer of a Repository 1.8 Tables and Columns Represent the physical structure of the data Import only the tables and columns needed for analytical processing Table Columns Module 1: Building the Physical Layer of a Repository 8of 29

27 Building the Physical Layer of a Repository 1.9 Physical Table Object Types Specify physical table object type in the Object Type drop-down list in the General tab of the Physical Table dialog box: None physical table object represents a physical table Alias physical table object is an alias to another physical table Stored Proc physical table object is a stored procedure Select physical table object is a Select statement 1 Module 1: Building the Physical Layer of a Repository 9of 29

28 Building the Physical Layer of a Repository 1.10 Alias Object Type Virtual physical table object that points to a physical table object Alias object type Alias points to this table Select valid physical table name from list Module 1: Building the Physical Layer of a Repository 10 of 29

29 Building the Physical Layer of a Repository 1.11 Select Statement Object Type Specifies that the physical table object is a select statement Requests for this object execute the select statement 1 Select object type Type select statement Module 1: Building the Physical Layer of a Repository 11 of 29

30 Building the Physical Layer of a Repository 1.12 Key Columns Primary key Uniquely identifies a single row of data Comprised of a column or set of columns Set of columns represent a compound or composite key Identified by the Key icon Foreign key Refers to the primary key columns in another table Composed of a column or set of columns Module 1: Building the Physical Layer of a Repository 12 of 29

31 Building the Physical Layer of a Repository 1.13 Joins Represent the primary key-foreign key relationships between tables in the Physical layer Complex joins are used to express relationships that do not involve a primary key-foreign key relationship Used to formulate the join when building the SQL 1 Double-click to view properties Joins Module 1: Building the Physical Layer of a Repository 13 of 29

32 Building the Physical Layer of a Repository 1.14 ABC Example Data sources for ABC reside in a relational database containing tables with: Invoice data Customer data Product data Period data Database or Data Source Source data Supplier Sales Import metadata using Analytics Administration Tool Module 1: Building the Physical Layer of a Repository 14 of 29

33 Building the Physical Layer of a Repository /5 Implementation Steps Define System Data Source Name (DSN) for data sources 1 Test connectivity to data sources Import data source schemas Edit connection pool properties Define physical keys and join conditions if they were not imported Module 1: Building the Physical Layer of a Repository 15 of 29

34 Building the Physical Layer of a Repository /5 Define System DSN for Data Sources Use the ODBC Data Source Administrator to define a System DSN for each data source Provides the Analytics Server with information to connect to a data source Module 1: Building the Physical Layer of a Repository 16 of 29

35 Building the Physical Layer of a Repository /5 Test Connectivity to Data Sources Use the Siebel Analytics ODBC Client Tool to test the connection to physical data sources Start > Programs > Siebel Analytics > Siebel Analytics ODBC Client 1 Select Utility > Enter SQL Query to execute a query to verify your connection to the data source Select File > Open Database to select the desired DSN Module 1: Building the Physical Layer of a Repository 17 of 29

36 Building the Physical Layer of a Repository /5 Import Data Source Schemas Use the Siebel Analytics Administration Tool to import data source schema Select the ODBC source to import metadata into the Physical layer Select File > Import > from Database Module 1: Building the Physical Layer of a Repository 18 of 29

37 Building the Physical Layer of a Repository /5 Select Tables and Columns for Import Select the tables and columns needed to support the business model Limit to tables and columns needed to support the users' analytic requirements Use filter to locate specific tables for import 1 Import the tables and columns identified in business model Tables and keys selected by default Module 1: Building the Physical Layer of a Repository 19 of 29

38 Building the Physical Layer of a Repository /5 Import Keys and Joins Include the tables used to define joins Keys and foreign keys are imported automatically only if they are defined in the source data If primary key-foreign key rules have been defined in the source data, they will automatically be created during the import; otherwise you will need to define the keys manually Module 1: Building the Physical Layer of a Repository 20 of 29

39 Building the Physical Layer of a Repository /5 Edit Connection Pool Properties Open the Connection Pool properties dialog box to ensure that the Data Source Name field indicates the correct System DSN Specify a distinct name for the Connection Pool 1 Provide distinct name User name and password for data source Maximum number of connections allowed for this connection pool System DSN that connects to the data source Enable connection pooling Module 1: Building the Physical Layer of a Repository 21 of 29

40 Building the Physical Layer of a Repository /5 Define Physical Keys and Join Conditions The Administration Tool allows you to define physical keys and joins that were not imported automatically, in several different ways: Define primary keys Using the Physical Table > Keys tab Define joins and foreign keys Using the Physical Table Diagram Using the Joins Manager Module 1: Building the Physical Layer of a Repository 22 of 29

41 Building the Physical Layer of a Repository /5 Define Primary Keys Open the Table properties dialog box to define key column(s) Right-click and select Properties, or double-click table object 1 Select the Keys tab and click the New button Check the appropriate Column check box Module 1: Building the Physical Layer of a Repository 23 of 29

42 Building the Physical Layer of a Repository /5 Define Joins Using Physical Table Diagram Use the Physical Table Diagram editor to: View the table schema and joins Define physical foreign keys and complex joins Use complex join in the Physical layer to create joins over nonforeign key and primary key columns Use Physical Diagram icon or right-click object to open editor Double-click to open the Joins properties box Module 1: Building the Physical Layer of a Repository 24 of 29

43 Building the Physical Layer of a Repository /5 Define Foreign Keys Use the New Foreign Key icon to join the tables 1. Select the Foreign Key icon 1 5. Select key columns 2. Select "one" table in relationship 3. Select "many" table in relationship 4. Physical Foreign Key dialog opens 6. Join expression: first table selected maintains primary key; second table selected maintains foreign key to first table Module 1: Building the Physical Layer of a Repository 25 of 29

44 Building the Physical Layer of a Repository /5 Define Joins Using Join Manager Use the Joins Manager editor to: View join relationships Create physical foreign keys and complex joins Select Manage > Joins in Administration Tool Module 1: Building the Physical Layer of a Repository 26 of 29

45 Building the Physical Layer of a Repository 1.27 Many-to-Many Relationships Sometimes required between dimension tables and fact tables Require bridge tables that reside between the fact and dimension table in the data warehouse Bridge table stores multiple records corresponding to that dimension Example: Bridge table 1 Module 1: Building the Physical Layer of a Repository 27 of 29

46 Building the Physical Layer of a Repository 1.28 Summary This module showed you how to: Identify the objects in the Physical layer of a repository Define a System Data Source Name (DSN) for a data source Build the Physical layer Module 1: Building the Physical Layer of a Repository 28 of 29

47 Building the Physical Layer of a Repository 1.29 Lab In the lab you will: Build the Physical layer 1 Module 1: Building the Physical Layer of a Repository 29 of 29

48 Building the Physical Layer of a Repository 1.30

49 Building the Business Model Layer of a Repository 2.1 Module 2: Building the Business Model Layer of a Repository 2

50 Building the Business Model Layer of a Repository 2.2 Module Objectives After completing this module you will be able to: Identify the objects of the Business Model and Mapping layer of a repository Build the business model Create simple measures Why you need to know: Enables you to perform the second step of creating a repository, building the Business Model and Mapping layer Module 2: Building the Business Model Layer of a Repository 2of 28

51 Building the Business Model Layer of a Repository 2.3 Business Model and Mapping Layer Physical schemas are simplified and reorganized to form the basis of the users view of the data 2 Module 2: Building the Business Model Layer of a Repository 3of 28

52 Building the Business Model Layer of a Repository 2.4 Business Model and Mapping Layer Objects The Business Model and Mapping Layer contains business model objects Business model Dimension hierarchies Logical dimension tables Logical fact table Logical table source Logical columns Module 2: Building the Business Model Layer of a Repository 4of 28

53 Building the Business Model Layer of a Repository 2.5 Business Model Mappings Business model objects map to source data objects in the Physical layer Business models may map to multiple data sources Logical tables may map to multiple physical tables Logical columns may map to multiple physical columns 2 Object name changed Module 2: Building the Business Model Layer of a Repository 5of 28

54 Building the Business Model Layer of a Repository 2.6 0/7 Business Model and Mapping Layer Objects Business model object Logical tables Logical table sources Column mappings Conditional expression Defining source content Where clause filter Logical columns Logical primary keys Logical joins Complex join Measures Module 2: Building the Business Model Layer of a Repository 6of 28

55 Building the Business Model Layer of a Repository 2.7 1/7 Business Model Object Highest level in the Business Model and Mapping layer Contains business model definitions and the mappings from logical to physical tables for the business model Business model object 2 Module 2: Building the Business Model Layer of a Repository 7of 28

56 Building the Business Model Layer of a Repository 2.8 2/7 Logical Tables Define data source mappings Represent fact or dimension data Have one or more logical table sources Have one or more logical columns Logical tables Logical table source Logical columns Module 2: Building the Business Model Layer of a Repository 8of 28

57 Building the Business Model Layer of a Repository 2.9 3/7 Logical Table Sources Define mappings between logical table and physical table Can include more than one physical table, but multiple tables must join 2 Identifies physical source Module 2: Building the Business Model Layer of a Repository 9of 28

58 Building the Business Model Layer of a Repository /7 Column Mappings Use the Column Mapping tab of the Logical Table Source dialog box to build, view, or modify logical to physical mappings Double-click logical table source to open dialog box Build or view mappings Click ellipsis to open Expression Builder Use Expression Builder to specify transformations between Physical and Business Model layer Module 2: Building the Business Model Layer of a Repository 10 of 28

59 Building the Business Model Layer of a Repository /7 Conditional Expression Calculations that use CASE statements Use Expression Builder to construct conditional expression 2 Expressions folder contains building block for conditional expressions Module 2: Building the Business Model Layer of a Repository 11 of 28

60 Building the Business Model Layer of a Repository /7 Defining Source Content Use the Content tab of the Logical Table Source dialog box to define: Aggregate table content definitions for the source Fragmented table definitions for the source Where clauses to limit the number of rows returned Aggregate content Fragmentation content Where clause Module 2: Building the Business Model Layer of a Repository 12 of 28

61 Building the Business Model Layer of a Repository /7 Where Clause Filter Specify Where clause filters in the Where Clause Filter window if you want to limit the number of rows returned Specify WHERE clause filter in Logical Table Source properties window 2 Use Expression Builder to build filter Click Ellipsis button to open Expression Builder Module 2: Building the Business Model Layer of a Repository 13 of 28

62 Building the Business Model Layer of a Repository /7 Logical Columns Represent the business view of the data Map to physical columns in the Physical layer May map to other logical columns Display in a tree structure expanded out from the logical table to which they belong Primary key columns display with a key icon Each logical column may map to many physical columns from many physical tables Module 2: Building the Business Model Layer of a Repository 14 of 28

63 Building the Business Model Layer of a Repository /7 Logical Primary Keys Define unique identifiers for logical tables Consist of one or more logical columns Are automatically defined when keys and joins are dragged from Physical layer, but can be changed 2 Logical primary keys Module 2: Building the Business Model Layer of a Repository 15 of 28

64 Building the Business Model Layer of a Repository /7 Logical Joins Logical tables must be related through logical joins Helps the server understand the relationships between various objects of the business model Examining logical joins is an integral part of how the Siebel Analytics Server figures out how to construct the physical queries Helps the server determine which tables are logical fact tables Required for a valid business model In this layer, use complex joins, not foreign key joins Use Complex Join icon to create joins Fact table is at many end of logical join Module 2: Building the Business Model Layer of a Repository 16 of 28

65 Building the Business Model Layer of a Repository /7 Logical Complex Join Allows Analytics Server to make best decision about exact physical SQL to generate based on logical query path In contrast to a physical foreign key join, which forces a single join path between tables Complex join has no key relationship defined and no expression 2 Double-click to view join characteristics Module 2: Building the Business Model Layer of a Repository 17 of 28

66 Building the Business Model Layer of a Repository /7 Measures Calculations defining measurable quantities Created on logical columns in the fact table Have a defined aggregation rule Summation icon denotes an aggregation rule Module 2: Building the Business Model Layer of a Repository 18 of 28 Aggregate An aggregate amounts to a sum, total, count, or count distinct. An example is aggregate sales in the software market.

67 Building the Business Model Layer of a Repository 2.19 ABC Example Create a dimensional model to represent ABC's business Products and Customers are logical dimensions Sales is the central fact containing total sales, units ordered, and units shipped 2 Products Customers Sales Type Description Price Weight Region District Customer Address Dollars Units Ordered Units Shipped The performance measures that ABC wants to analyze Module 2: Building the Business Model Layer of a Repository 19 of 28

68 Building the Business Model Layer of a Repository /5 Implementation Steps 1. Create logical business model 2. Create logical tables and columns 3. Define logical joins 4. Modify logical tables and columns 5. Define measures Module 2: Building the Business Model Layer of a Repository 20 of 28

69 Building the Business Model Layer of a Repository /5 1. Create Logical Business Model Right-click inside the Business Model layer and select New Business Model 2 Create a new business model Module 2: Building the Business Model Layer of a Repository 21 of 28

70 Building the Business Model Layer of a Repository /5 2. Create Logical Tables and Columns Select Business Model Object > New Object > Logical Table or drag and drop physical table objects from Physical layer into the business model Model is not yet available for queries Logical tables map to physical tables Sources folder identifies physical source(s) for logical tables Keys automatically created if defined in Physical layer Logical columns map to physical columns Module 2: Building the Business Model Layer of a Repository 22 of 28

71 Building the Business Model Layer of a Repository /5 3. Define Logical Joins Define logical table joins in the Business Model layer using techniques similar to those used in the Physical layer Use complex joins Right-click object and select Business Model Diagram option Complex join has no key relationship defined and no expression 2 Use Complex Join icon Double-click to view join characteristics Module 2: Building the Business Model Layer of a Repository 23 of 28

72 Building the Business Model Layer of a Repository /5 4. Modify Logical Tables and Columns Use an object's properties window or the UI to: Rename tables and columns Reorder columns Add or delete tables and columns Add or delete sources Cut, copy, and paste objects Cut, copy, paste, delete, duplicate, and rename objects Change column order Add, edit, and remove columns Module 2: Building the Business Model Layer of a Repository 24 of 28

73 Building the Business Model Layer of a Repository /5 5. Define Measures Right-click the logical column and select Properties > Aggregation 2 Right-click and select Properties Specify aggregation rule Module 2: Building the Business Model Layer of a Repository 25 of 28

74 Building the Business Model Layer of a Repository 2.26 Leading Practices Use only complex joins in Business Model and Mapping layer If joined tables were dragged from Physical layer, replace foreign key joins with complex joins Rename logical columns Use names acceptable to the organization and understood by users Use short names to minimize browser or reporting real estate Create unique names for all columns Avoid using the same name as a logical table or business model Never delete logical columns that define contents of logical table sources Module 2: Building the Business Model Layer of a Repository 26 of 28

75 Building the Business Model Layer of a Repository 2.27 Summary This module showed you how to: Identify the objects of the Business Model and Mapping layer of a repository Build the business model Create simple measures 2 Module 2: Building the Business Model Layer of a Repository 27 of 28

76 Building the Business Model Layer of a Repository 2.28 Lab In the lab you will: Build the Business Model and Mapping layer Module 2: Building the Business Model Layer of a Repository 28 of 28

77 Building the Presentation Layer of a Repository 3.1 Module 3: Building the Presentation Layer of a Repository 3

78 Building the Presentation Layer of a Repository 3.2 Module Objectives After completing this module you will be able to: Identify the objects of the Presentation layer of a repository Modify the properties of the Presentation layer objects Build the Presentation layer of a repository Why you need to know: Enables you to perform the third step of creating a repository: building the Presentation layer of the repository Provides the actual presentation of a business model to users Module 3: Building the Presentation Layer of a Repository 2of 23

79 Building the Presentation Layer of a Repository 3.3 Presentation Layer Metadata that describes the users view of the business model Simplifies the business model and makes it easy for users to understand and query Exposes only the data meaningful to the users Organizes the data in a way that aligns with the way users think about the data Renames data as necessary for the set of users 3 Module 3: Building the Presentation Layer of a Repository 3of 23

80 Building the Presentation Layer of a Repository 3.4 Presentation Layer Continued Created using the Analytics Administration Tool Third layer built in the repository after Physical and Business Model and Mapping layers Module 3: Building the Presentation Layer of a Repository 4of 23

81 Building the Presentation Layer of a Repository 3.5 Presentation Catalogs Organize and simplify the business model for a set of users Refer to a single business model; cannot span business models Multiple presentation catalogs can refer to the same business model Catalog for user group 1 3 Catalog for user group 2 Both reference same business model Module 3: Building the Presentation Layer of a Repository 5of 23

82 Building the Presentation Layer of a Repository 3.6 Presentation Tables and Columns Define the interface the user uses to query the data from the data sources Appear as folders and columns in Siebel Answers Refer to a subset of the logical tables and logical columns in the Business Model and Mapping layer Use terminology that is meaningful to the user Presentation table Presentation column Module 3: Building the Presentation Layer of a Repository 6of 23

83 Building the Presentation Layer of a Repository 3.7 Nested Presentation Tables Gives the appearance of nested folders in Siebel Answers Prefix nested folder with hyphen and space: - <folder name> Place after the folder in which it nests Multiple folders can be nested Sales Facts nested under Facts 3 Hyphen and space omitted in Answers Module 3: Building the Presentation Layer of a Repository 7of 23

84 Building the Presentation Layer of a Repository 3.8 ABC Example Build the Presentation layer for ABC to expose: The dimension data: Customers, Periods, and Products The fact data: SalesFacts Module 3: Building the Presentation Layer of a Repository 8of 23

85 Building the Presentation Layer of a Repository 3.9 Implementation Steps Create a new presentation catalog Customize the presentation catalog 3 Module 3: Building the Presentation Layer of a Repository 9of 23

86 Building the Presentation Layer of a Repository 3.10 Create a New Presentation Catalog Drag and drop a business model from the Business Model and Mapping layer to the Presentation layer Select Edit > Duplicate to copy an existing catalog Dragging and dropping creates the new catalog Module 3: Building the Presentation Layer of a Repository 10 of 23

87 Building the Presentation Layer of a Repository 3.11 Create a New Presentation Catalog Continued Corresponding Presentation layer objects are automatically created for Business Model and Mapping layer objects Subject area becomes presentation catalog Logical tables become presentation tables 3 Logical columns become presentation columns Module 3: Building the Presentation Layer of a Repository 11 of 23

88 Building the Presentation Layer of a Repository 3.12 Customize the Presentation Catalog Organize and modify the presentation objects so that they make sense to the users Reorder tables and columns Rename tables and columns Remove columns that serve no business need Module 3: Building the Presentation Layer of a Repository 12 of 23

89 Building the Presentation Layer of a Repository 3.13 Reorder Tables Open the Presentation Catalog properties box and use the Up and Down buttons or drag and drop to reorder the tables Double-click Use the Presentation Tables tab and the Up and Down buttons to reorder tables 3 Module 3: Building the Presentation Layer of a Repository 13 of 23

90 Building the Presentation Layer of a Repository 3.14 Reorder Columns In the Presentation Table properties box, use the Columns tab and use the Up and Down buttons or drag and drop to reorder columns Select and move a group of columns Module 3: Building the Presentation Layer of a Repository 14 of 23

91 Building the Presentation Layer of a Repository 3.15 Rename Tables In the Presentation Table properties box, use the General tab to rename tables Use the General tab Double-click Custom name Check box to create custom name 3 Module 3: Building the Presentation Layer of a Repository 15 of 23

92 Building the Presentation Layer of a Repository 3.16 Alias An alias is created automatically when the name of a presentation table is changed Used to maintain compatibility with previously written queries Use the Aliases tab to view the previous name Module 3: Building the Presentation Layer of a Repository 16 of 23

93 Building the Presentation Layer of a Repository 3.17 Rename Columns Use the General tab to rename columns in the Presentation Column properties box Use the General tab Custom name 3 Uncheck to specify a name that is different from the logical column name Module 3: Building the Presentation Layer of a Repository 17 of 23

94 Building the Presentation Layer of a Repository 3.18 Remove Unneeded Columns Right-click and select Delete to remove unneeded columns Remove keys that are used only for processing, unless they have an intrinsic meaning, such as date Module 3: Building the Presentation Layer of a Repository 18 of 23

95 Building the Presentation Layer of a Repository 3.19 Considerations Presentation columns are automatically renamed when the corresponding logical object is renamed Presentation tables cannot have the same name as the presentation catalogs (error is received) Presentation objects can be deleted without affecting corresponding logical objects 3 Module 3: Building the Presentation Layer of a Repository 19 of 23

96 Building the Presentation Layer of a Repository 3.20 Considerations for Other Clients To expose the key columns to other ODBC clients, check the Export logical keys check box Exporting logical keys is irrelevant to Siebel Analytics Web users Expose the logical keys to other ODBC clients Module 3: Building the Presentation Layer of a Repository 20 of 23

97 Building the Presentation Layer of a Repository 3.21 Leading Practices Use meaningful names Names can not contain single quotes ('); Admin Tool prevents it Use of double quotes (") is permitted, but should be avoided Keep presentation object names unique Naming presentation columns the same as presentation tables can lead to inaccurate results Uniqueness allows SQL statements to be shorter because qualifiers are unnecessary Group objects in meaningful ways Columns must be in same business model Eliminate unneeded objects to reduce user confusion Limit number of objects in folder to 7 12 Use object description fields to convey information to users Keep names short to save space on reports 3 Module 3: Building the Presentation Layer of a Repository 21 of 23

98 Building the Presentation Layer of a Repository 3.22 Summary This module showed you how to: Identify the objects of the Presentation layer of a repository Modify the properties of the Presentation layer objects Build the Presentation layer of a repository Module 3: Building the Presentation Layer of a Repository 22 of 23

99 Building the Presentation Layer of a Repository 3.23 Lab In the lab you will: Build the Presentation layer 3 Module 3: Building the Presentation Layer of a Repository 23 of 23

100 Building the Presentation Layer of a Repository 3.24

101 Testing and Validating a Repository 4.1 Module 4: Testing and Validating a Repository 4

102 Testing and Validating a Repository 4.2 Module Objectives After completing this module you will be able to: Describe techniques for testing a repository Execute steps to validate and test a repository Why you need to know: Testing and validating the repository file before making it available to users helps ensure implementation success Module 4: Testing and Validating a Repository 2of 21

103 Testing and Validating a Repository 4.3 Validating the Repository Verifies that the business model yields correct results ABC has modeled the data requirements and now it is time to test and validate the repository Validation techniques include: Check repository for consistency Turn on logging Check business model using Siebel Analytics ODBC Client Use Siebel Answers Check results by inspecting SQL 4 Module 4: Testing and Validating a Repository 3of 21

104 Testing and Validating a Repository 4.4 Check Repository for Consistency A feature in the Administration Tool that checks the metadata in the repository for certain kinds of errors, including: Finding logical columns that are not mapped to physical sources Checking for undefined logical join conditions Determining if physical tables referenced in a business model are not joined to tables referenced in the business model Does not guarantee that the business model is constructed correctly Module 4: Testing and Validating a Repository 4of 21

105 Testing and Validating a Repository 4.5 Turn On Logging Test proper repository configuration by logging query activity Use logging for testing, debugging, and technical support Query activity gets logged in NQQuery.log file Enable for individual users Set logging level for each user whose queries you want logged Query logging is disabled by default Can quickly produce very large log files 4 Module 4: Testing and Validating a Repository 5of 21

106 Testing and Validating a Repository 4.6 Turn On Logging Continued To see the SQL generated, set logging level to 1 or 2 Level 1 Logs: User name, session ID, and request ID for each query SQL for the request using business model names Query status (success, failure, termination, or timeout) Elapsed times for query compilation, execution, query cache, and back-end database processing Level 2 Logs: All items for Level 1, plus: Repository name, business model name, presentation catalog name SQL for the request using physical data source syntax Queries issued against the cache Number of rows returned from a physical database Number of rows returned to the client Module 4: Testing and Validating a Repository 6of 21

107 Testing and Validating a Repository 4.7 Check Business Model Using Siebel Analytics ODBC Client ODBC Client is a tool that allows you to: View the database schema Create new SQL queries and save them Use the ODBC Client to validate the business model Verify that the data elements and joins work as expected Compare query results from ODBC Client with the results generated by Siebel Answers 4 Module 4: Testing and Validating a Repository 7of 21

108 Testing and Validating a Repository 4.8 ABC Example Test the business model before making it available to end users ABC users will query the SupplierSales subject area to get answers Module 4: Testing and Validating a Repository 8of 21

109 Testing and Validating a Repository 4.9 0/8 Verification Steps 1. Check consistency 2. Set query logging level 3. Define repository name in initialization file 4. Start Siebel Analytics Server 5. Use ODBC Client to verify repository 6. Execute SQL to verify results 4 7. Examine the query log file 8. Test business model using Siebel Answers Module 4: Testing and Validating a Repository 9of 21

110 Testing and Validating a Repository /8 1. Check Consistency Validate repository for certain kinds of errors Can validate entire repository or a single business model Select File > Check Global Consistency to check for compilation errors in the entire repository Right-click business model and select Check Consistency to check compilation errors in a particular business model Module 4: Testing and Validating a Repository 10 of 21

111 Testing and Validating a Repository /8 1. Check Consistency Continued Any errors are displayed in a dialog box Correct the errors and check for consistency again Repeat the process until there are no more errors 4 Module 4: Testing and Validating a Repository 11 of 21

112 Testing and Validating a Repository /8 2. Set Query Logging Level Select Manage > Security to open Security Manager Select Users (in left pane) to display the users (in right pane) Set the appropriate logging level for user Module 4: Testing and Validating a Repository 12 of 21

113 Testing and Validating a Repository /8 3. Define Repository Name in Initialization File Open c:\siebelanalytics\config\nqsconfig.ini In the repository section, add an entry for your repository file Format: logical_name = repository_file_name 4 ABC s repository file Module 4: Testing and Validating a Repository 13 of 21

114 Testing and Validating a Repository /8 4. Start Siebel Analytics Server Use Windows service manager to start the server and load the repository file into memory Upon Siebel Analytics Server startup, repository file defined in NQSConfig.ini becomes available for querying Right-click the service and select Start Module 4: Testing and Validating a Repository 14 of 21

115 Testing and Validating a Repository /8 5. Use ODBC Client to Verify Repository Test and verify that the repository displays the correct tables and columns Lists all tables in default repository Lists all columns in selected table 4 Lists attributes of selected column Module 4: Testing and Validating a Repository 15 of 21

116 Testing and Validating a Repository /8 6. Execute SQL to Verify Results Verify the logical and physical model Use logical column names in the SELECT clause Use logical table name or logical subject area name in the FROM clause Use any restrictions in a WHERE clause Enter SQL query and click the Execute button Module 4: Testing and Validating a Repository 16 of 21

117 Testing and Validating a Repository /8 6. Execute SQL to Verify Results Continued Examine the results To further verify, you can double-check these results with results from the Database Query Tool Verify that data is as expected 4 Module 4: Testing and Validating a Repository 17 of 21

118 Testing and Validating a Repository /8 7. Examine the Query Log File Check SQL generated by Siebel Analytics Server to verify that the server is querying correct table(s) and column(s) Open c:\siebelanalytics\log\nqquery.log Verify that joins and data elements work as expected Select statement executed by ODBC Client to verify repository SQL statement sent to the database Verify that correct repository, subject area, and presentation catalog were queried Number of rows returned Module 4: Testing and Validating a Repository 18 of 21

119 Testing and Validating a Repository /8 8. Test Business Model Using Siebel Answers Query business model to ask factual questions and get answers Compare results generated by Siebel Answers to the query results from the Siebel Analytics ODBC Client and in the NQQuery.log file ABC users query SupplierSales to get answers 4 Module 4: Testing and Validating a Repository 19 of 21

120 Testing and Validating a Repository 4.20 Summary This module showed you how to: Describe techniques for testing a repository Execute steps to validate and test a repository Module 4: Testing and Validating a Repository 20 of 21

121 Testing and Validating a Repository 4.21 Lab In the lab you will: Test and validate the repository file 4 Module 4: Testing and Validating a Repository 21 of 21

122 Testing and Validating a Repository 4.22

123 Adding Multiple Sources to a Dimension 5.1 Module 5: Adding Multiple Sources to a Dimension 5

124 Adding Multiple Sources to a Dimension 5.2 Module Objectives After completing this module you will be able to: Describe normalized and denormalized table structures in database designs Add multiple sources to an existing logical table source for a dimension in the business model Add a second table source to a dimension in the business model Why you need to know: Source data that you want to model is often stored in multiple tables and may not be in an ideal structure Module 5: Adding Multiple Sources to a Dimension 2of 25

125 Adding Multiple Sources to a Dimension 5.3 Table Structures Normalized table structures: Consist of many tables data has been split apart, or normalized Reduce data redundancy to improve performance Do not work well for queries that perform business data analysis Denormalized table structures: Follow a business model and are easier to understand Have data that may appear in several locations in the database Reduces the need for join paths Can take the form of a star schema 5 Module 5: Adding Multiple Sources to a Dimension 3of 25

126 Adding Multiple Sources to a Dimension 5.4 Business Challenge Data may be spread among several physical tables and needs to be combined into a single logical table Some data may be normalized; some data may be duplicated These normalized tables contain additional product information and are joined to D1_products Module 5: Adding Multiple Sources to a Dimension 4of 25

127 Adding Multiple Sources to a Dimension 5.5 Business Solution Model multiple physical sources for the logical table Where data is not duplicated across tables Add physical tables to an existing logical table source Where data is duplicated across tables Add a second logical table source that maps to the physical table that is the most economical source 5 Module 5: Adding Multiple Sources to a Dimension 5of 25

128 Adding Multiple Sources to a Dimension 5.6 ABC Example Add normalized tables that store price list, type description, and supplier information to the Product dimension in the SupplierSales business model The additional product information is contained in separate tables and joined to the root table, D1_products Module 5: Adding Multiple Sources to a Dimension 6of 25

129 Adding Multiple Sources to a Dimension 5.7 Implementation Steps Import physical sources and create supporting joins (as usual) Determine the physical columns Drag columns onto existing source Automatically adds the physical table sources, joins, and column mappings Specify the most economical source Rename the columns (as usual) Add columns to presentation catalog (as usual) 5 Module 5: Adding Multiple Sources to a Dimension 7of 25

130 Adding Multiple Sources to a Dimension 5.8 Determine the Physical Columns Locate the tables and columns in the Physical layer with the additional product information These physical columns contain the additional product information Module 5: Adding Multiple Sources to a Dimension 8of 25

131 Adding Multiple Sources to a Dimension 5.9 Drag Columns onto Existing Source Drag each physical column onto the existing logical table source for the Product dimension This Price physical column is dragged onto the D1_products Sources to add the price list information to the Products dimension 5 Module 5: Adding Multiple Sources to a Dimension 9of 25

132 Adding Multiple Sources to a Dimension 5.10 Drag Columns onto Existing Source Continued Creates the mapping, joins, and new columns in the Products dimension automatically New logical columns are added to the Products dimension Single source maps to multiple physical tables Joins automatically created Module 5: Adding Multiple Sources to a Dimension 10 of 25

133 Adding Multiple Sources to a Dimension 5.11 Drag Columns onto Existing Source Continued Maps the new logical columns to the physical tables automatically 5 Module 5: Adding Multiple Sources to a Dimension 11 of 25

134 Adding Multiple Sources to a Dimension 5.12 Alternative Method to Add Sources to an Existing Source Use source properties box to select physical tables Create the table mappings and joins Add logical columns manually Create logical column mappings manually Module 5: Adding Multiple Sources to a Dimension 12 of 25

135 Adding Multiple Sources to a Dimension 5.13 Specify Most Economical Source Where data is duplicated in the physical sources, determine the most efficient column mapping to physical sources For each logical column in a query, the Siebel Analytics Server uses the column mapping to determine the physical columns to query 5 Module 5: Adding Multiple Sources to a Dimension 13 of 25

136 Adding Multiple Sources to a Dimension 5.14 Example: Specify Most Economical Source ItemType is a column that was just added to the Product logical table it maps to a different physical table from the other logical columns Current mapping for ItemType logical column Module 5: Adding Multiple Sources to a Dimension 14 of 25

137 Adding Multiple Sources to a Dimension 5.15 Example: Specify Most Economical Source Continued A more economical source for ItemType is a table that stores information for other logical columns as well the server queries only one table Since D1_product_type stores TypeCode and ItemType information, it is a more economical source 5 Module 5: Adding Multiple Sources to a Dimension 15 of 25

138 Adding Multiple Sources to a Dimension 5.16 Example: Specify Most Economical Source Continued Adding a second logical table source to the Product dimension enables you to map ItemType and TypeCode to the physical table that stores information for both columns Allows creation of a new logical column A second logical table source is added to Products The two logical columns are now mapped to a more economical source the server queries only one table for both columns Module 5: Adding Multiple Sources to a Dimension 16 of 25

139 Adding Multiple Sources to a Dimension /3 Steps to Specify Most Economical Source 1. Create a new logical table source manually 2. Select the physical table 3. Specify the content 5 Module 5: Adding Multiple Sources to a Dimension 17 of 25

140 Adding Multiple Sources to a Dimension /3 1. Create a New Logical Table Source Manually Right-click the Sources folder for the Product dimension and select New Logical Table Source Module 5: Adding Multiple Sources to a Dimension 18 of 25

141 Adding Multiple Sources to a Dimension /3 2. Select the Physical Table Click the Add button and select the physical table 5 Module 5: Adding Multiple Sources to a Dimension 19 of 25

142 Adding Multiple Sources to a Dimension /3 3. Specify the Content The TypeCode logical column is now mapped to two physical tables TypeCode maps to two physical tables Module 5: Adding Multiple Sources to a Dimension 20 of 25

143 Adding Multiple Sources to a Dimension /3 3. Specify the Content Continued Multiple sources require content information Define the content for the TypeCode column on the Content tab of the second logical table source Lets Analytics Server know under what circumstance to use a particular source Specify the content for the source so the Analytics server knows it contains different content and queries d1_products_type instead of D1_products for TypeCode 5 Module 5: Adding Multiple Sources to a Dimension 21 of 25

144 Adding Multiple Sources to a Dimension 5.22 Rename the Columns Create names for the new columns that users can recognize in the business model Module 5: Adding Multiple Sources to a Dimension 22 of 25 Rename Wizard The Rename Wizard allows you to rename Presentation and Business Model and Mapping layer columns. It provides a convenient way to transform physical names to user-friendly names. To start the wizard, select Tools > Utilities > Rename Wizard.

145 Adding Multiple Sources to a Dimension 5.23 Add Columns to Presentation Catalog Drag the new logical columns onto the Product presentation table to make them available to users 5 Module 5: Adding Multiple Sources to a Dimension 23 of 25

146 Adding Multiple Sources to a Dimension 5.24 Summary This module showed you how to: Describe normalized and denormalized table structures in database designs Add multiple sources to an existing logical table source for a dimension in the business model Add a second table source to a dimension in the business model Module 5: Adding Multiple Sources to a Dimension 24 of 25

147 Adding Multiple Sources to a Dimension 5.25 Lab In the lab you will: Add multiple table sources to an existing logical table source of a dimension in the business model Add a second table source to a dimension in the business model 5 Module 5: Adding Multiple Sources to a Dimension 25 of 25

148 Adding Multiple Sources to a Dimension 5.26

149 Adding Calculations to a Fact 6.1 Module 6: Adding Calculations to a Fact 6

150 Adding Calculations to a Fact 6.2 Module Objectives After completing this module you will be able to: Describe a calculation measure and its use in a business model Implement new calculation measures Create new calculation measures based on existing logical columns Create new calculation measures based on physical columns Create new calculation measures using the Calculation Wizard Why you need to know: Calculations allow data to be processed to derive other measurements that are valuable to analysis Module 6: Adding Calculations to a Fact 2of 25

151 Adding Calculations to a Fact 6.3 Business Problem Businesses need to track the effectiveness of their operations Businesses want to use business language, not technical speak For example: Show me the accounts receivable balance as of Q3 Some information is derived from other data, such as: Derive money outstanding Compare amount billed with amount received Derive units backordered Compare units ordered with units shipped Module 6: Adding Calculations to a Fact 3of 25 6

152 Adding Calculations to a Fact 6.4 Solution The Siebel Analytics Server provides utilities to create calculation measures in the business model Use the Expression Builder utility to create a new logical column with a formula Select existing columns as objects in the formula Module 6: Adding Calculations to a Fact 4of 25

153 Adding Calculations to a Fact 6.5 ABC Example Payment for an order is not due until ABC ships the product This delay has a significant effect on ABC s cash flow The difference between the units ordered and the units shipped, called Cuts, is an important measure ABC wants to track Implement a new measure that calculates Cuts Module 6: Adding Calculations to a Fact 5of 25 6

154 Adding Calculations to a Fact 6.6 Implementation Methods There are three methods for creating calculation measures: Use existing logical columns as objects in a formula Use physical columns as objects in a formula Use the Calculation Wizard to automate the process Creates new columns that compare two existing columns Uses existing logical columns as objects in a formula Module 6: Adding Calculations to a Fact 6of 25

155 Adding Calculations to a Fact 6.7 0/3 Steps for Using Existing Logical Columns 1. Create a new logical column 2. Specify logical columns as the source 3. Build a formula Module 6: Adding Calculations to a Fact 7of 25 6

156 Adding Calculations to a Fact 6.8 1/3 1. Create a New Logical Column Right-click the fact table and select New Object > Logical Column Enter a name for the new column Module 6: Adding Calculations to a Fact 8of 25

157 Adding Calculations to a Fact 6.9 2/3 2. Specify Logical Columns as the Source Select the Use existing logical columns as the source check box Module 6: Adding Calculations to a Fact 9of 25 6

158 Adding Calculations to a Fact /3 3. Build a Formula Open the Expression Builder and build the formula Aggregation is applied based on the column aggregation in the formula Undo deletes the last item inserted into the formula Click the Ellipsis button to open the Expression Builder Select logical columns and click Insert or double-click to build the formula above Module 6: Adding Calculations to a Fact 10 of 25

159 Adding Calculations to a Fact /3 Steps for Using Physical Columns 1. Create a new logical column 2. Map the new column 3. Build the formula Module 6: Adding Calculations to a Fact 11 of 25 6

160 Adding Calculations to a Fact /3 1. Create a New Logical Column Right-click the fact table and select New Object > Logical Column Specify the aggregation rule Enter a name for the new column Module 6: Adding Calculations to a Fact 12 of 25

161 Adding Calculations to a Fact /3 2. Map the New Column Open the properties box of the logical table source Click the Ellipsis button to map the column using the Expression Builder Select to see unmapped columns Click to open the Expression Builder Module 6: Adding Calculations to a Fact 13 of 25 6

162 Adding Calculations to a Fact /3 3. Build the Formula Select the physical columns to construct the formula Building a formula using the physical columns also maps the columns Select physical columns to build the formula Module 6: Adding Calculations to a Fact 14 of 25

163 Adding Calculations to a Fact /2 Steps for Using the Calculation Wizard 1. Open the Calculation Wizard 2. Follow prompts: select columns and calculations Module 6: Adding Calculations to a Fact 15 of 25 6

164 Adding Calculations to a Fact /2 1. Open the Calculation Wizard Right-click a logical column you want to use in the calculation and select Calculation Wizard Module 6: Adding Calculations to a Fact 16 of 25

165 Adding Calculations to a Fact /2 2. Follow Prompts: Select Column and Calculations Select the logical column you want to compare and the calculation Units Shipped is the column to compare with Units Ordered Change and Percent Changes are the calculations that will be generated by the wizard Specify the result you want if the column is null Module 6: Adding Calculations to a Fact 17 of 25 6

166 Adding Calculations to a Fact /2 2. Follow Prompts: Creates Comparison Measures The Calculation Wizard can create up to four comparison measures (new logical columns) based on existing logical columns Module 6: Adding Calculations to a Fact 18 of 25

167 Adding Calculations to a Fact 6.19 Considerations: Using Physical Columns Use physical columns for calculation formulas that require an aggregation rule (such as sum or average) that is applied after the calculation select T79.ItemType as c1, T9.Month_in_Year as c2, sum(t55.unitordd - T55.UnitShpd) as c3 from D1_Calendar2 T9, D1_product_subtypes T75, D1_product_type T79, D1_products T82, D1_Orders2 T55 where T9.YYYYMMDD = T55.PeriodKey and T75.TypeCode = T79.TypeCode and T75.SubtypeCode = T82.SubtypeCode and T55.ProdKey = T82.ProductKey and 1999 = T9.Year Using physical columns, the SQL: Calculates the difference between units ordered and units shipped Applies aggregation rules group by T9.Month_in_Year, T79.ItemType Module 6: Adding Calculations to a Fact 19 of 25 6

168 Adding Calculations to a Fact 6.20 Example: Using Physical Columns Example: To accurately calculate total revenue, you would multiply the unit price by the number of units sold, and then sum the totals Module 6: Adding Calculations to a Fact 20 of 25

169 Adding Calculations to a Fact 6.21 Considerations: Using Logical Columns Use logical columns for calculation formulas that require an aggregation rule that is applied before the calculation Using logical columns, the SQL applies the aggregation rule (sum) to the units shipped and the units ordered first select distinct d1.c3 as c1, d1.c4 as c2, d1.c1 as c3, d1.c2 as c4, d1.c2 - d1.c1 as c5 from (select sum(t55.unitshpd) as c1, sum(t55.unitordd) as c2, T79.ItemType as c3, T9.Month_in_Year as c4 and then calculates the difference from D1_Calendar2 T9, d1_product_subtypes T75, D1_product_type T79, D1_products T82, D1_Orders2 T55 where T75.TypeCode = T79.TypeCode and T9.YYYYMMDD = T55.PeriodKey and T75.SubtypeCode = T82.SubtypeCode and T9.Year = 1999 and T55.ProdKey = T82.ProductKey group by T9.Month_in_Year, T79.ItemType Module 6: Adding Calculations to a Fact 21 of 25 6

170 Adding Calculations to a Fact 6.22 Example: Using Logical Columns To accurately calculate a unit price, you would total the dollars sold and divide by the total of the units sold Module 6: Adding Calculations to a Fact 22 of 25

171 Adding Calculations to a Fact 6.23 Considerations: Calculation Wizard Use the wizard for more involved calculations Uses existing logical columns use for calculation formulas that require an aggregation rule that is applied before the calculation Module 6: Adding Calculations to a Fact 23 of 25 6

172 Adding Calculations to a Fact 6.24 Summary This module showed you how to: Describe a calculation measure and its use in a business model Implement new calculation measures Create new calculation measures based on existing logical columns Create new calculation measures based on physical columns Create new calculation measures using the Calculation Wizard Module 6: Adding Calculations to a Fact 24 of 25

173 Adding Calculations to a Fact 6.25 Lab In the lab you will: Create new calculation measures Module 6: Adding Calculations to a Fact 25 of 25 6

174 Adding Calculations to a Fact 6.26

175 Creating Dimensional Hierarchies and Level-Based Measures 7.1 Module 7: Creating Dimensional Hierarchies and Level-Based Measures 7

176 Creating Dimensional Hierarchies and Level-Based Measures 7.2 Module Objectives After completing this module you will be able to: Create a dimensional hierarchy Use level-based measures Create a rank measure Why you need to know: Dimensional hierarchies introduce formal hierarchies into a business model, allowing the Siebel Analytics Server to calculate useful measures, and allowing users to drill down to more detail Module 7: Creating Dimensional Hierarchies and Level-Based Measures 2of 22

177 Creating Dimensional Hierarchies and Level-Based Measures 7.3 Set Up Dimensional Hierarchies Defines parent-child relationships within a dimension Establishes levels for data groupings and calculations Provides paths for drilldown Period Dimensional Hierarchy Levels Grand Total Years Quarters Months Days Module 7: Creating Dimensional Hierarchies and Level-Based Measures 3of 22 7

178 Creating Dimensional Hierarchies and Level-Based Measures 7.4 Level-Based Measures Columns whose values are calculated to a specific level of aggregation Period Dimensional Hierarchy Levels Measures Grand Total PeriodRevenue Years YearRevenue Quarters QuarterRevenue Months MonthRevenue Days DayRevenue Module 7: Creating Dimensional Hierarchies and Level-Based Measures 4of 22

179 Creating Dimensional Hierarchies and Level-Based Measures 7.5 Share Measures Calculated by dividing a measure by a level-based measure to calculate a percentage Period Dimension at the Month Level $10,000 (Total Sales for Salesperson XYZ for January) = $100,000 (Total Sales for January) 10 % (Share of Sales Attributable to Salesperson XYZ) Module 7: Creating Dimensional Hierarchies and Level-Based Measures 5of 22 7

180 Creating Dimensional Hierarchies and Level-Based Measures 7.6 Dimensional Hierarchy Example Dimensional hierarchy based on the Periods dimension table Dimension Level Column Measure Module 7: Creating Dimensional Hierarchies and Level-Based Measures 6of 22

181 Creating Dimensional Hierarchies and Level-Based Measures 7.7 ABC Example ABC has a table that represents its calendar or period information You have already logically modeled the table as Periods Logical table mappings Data in physical table Module 7: Creating Dimensional Hierarchies and Level-Based Measures 7of 22 7

182 Creating Dimensional Hierarchies and Level-Based Measures 7.8 ABC Example Continued Create a dimensional hierarchy to represent the relationship in the period data Create level-based measures for share and total Period Dimensional Hierarchy Total Drilldown for details Year Month Day Module 7: Creating Dimensional Hierarchies and Level-Based Measures 8of 22

183 Creating Dimensional Hierarchies and Level-Based Measures 7.9 0/6 Steps to Implement a Dimensional Hierarchy 1. Create a dimension object 2. Add parent-level object 3. Add child-level objects 4. Specify level columns 5. Create level keys 6. Create level-based measures Module 7: Creating Dimensional Hierarchies and Level-Based Measures 9of 22 7

184 Creating Dimensional Hierarchies and Level-Based Measures /6 1. Create a Dimension Object Select Dimension to create a new dimension object Dimension is distinguished by the multi-arrow icon Dimension icon Module 7: Creating Dimensional Hierarchies and Level-Based Measures 10 of 22

185 Creating Dimensional Hierarchies and Level-Based Measures /6 2. Add Parent-Level Object Create the highest level of the hierarchy grand total first Right-click the Dimension and select Logical Level Check to indicate highest level Module 7: Creating Dimensional Hierarchies and Level-Based Measures 11 of 22 7

186 Creating Dimensional Hierarchies and Level-Based Measures /6 3. Add Child-Level Objects Add subsequent levels in the hierarchy from the top down Number of elements represents the distinct count for the level Right-click the level and select New Object > Child Level Can be relative Allows rollup Module 7: Creating Dimensional Hierarchies and Level-Based Measures 12 of 22 Number of Elements Ratios between levels are used to determine the most efficient data source when multiple sources exist. Therefore, exact numbers will not produce different results than inexact, approximate ones.

187 Creating Dimensional Hierarchies and Level-Based Measures /6 4. Specify Level Columns Drag logical columns from logical table for the dimension data to associate them with a level Columns not dragged are assumed to be at the lowest level Dragging logical column from logical table to level in hierarchy associates the column with the level Results in Module 7: Creating Dimensional Hierarchies and Level-Based Measures 13 of 22 7

188 Creating Dimensional Hierarchies and Level-Based Measures /6 5. Create Level Keys Define the unique identifier for the level Provide context for drilldown (specifies the subset of data to include from the next level down) Level keys are required for all levels except Grand Total level Specify which level key to display when drilling down Module 7: Creating Dimensional Hierarchies and Level-Based Measures 14 of 22

189 Creating Dimensional Hierarchies and Level-Based Measures /6 6. Create Level-Based Measures Create a level-based measure for the grand total level that refers to an existing logical fact column 1. Create a new logical column in SalesFacts table 4. Associate column to grand total level in dimension 2. Use Expression Builder to build measure using existing logical columns 5. Measure added to dimension 3. Formula sums the physical column 6. New column in fact table Module 7: Creating Dimensional Hierarchies and Level-Based Measures 15 of 22 7

190 Creating Dimensional Hierarchies and Level-Based Measures 7.16 Create Other Measures Create other explicit measures to make measures from various levels available simultaneously Refers to Dollars Defined like the grand total but at lower level Sums the physical column to the year level Module 7: Creating Dimensional Hierarchies and Level-Based Measures 16 of 22

191 Creating Dimensional Hierarchies and Level-Based Measures 7.17 Create a Share Measure Create a new logical fact column that calculates the share by dividing the appropriate measure by a total measure For example: Divide the total per month by the total for all months Module 7: Creating Dimensional Hierarchies and Level-Based Measures 17 of 22 7

192 Creating Dimensional Hierarchies and Level-Based Measures 7.18 Create a Rank Measure Create a logical fact column Use the Rank display function and specify what to rank by Rank by dollars Note: This is a non-level based measure Module 7: Creating Dimensional Hierarchies and Level-Based Measures 18 of 22

193 Creating Dimensional Hierarchies and Level-Based Measures 7.19 Add Measures to Presentation Add new measures to the Presentation layer Module 7: Creating Dimensional Hierarchies and Level-Based Measures 19 of 22 7

194 Creating Dimensional Hierarchies and Level-Based Measures 7.20 Test Results Select measures to test results Drill down to check relative recalculations Drill down for details Share and rank are relative to period Module 7: Creating Dimensional Hierarchies and Level-Based Measures 20 of 22

195 Creating Dimensional Hierarchies and Level-Based Measures 7.21 Summary This module showed you how to: Create a dimensional hierarchy Use level-based measures Create a rank measure Module 7: Creating Dimensional Hierarchies and Level-Based Measures 21 of 22 7

196 Creating Dimensional Hierarchies and Level-Based Measures 7.22 Lab In the lab you will: Create dimensional hierarchies Create level-based measures Create rank measures Module 7: Creating Dimensional Hierarchies and Level-Based Measures 22 of 22

197 Using Aggregates 8.1 Module 8: Using Aggregates 8

198 Using Aggregates 8.2 Module Objectives After completing this module you will be able to: Describe aggregate tables Model aggregate tables to speed processing Why you need to know: Understanding these techniques will help you improve the performance of your application Module 8: Using Aggregates 2of 19

199 Using Aggregates 8.3 Business Challenge Data in fact and dimension sources is stored at the lowest level of detail for the analysis Often data needs to be rolled up or summarized during analysis Based on the amount of data, performing calculations at the time of the query can be resource-intensive and delay results to the user Indicates results are delayed Module 8: Using Aggregates 3of 19 8

200 Using Aggregates 8.4 Business Example User requests total products by region by year Siebel Analytics Server works with the data source to summarize fact data up to those levels in each dimensional hierarchy to provide the result Total Total Total Dimension Hierarchies Type Subtype Generic Product Hierarchy Region District Sales Rep Customer Hierarchy Year Quarter Month Period Hierarchy Specific Customer Day Module 8: Using Aggregates 4of 19

201 Using Aggregates 8.5 Business Solution Create physical tables in a data source that store precomputed measures for groupings of data Use these aggregate tables to process user queries Eliminates run-time calculations Delivers faster results to the users Detailed fact data table; lots of rows; longer time to read and calculate Aggregate fact table; fewer rows; quicker to read; precalculated Invoice Customer Key 1001 Period Key Product Key 100 Dollars Region West Year 1998 Total Dollars West Summarized Central Module 8: Using Aggregates 5of 19 8

202 Using Aggregates 8.6 Modeling Aggregates Model aggregate tables similarly to other multisource data Physical layer Create the ODBC data source (if necessary) Import physical sources Create physical joins Business Model layer Add sources to logical tables Specify aggregation content Identifies the type of data contained in the aggregate table so the Siebel Analytics Server can determine when to use it for queries Test the results New step No Presentation layer changes, since aggregate contains same data as detail that is already exposed Module 8: Using Aggregates 6of 19

203 Using Aggregates 8.7 ABC Example Use prebuilt aggregate tables to improve performance Must have matching levels of aggregation for fact and dimensions Sales (fact) aggregated to Sales Rep, Product Type, and Month levels Customer (dimension) aggregated to Sales Rep level Product (dimension) aggregated to Type level Period (dimension) aggregated to Month level Module 8: Using Aggregates 7of 19 8

204 Using Aggregates 8.8 Physical Layer for Aggregate Import the physical tables and create keys Aggregate fact table Aggregate dimension tables Module 8: Using Aggregates 8of 19

205 Using Aggregates 8.9 Physical Layer for Aggregate Continued Create joins between aggregate fact table and aggregate dimension tables Module 8: Using Aggregates 9of 19 8

206 Using Aggregates 8.10 Business Model Layer for Aggregate Create new logical sources that refer to the aggregate table(s) Aggregate as new source for facts Columns refer to detail and aggregate tables Repeat for dimensions Module 8: Using Aggregates 10 of 19

207 Using Aggregates 8.11 Business Model Layer for Aggregate Continued Use the Content tab to specify what level of data within each hierarchy the aggregate table source contains Select level Pick the appropriate levels in the hierarchies that match the levels the data is stored at in the table Repeat for dimensions Module 8: Using Aggregates 11 of 19 8

208 Using Aggregates 8.12 Business Model Layer for Aggregate Continued If dimensional hierarchies are not defined, use the Content tab to specify how the data is grouped within the aggregate table source Select Column Pick the appropriate table columns that match the grouping of data stored in the table Module 8: Using Aggregates 12 of 19

209 Using Aggregates 8.13 Test the Results Run queries and verify the aggregate tables are accessed appropriately by inspecting the log file Query requests data at the level stored in the aggregate Aggregate tables used instead of detail tables Module 8: Using Aggregates 13 of 19 8

210 Using Aggregates 8.14 Test the Results Continued The aggregate is used for requests for data at or above the level stored Year is at higher level than month data stored in aggregate, so aggregate is used Day is at lower level than month data stored in aggregate, so aggregate cannot be used Module 8: Using Aggregates 14 of 19

211 Using Aggregates 8.15 Test the Results Continued If results are unexpected, check for common issues that prevent successful aggregate table processing: Aggregation content is not specified correctly Aggregate dimension sources are not physically joined to aggregate fact table sources at the same level Dimensional source does not exist at the same level as a fact table source Aggregate dimension sources do not contain a column that maps to the primary key of the dimension hierarchy level Number of elements at this level is not specified correctly for dimension hierarchy levels Module 8: Using Aggregates 15 of 19 8

212 Using Aggregates 8.16 Considerations Using aggregates does not come without a price Additional time is required to build and load these tables Additional storage is necessary Only build the aggregates you need Module 8: Using Aggregates 16 of 19

213 Using Aggregates 8.17 Leading Practices Look at query patterns and build aggregates to speed up common queries that require summarized results Ensure that enough data is combined to offset the cost to build Detail to aggregate data ratio should be at least 50:1 Monitor and adjust to account for changing query patterns Module 8: Using Aggregates 17 of 19 8

214 Using Aggregates 8.18 Summary This module showed you how to: Describe aggregate tables Model aggregate tables to speed processing Module 8: Using Aggregates 18 of 19

215 Using Aggregates 8.19 Lab In the lab you will: Model aggregate data to speed processing Module 8: Using Aggregates 19 of 19 8

216 Using Aggregates 8.20

217 Using Partitions or Fragments 9.1 Module 9: Using Partitions or Fragments 9

218 Using Partitions or Fragments 9.2 Module Objectives After completing this module you will be able to: Identify reasons for segmenting data Describe techniques to model partitions Implement a value-based partition Why you need to know: Understanding how to model partitions can improve application performance and usability Module 9: Using Partitions or Fragments 2of 18

219 Using Partitions or Fragments 9.3 Business Challenge Seamlessly access and process data from multiple sources in an efficient manner to satisfy the user s request Requires that the application know where to go for what type of data and under what conditions Module 9: Using Partitions or Fragments 3of 18 9

220 Using Partitions or Fragments 9.4 Partition Business Example A database administrator (DBA) may decide to divide a large table into several smaller tables or partitions for performance Application must decide which table(s) to access and combine the results as necessary Large table multiple smaller tables becomes Module 9: Using Partitions or Fragments 4of 18

221 Using Partitions or Fragments 9.5 Partition Business Example Continued Requirements may demand the latest data be obtained from the transactional system and combined with data in the warehouse Application must decide when to access which data, or if both are required Older data Latest data USE OR OR BOTH? Module 9: Using Partitions or Fragments 5of 18 9

222 Using Partitions or Fragments 9.6 Aggregate Business Example A DBA may create a summarized (aggregate) table for performance Application must decide when it is appropriate to access the aggregate instead of the detailed table Detailed table Aggregate table OR Module 9: Using Partitions or Fragments 6of 18

223 Using Partitions or Fragments 9.7 Partition Is a subset of data, a data fragment, that contains part of the data for a fact or a dimension Combines as necessary with other data fragments May be: Fact-based Value-based Level-based Mixed Complex Module 9: Using Partitions or Fragments 7of 18 9

224 Using Partitions or Fragments 9.8 Fact-Based Tables have different columns and store different information Some column data may be duplicated between them Example: Main product data versus ancillary product data Main product data Ancillary product data Product Number Product Description Diet Soda Product Weight 12 Product Number Product Line Beverages Product Manager Smith Example: Actual sales versus quota targets Actual sales Quota targets Sales Rep Product Sale Sales Rep Product Quota Module 9: Using Partitions or Fragments 8of 18

225 Using Partitions or Fragments 9.9 Value-Based Tables have same columns and grain, but different values Example: Invoice data is stored separately for each region Invoices for Central Region InvNbr Dollars 1000 Region Central Invoices for West Region InvNbr Dollars 200 Region West Module 9: Using Partitions or Fragments 9of 18 Grain The grain of a table is the level at which the data in the table is stored. 9

226 Using Partitions or Fragments 9.10 Level-Based Tables have different grain and often have different columns Aggregate tables are an example of a level-based partition Example: Detailed sales data is summarized and stored by year and region Sales detailed data Sales by year and region Sales Rep Date Product Sale Year Total Dollars Region Central Central Module 9: Using Partitions or Fragments 10 of 18

227 Using Partitions or Fragments 9.11 Mixed Data is partitioned using more than one technique Example: Invoice sales data is partitioned by value and level Invoices by month for Central Invoices by year for Central Month Total Dollars Region Year Total Dollars Region Central Central Central Invoices by month for West Invoices by year for West Month Total Dollars Region Year Total Dollars Region West West West Module 9: Using Partitions or Fragments 11 of 18 9

228 Using Partitions or Fragments 9.12 Complex Other complex techniques may be required based on the data sources Conditional application of partitions Data for the past year is in monthly form only, and not yearly, while all other former years are in both Data for the West region is broken out, but all other regions are together Data duplications or omissions Data for the West region is stored in the main partition and a separate partition Data for the Central region is missing for 1997 Module 9: Using Partitions or Fragments 12 of 18

229 Using Partitions or Fragments 9.13 Solution Siebel Analytics Server handles the complexity of partitions so they are transparent to the user Requires the architect to model partitions similarly to other multisource data Create the ODBC data source (if necessary) Import physical sources Create physical joins Add sources to the Business Model and Mapping layer Map logical columns Specify fragmentation content New step Create presentation Test the results Module 9: Using Partitions or Fragments 13 of 18 9

230 Using Partitions or Fragments 9.14 ABC Example Replace the current, single source for customer data with two value-based partitions Customers with names starting with A M NewKey 1000 Name Clifton Lunch Customers with names starting with N Z NewKey 1002 Name Tong s Wok Module 9: Using Partitions or Fragments 14 of 18

231 Using Partitions or Fragments 9.15 Implementation Steps Import physical sources (as usual) Create physical joins (as usual) Add sources to the Business Model and Mapping layer (as usual) Specify fragmentation content New step Test the results (as usual) Module 9: Using Partitions or Fragments 15 of 18 9

232 Using Partitions or Fragments 9.16 Specify Fragmentation Content Use the expression builder to define what type of content the fragment contains Set the flag to specify whether to combine this fragment with other data Specifies customer data from A M is contained in this fragment Specifies to combine this fragment with the other Module 9: Using Partitions or Fragments 16 of 18

233 Using Partitions or Fragments 9.17 Summary This module showed you how to: Identify reasons for segmenting data Describe techniques to model partitions Implement a value-based partition Module 9: Using Partitions or Fragments 17 of 18 9

234 Using Partitions or Fragments 9.18 Lab In the lab you will: Implement partitions Module 9: Using Partitions or Fragments 18 of 18

235 Using Repository Variables 10.1 Module 10: Using Repository Variables 10

236 Using Repository Variables 10.2 Module Objectives After completing this module you will be able to: Describe session variables Describe repository variables Describe initialization blocks Implement a dynamic repository variable Why you need to know: Variables allow you to incorporate flexibility into your model Module 10: Using Repository Variables 2of 23

237 Using Repository Variables 10.3 Variables Contain values in memory that are used by the Siebel Analytics Server during its processing Are created and managed using the Variable Manager feature in the Analytics Administration Tool Consist of two types: Session variables Repository variables Module 10: Using Repository Variables 3of 23 10

238 Using Repository Variables 10.4 Session Variables Persist only while a user s session is active Receive values when users establish their sessions Come in two types: System Non-System Module 10: Using Repository Variables 4of 23

239 Using Repository Variables 10.5 System Session Variables Are predefined session variables that are used by the Siebel Analytics Server for specific purposes Have reserved names, which cannot be used for other kinds of variables Example: USER holds the value the user entered for login name Right-click for list For complete definitions, see the Siebel Analytics Server Administration Guide. Module 10: Using Repository Variables 5of 23 10

240 Using Repository Variables 10.6 Non-System Session Variables Are application-specific variables that are created by the implementation team Example: Capture the user s region and limit the records the user sees to only those for that region Module 10: Using Repository Variables 6of 23

241 Using Repository Variables 10.7 Repository Variables Persist from the time the Siebel Analytics Server is started until it is shut down Can be used instead of literals or constants in expression builders in the Administration Tool Siebel Analytics Server will substitute the value of the repository variable for the variable itself in the metadata Come in two types: Static Dynamic Module 10: Using Repository Variables 7of 23 10

242 Using Repository Variables 10.8 Static Repository Variables Are repository variables whose values are constant and do not change while the Siebel Analytics Server is running Values are initialized in the Variable dialog box Static value Module 10: Using Repository Variables 8of 23

243 Using Repository Variables 10.9 Dynamic Repository Variables Are repository variables whose values change according to a refresh schedule Values are initialized and refreshed using an initialization block Module 10: Using Repository Variables 9of 23 10

244 Using Repository Variables Initialization Blocks Are used to initialize session variables and dynamic repository variables Specify SQL to be run to populate one or more variables by accessing data sources Are invoked at Siebel Analytics Server startup and periodically rerun to refresh values for dynamic variables according to an established schedule New initialization block Select type of variable Module 10: Using Repository Variables 10 of 23

245 Using Repository Variables Initialization Blocks Example Determine the latest date contained in the source data and store it in variables Variables populated by this initialization block Data source to run the SQL against SQL to initialize the variables in order select list matches variable sequence Module 10: Using Repository Variables 11 of 23 10

246 Using Repository Variables Initialization Blocks Example Continued Set the refresh interval to determine when to reissue the SQL to update the variable values Rerun the SQL statement every hour Module 10: Using Repository Variables 12 of 23

247 Using Repository Variables ABC Example Use a variable to dynamically determine which set of customer data is in which partition and use it to specify the fragmentation content instead of hard-coding it as A M and N Z Fragment 1 will contain half the data NewKey 1000 Name Clifton Lunch Names will start with A but end is unknown Fragment 2 will contain the remaining data NewKey 1002 Name Tong s Wok Names will end with Z but start is unknown Module 10: Using Repository Variables 13 of 23 10

248 Using Repository Variables /6 Implementation Steps 1. Open the Variable Manager 2. Create an initialization block 3. Specify the initialization value 4. Create the variable 5. Use the variable 6. Test Module 10: Using Repository Variables 14 of 23

249 Using Repository Variables /6 1. Open the Variable Manager From the Analytics Administration Tool, select Manage > Variables Module 10: Using Repository Variables 15 of 23 10

250 Using Repository Variables /6 2. Create an Initialization Block From the Variable Manager, select Action > New > Initialization Block Enter a name for the initialization block Module 10: Using Repository Variables 16 of 23

251 Using Repository Variables /6 3. Specify the Initialization Value Select the connection pool of the data source to query from Select the source Module 10: Using Repository Variables 17 of 23 10

252 Using Repository Variables /6 3. Specify the Initialization Value Continued Enter the SQL to establish the values Selects the maximum customer name and extracts the first letter Module 10: Using Repository Variables 18 of 23

253 Using Repository Variables /6 4. Create the Variable Click the Variable tab and click New to create a new variable associated with the initialization block Enter a default literal value Module 10: Using Repository Variables 19 of 23 10

254 Using Repository Variables /6 5. Use the Variable Use VALUEOF( VariableName ) to use the value of the variable in an expression Picking the variable in the expression builder will add the VALUEOF function automatically Module 10: Using Repository Variables 20 of 23

255 Using Repository Variables /6 6. Test Verify that the variable gets initialized and used properly SQL for the initialization block is recorded in the log file Module 10: Using Repository Variables 21 of 23 10

256 Using Repository Variables Summary This module showed you how to: Describe session variables Describe repository variables Describe initialization blocks Implement a dynamic repository variable Module 10: Using Repository Variables 22 of 23

257 Using Repository Variables Lab In the lab you will: Create and use a dynamic repository variable Module 10: Using Repository Variables 23 of 23 10

258 Using Repository Variables 10.24

259 Modeling Time Series Data 11.1 Module 11: Modeling Time Series Data 11

260 Modeling Time Series Data 11.2 Module Objectives After completing this module you will be able to: Describe the use of time comparisons for business analysis Implement time comparison measures in the business model using the Time Series Wizard Why you need to know: Time comparisons allow you to compare current versus past performance and enable trend analysis The ability to compare business performance with previous time periods is fundamental to understanding a business Module 11: Modeling Time Series Data 2of 27

261 Modeling Time Series Data 11.3 Time Comparisons Analyze data spanning multiple time periods Give context to information Example: Compare this year s sales and last year s sales Sales This Year Sales Last Year How do they compare? Module 11: Modeling Time Series Data 3of 27 11

262 Modeling Time Series Data 11.4 Time Comparisons Continued Need to ask three different questions to get the result Question 1: What was this year s sales? Question 2: What was last year s sales? Question 3: How do they compare? Module 11: Modeling Time Series Data 4of 27

263 Modeling Time Series Data 11.5 Business Challenge: Time Comparisons SQL is not designed with business analysis in mind There is no direct way in SQL to compare this year to last year Query 1: What was this year s sales? Query 2: What was last year s sales? Requires three different queries to get the result Query 3: How do they compare? Module 11: Modeling Time Series Data 5of 27 11

264 Modeling Time Series Data 11.6 Solution: Time Comparisons Model time series data in the Siebel Analytics repository: Allow users to make one request for the desired result Run multiple queries in parallel to get the results Query 1: What was this year s sales? Query 2: What was last year s sales? User makes one request for all three queries using time comparison measures Query 3: How do they compare? Module 11: Modeling Time Series Data 6of 27

265 Modeling Time Series Data 11.7 ABC Example ABC wants to compare performance against performance in previous time periods to monitor its business Implement new measures in the business model to compare dollar performance to a year ago Show change in dollars Show change as percentage Example: In 1999, dollars increased in the central region compared to the previous year It was a change of 2.83% Module 11: Modeling Time Series Data 7of 27 11

266 Modeling Time Series Data 11.8 Solution: Time Series Wizard Is a utility of the Analytics Administration Tool Automates the process of modeling time series data Guides you through a series of prompts to model comparison measures Module 11: Modeling Time Series Data 8of 27

267 Modeling Time Series Data /6 Steps to Model Time Series Data 1. Examine physical sources 2. Start Time Series Wizard 3. Name comparison measures 4. Select period table 5. Select period table key 6. Select measures and calculations Module 11: Modeling Time Series Data 9of 27 11

268 Modeling Time Series Data /6 1. Examine Physical Sources Identify time periods you want to compare Identify the period table for keys to the previous time periods The months period table contains the YAGO column with the keys to the yearago totals These two fact tables contain aggregates of the year-ago totals Module 11: Modeling Time Series Data 10 of 27

269 Modeling Time Series Data /6 2. Start Time Series Wizard Right-click the business model and select the Time Series Wizard menu option Module 11: Modeling Time Series Data 11 of 27 11

270 Modeling Time Series Data /6 3. Name Comparison Measures The Time Series Wizard prompts you to create names for the comparison measures that it adds to the business model Year Ago will be added as a prefix to the new measures Module 11: Modeling Time Series Data 12 of 27

271 Modeling Time Series Data /6 4. Select Period Table The Time Series Wizard prompts you to select the period table used for the comparison measures The months table stores the keys to the year-ago totals Module 11: Modeling Time Series Data 13 of 27 11

272 Modeling Time Series Data /6 5. Select Period Table Key Select the column in the period table that provides the key to the comparison period YAGO is the column with keys to the year-ago measures Module 11: Modeling Time Series Data 14 of 27

273 Modeling Time Series Data /6 6. Select Measures and Calculations Select the measures you want to compare Select the calculations you want to generate Dollars is the measure selected for the comparison Change and the Percent Change of dollars are the calculations Specify the result you want if the measure returns zero or is not available Module 11: Modeling Time Series Data 15 of 27 11

274 Modeling Time Series Data /4 Times Series Wizard Output Aliases for fact tables Joins between period table and alias fact tables Comparison measures Logical table sources Module 11: Modeling Time Series Data 16 of 27

275 Modeling Time Series Data /4 Aliases for Fact Tables The Time Series Wizard creates aliases for each physical fact table that is used as a source Aliases of the two fact tables These alias tables are identical to fact tables but have a different name Module 11: Modeling Time Series Data 17 of 27 11

276 Modeling Time Series Data /4 Aliases for Fact Tables Continued Properties of the new alias physical table indicate that it was created by the Time Series Wizard Originating fact table This alias table was created by the Time Series Wizard Module 11: Modeling Time Series Data 18 of 27

277 Modeling Time Series Data /4 Joins Between Period Table and Alias Fact Tables The Time Series Wizard configures joins between the period table and the new alias fact tables Joins are complex because YAGO is not the key for the period table YAGO is the column in the period table with keys to the year-ago totals Module 11: Modeling Time Series Data 19 of 27 11

278 Modeling Time Series Data /4 Comparison Measures The Time Series Wizard adds comparison measures (new logical columns) to the fact table Comparison measures added to the fact table Module 11: Modeling Time Series Data 20 of 27

279 Modeling Time Series Data /4 Logical Table Sources The Time Series Wizard adds logical table sources to the fact table that maps the comparison measures to the physical tables New logical table sources map the comparison measures to the physical sources Module 11: Modeling Time Series Data 21 of 27 11

280 Modeling Time Series Data /4 Logical Table Sources Continued Properties of the new logical table sources indicate it was created by the Time Series Wizard Originating period table and fact table information Module 11: Modeling Time Series Data 22 of 27

281 Modeling Time Series Data /2 Testing the Repository Changes 1. Add comparison measures to presentation catalog 2. Test comparison measures Module 11: Modeling Time Series Data 23 of 27 11

282 Modeling Time Series Data /2 1. Add Comparison Measures to Presentation Catalog Drag the new comparison measures to the presentation catalog in the Presentation layer Module 11: Modeling Time Series Data 24 of 27

283 Modeling Time Series Data /2 2. Test Comparison Measures Use Siebel Answers to test the new measures Change Dollars Year Ago returns the difference in dollars sold compared to the previous year Since there is no data for 1997, Percent Change Dollars Year Ago returns NULL as specified in the wizard Module 11: Modeling Time Series Data 25 of 27 11

284 Modeling Time Series Data Summary This module showed you how to: Describe the use of time comparisons for business analysis Implement time comparison measures in the business model using the Time Series Wizard Module 11: Modeling Time Series Data 26 of 27

285 Modeling Time Series Data Lab In the lab you will: Create comparison measures to compare dollar performance from previous time periods Use the Time Series Wizard to create the time comparison measures Module 11: Modeling Time Series Data 27 of 27 11

286 Modeling Time Series Data 11.28

287 Modeling Slowly Changing Dimensions Module 12: Modeling Slowly Changing Dimensions

288 Modeling Slowly Changing Dimensions 12.2 Module Objectives After completing this module you will be able to: Describe techniques to address slowly changing dimensions Implement a solution to model slowly changing dimensions Why you need to know: Understanding techniques to deal with slowly changing dimensions allows you to design and model for your business needs Module 12: Modeling Slowly Changing Dimensions 2of 19

289 Modeling Slowly Changing Dimensions 12.3 Slowly Changing Dimensions (SCDs) 12 Refer to dimensions whose attribute values vary over time Example: The sales representative assigned to a customer may change over time Alley-Cats is transferred from Linda to Kathy Linda Rivero s Customers Clifton Lunch Club 427 Alley-Cats Kathy Lobo s Customers Royal Barbecue Tong s Wok Alley-Cats Module 12: Modeling Slowly Changing Dimensions 3of 19

290 Modeling Slowly Changing Dimensions 12.4 Challenge Provide capabilities to analyze data as is, as was, or both When calculating total sales, should the dollars for Alley-Cats be reported for Linda or Kathy, or split between them? Linda Rivero s Customers Clifton Lunch Club 427 Alley-Cats Kathy Lobo s Customers Royal Barbecue Tong s Wok Alley-Cats Module 12: Modeling Slowly Changing Dimensions 4of 19

291 Modeling Slowly Changing Dimensions 12.5 Techniques 12 Siebel Analytics can model any method of handling SCDs There are three common methods: Type One: overwriting history Type Two: preserving history Type Three: preserving a version of history Module 12: Modeling Slowly Changing Dimensions 5of 19

292 Modeling Slowly Changing Dimensions 12.6 Type One: Overwriting History One record is stored with the new data New value overwrites the old value Single column stores the attribute value Old value is lost Customer Sales Representative Customer Sales Representative Clifton Lunch Linda Rivero Clifton Lunch Linda Rivero Club 427 Linda Rivero Club 427 Linda Rivero Alley-Cats Linda Rivero Alley-Cats Kathy Lobo Royal Barbecue Kathy Lobo Royal Barbecue Kathy Lobo Tong s Wok Kathy Lobo Tong s Wok Kathy Lobo becomes Module 12: Modeling Slowly Changing Dimensions 6of 19

293 Modeling Slowly Changing Dimensions 12.7 Type Two: Preserving History 12 Multiple records are stored Old record contains old value in a single column New record contains new value in a single column Common to using timestamps to differentiate the records All prior versions are saved Customer Sales Representative Effective Start Effective End Clifton Lunch Linda Rivero Club 427 Linda Rivero Old Alley-Cats Linda Rivero End date adjusted Royal Barbecue Kathy Lobo Tong s Wok Kathy Lobo New Alley-Cats Kathy Lobo New record added Module 12: Modeling Slowly Changing Dimensions 7of 19

294 Modeling Slowly Changing Dimensions 12.8 Type Three: Preserving a Version of History One record is stored with the old and new values Two columns store the attribute values Old value is moved from current to prior column Only one prior version is saved New value overwrites current column Customer Current Sales Representative Prior Sales Representative Clifton Lunch Club 427 Linda Rivero Linda Rivero NULL NULL becomes Alley-Cats Linda Rivero NULL Royal Barbecue Tong s Wok Kathy Lobo Kathy Lobo NULL Customer NULL Clifton Lunch Current Sales Representative Linda Rivero Prior Sales Representative NULL Club 427 Linda Rivero NULL Alley-Cats Kathy Lobo Linda Rivero Royal Barbecue Kathy Lobo NULL Tong s Wok Kathy Lobo NULL Module 12: Modeling Slowly Changing Dimensions 8of 19

295 Modeling Slowly Changing Dimensions 12.9 Alternative to Type Two (Preserving History) 12 Add a new record at the time of the attribute change and introduce a new key value New key of 4555 is introduced that maps to customer maps to Linda; 4555 maps to Kathy Customer Key Customer Name Alley-Cats Alley-Cats Customer Sales ID Representative 123 Linda Rivero 123 Kathy Lobo Sales are recorded using the correct key value and clearly map to Linda or Kathy Customer Key Period Key Product Key Dollars Sales for Linda Sales for Kathy Module 12: Modeling Slowly Changing Dimensions 9of 19

296 Modeling Slowly Changing Dimensions Considerations Which technique to use depends on your business requirements Type One does not store history Type Two stores all versions of history Type Three stores only one version of history Columns and processing must be added to your source system to track the changes and populate the version information Module 12: Modeling Slowly Changing Dimensions 10 of 19

297 Modeling Slowly Changing Dimensions ABC Example 12 Enhance ABC s model to incorporate a slowly changing customer dimension to account for changing sales representatives using the Type Two method Customer Sales Representative Effective Start Effective End Clifton Lunch Linda Rivero Club 427 Linda Rivero Alley-Cats Linda Rivero Royal Barbecue Kathy Lobo Tong s Wok Kathy Lobo Alley-Cats Kathy Lobo Module 12: Modeling Slowly Changing Dimensions 11 of 19

298 Modeling Slowly Changing Dimensions /4 Implementation Steps 1. Create the Physical layer 2. Create the Business Model and Mapping layer 3. Create the Presentation layer 4. Test the results Module 12: Modeling Slowly Changing Dimensions 12 of 19

299 Modeling Slowly Changing Dimensions /4 1. Create the Physical Layer 12 Import the source that has Type Two physical columns Create a physical join that includes effective dates Type Two date columns Includes clause to match to the correct customer record based on the order date Module 12: Modeling Slowly Changing Dimensions 13 of 19

300 Modeling Slowly Changing Dimensions /4 2. Create the Business Model and Mapping Layer Create the logical dimension table and corresponding dimensional hierarchy as usual Module 12: Modeling Slowly Changing Dimensions 14 of 19

301 Modeling Slowly Changing Dimensions /4 2. Create the Business Model and Mapping Layer Continued 12 Model the logical relationship No need to add effective dates clause Module 12: Modeling Slowly Changing Dimensions 15 of 19

302 Modeling Slowly Changing Dimensions /4 3. Create the Presentation Layer Expose data columns as usual No need to expose effective dates except for testing purposes Can coexist with non-scd customer dimension Module 12: Modeling Slowly Changing Dimensions 16 of 19

303 Modeling Slowly Changing Dimensions /4 4. Test the Results 12 Verify that facts are appearing as expected when attributes change Results without SCD Results using SCD: Sales are allocated between two sales reps assigned to the Alley-Cats customer Module 12: Modeling Slowly Changing Dimensions 17 of 19

304 Modeling Slowly Changing Dimensions Summary This module showed you how to: Describe techniques to address slowly changing dimensions Implement a solution to model slowly changing dimensions Module 12: Modeling Slowly Changing Dimensions 18 of 19

305 Modeling Slowly Changing Dimensions Lab 12 In the lab you will: Implement a slowly changing dimension Module 12: Modeling Slowly Changing Dimensions 19 of 19

306 Modeling Slowly Changing Dimensions 12.20

307 Modeling Extension Tables 13.1 Module 13: Modeling Extension Tables 13

308 Modeling Extension Tables 13.2 Module Objectives After completing this module you will be able to: Describe the purpose of extension tables Model extension tables Why you need to know: Understanding these techniques will help you model the less typical situations you may encounter Module 13: Modeling Extension Tables 2of 25

309 Modeling Extension Tables 13.3 Main Dimension Table Is the physical table that contains the main dimension information Shorthand is to use D to refer to it 13 Customer Dimension Table NewKey Name Dimension (D) 1003 New York Cafe Module 13: Modeling Extension Tables 3of 25

310 Modeling Extension Tables 13.4 Main Fact Table Is the physical table that contains the main fact information Shorthand is to use F to refer to it Sales Fact Table Fact (F) InvNbr Dollars 1000 Module 13: Modeling Extension Tables 4of 25

311 Modeling Extension Tables 13.5 Challenge: Main Tables May require hundreds of columns to hold the necessary attributes Database vendors may limit the number of columns or row size on a table 13 Module 13: Modeling Extension Tables 5of 25

312 Modeling Extension Tables 13.6 Solution: Extension Tables Extension tables are additional physical tables that work with main tables to hold additional attributes Are created in the physical database when the database administrator decides they are necessary From main dimension From main fact From extension fact Module 13: Modeling Extension Tables 6of 25

313 Modeling Extension Tables 13.7 Extension Table Relationships Extension tables have a zero-to-one relationship with their corresponding main table A row in the main table may or may not have a corresponding row in the extension, depending on the business requirements and design 13 Main Key points to main Extension NewKey Name CustKey PriceCategory 1003 New York Café Club Clifton Lunch No row for Club 427 in the Extension Module 13: Modeling Extension Tables 7of 25

314 Modeling Extension Tables 13.8 Dimension Extension Table Extends the attributes for the associated dimension Shorthand is to use DX to refer to it ABC will use an extension table to add Price Category in the Customer Dimension NewKey :0 Name New York Cafe Dimension (D) Dimension Extension (DX) CustKey Price Category Module 13: Modeling Extension Tables 8of 25

315 Modeling Extension Tables /5 Model a Dimension Extension Table 1. Create physical joins Create separate logical table source 3. Map logical columns 4. Create presentation 5. Test results Module 13: Modeling Extension Tables 9of 25

316 Modeling Extension Tables /5 1. Create Physical Joins Use foreign key joins from: The dimension extension to the main dimension The dimension extension to the main fact DX Physical joins with extension on the 1 side F D Module 13: Modeling Extension Tables 10 of 25

317 Modeling Extension Tables /5 2. Create Separate Logical Table Source Within the dimension, add a second logical table source that maps to the physical extension table The Siebel Analytics Server will join: Across multiple sources for a dimension Only to sources needed to satisfy the query 13 Customer dimension Source for main DX D Source for extension Module 13: Modeling Extension Tables 11 of 25

318 Modeling Extension Tables /5 3. Map Logical Columns Create logical dimension columns that map to the physical columns on the dimension table DX Additional dimension attribute Module 13: Modeling Extension Tables 12 of 25

319 Modeling Extension Tables /5 4. Create Presentation Add additional dimension column to presentation catalog 13 Additional dimension extension attribute Module 13: Modeling Extension Tables 13 of 25

320 Modeling Extension Tables /5 5. Test Results Verify that the results are as expected New dimension information Module 13: Modeling Extension Tables 14 of 25

321 Modeling Extension Tables Fact Extension Table Extends the attributes for the associated fact Shorthand is to use FX to refer to it 13 ABC will use an extension table to show Credits in the Sales Fact D1_Orders2 D1_Orders2X InvNbr :0 ROW_WID 1003 Fact (F) ROW_WID 1003 Fact Extension (FX) CMDolrs 1 Credits Module 13: Modeling Extension Tables 15 of 25

322 Modeling Extension Tables /5 Model a Fact Extension Table 1. Create physical join 2. Add table to existing source 3. Map columns to extension 4. Create presentation 5. Test results Module 13: Modeling Extension Tables 16 of 25

323 Modeling Extension Tables /5 1. Create Physical Join Use a foreign key join from fact extension to main fact 13 FX Physical join with extension on 1 side F Module 13: Modeling Extension Tables 17 of 25

324 Modeling Extension Tables /5 2. Add Table to Existing Source Add the extension table columns to the existing source The Siebel Analytics Server will: Pick the best single source to satisfy the query Not join across separate sources for a fact Sales fact D DX Module 13: Modeling Extension Tables 18 of 25

325 Modeling Extension Tables /5 3. Map Columns to Extension Map new columns to extension table Columns map to main (F) and extension (FX) 13 Module 13: Modeling Extension Tables 19 of 25

326 Modeling Extension Tables /5 4. Create Presentation Add the additional fact column to presentation catalog New fact column Module 13: Modeling Extension Tables 20 of 25

327 Modeling Extension Tables /5 5. Test Results Verify that the results are as expected 13 New fact information Module 13: Modeling Extension Tables 21 of 25

328 Modeling Extension Tables Considerations When no data from the extension is needed, the generated SQL should not join to the extension for performance reasons Data from main only Data from main and extension Query F not FX Query F and FX Module 13: Modeling Extension Tables 22 of 25

329 Modeling Extension Tables Considerations Continued How you model will determine the SQL generated, and hence the performance Determine unnecessary joins using NQQuery.log Using the described techniques yields best performance 13 Data from main only Data from main and extension Module 13: Modeling Extension Tables 23 of 25

330 Modeling Extension Tables Summary This module showed you how to: Describe the purpose of extension tables Model extension tables Module 13: Modeling Extension Tables 24 of 25

331 Modeling Extension Tables Lab In the lab you will: Implement extension tables for dimensions and facts 13 Module 13: Modeling Extension Tables 25 of 25

332 Modeling Extension Tables 13.26

333 Analytics Security 14.1 Module 14: Analytics Security 14

334 Analytics Security 14.2 Module Objectives After completing this module you will be able to: Define authentication and authorization Describe the different levels at which security is enforced Configure security using Security Manager Configure query-governing controls Why you need to know: You need to set up security so the Siebel Analytics Server can authenticate users and assign appropriate permissions Module 14: Analytics Security 2of 30

335 Analytics Security 14.3 Business Challenge Only qualified persons should have access rights to dataanalysis applications Data needs to be protected so that only authorized employees can access sensitive information Employees should automatically see the information that is relevant to their roles 14 Module 14: Analytics Security 3of 30

336 Analytics Security 14.4 Business Solution: Siebel Analytics Security Provides ability to authenticate users through login Controls user access to data Secures access control on object and data levels The application authenticates CCHENG The application authorizes CCHENG to view only certain folders Module 14: Analytics Security 4of 30

337 Analytics Security 14.5 Authentication Process by which an application verifies that a user has the right to log in and access data Verification through user name and password Supports many methods of authenticating users, including: Lightweight Directory Access Protocol (LDAP) Siebel Analytics Repository authentication Default method used for authentication Microsoft Active Directory (ADSI) External Database authentication 14 For information about authentication options, see the Authentication Options section of the Siebel Analytics Server Administration Guide. Module 14: Analytics Security 5of 30

338 Analytics Security 14.6 Example: External Database Authentication Maintain authentication information in an external database instead of analytics repository Authentication occurs when SQL queries the external database and locates a match to the user name and password submitted Module 14: Analytics Security 6of 30

339 Analytics Security 14.7 Connecting to the External Database Authentication occurs when the external database returns a value for the USER system variable Initialization block is used to query against the database requesting authentication information on the USER 14 SQL that queries the database for authentication External Database Module 14: Analytics Security 7of 30

340 Analytics Security 14.8 Authorization Process by which an application verifies what a user or group is authorized to: View, referred to as permissions Defined for server and Web objects Perform, referred to as privileges Defined for Web objects only Permissions example Privileges example User/Group Permissions User/Group Privileges Granted User 1 Read User 1 Manage Privileges User 2 Denied User 2 Create Folders Group 1 User 3 Full Control Denied Web Group 1 User 3 Create ibots, Publish ibots Create Folders Group 2 Read Web Group 2 Create Views Module 14: Analytics Security 8of 30

341 Analytics Security /2 Siebel Analytics Security Levels Authentication and authorization are enforced on two levels: Object-level security Data-level security 14 Module 14: Analytics Security 9of 30

342 Analytics Security /2 Object-Level Security Controls access to Analytics repository objects (metadata), such as subject areas, tables, and columns Configured in the Siebel Analytics Administration Tool Controls access to Analytics Web catalog objects, such as dashboards, folders, filters, views, and reports Configured in Analytics Web applications User 1 session User 2 session User 1 is authorized to view additional folders compared to User 2 Module 14: Analytics Security 10 of 30

343 Analytics Security /2 Data-Level Security Controls access to content that appears in end-user objects, such as dashboard reports Configured in the Analytics Administration Tool Example: Monthly sales report viewed by two different users Columns for the reports are the same, but the data is different 14 Regional manager has access to all district managers and their sales reps District manager only has access to his sales reps Region District Sales Rep. Month Mark May Stacy June May Ryan June Sue Jon Mike May June Tim May June Beth Jen May June May Frank June Sales 13K 12K 6K 9K 17K 9K 13K 15K 7K 11K 13K 11K Region District Sales Rep. Month Mike May Sue Jon June Tim May June Sales 17K 9K 13K 15K Module 14: Analytics Security 11 of 30

344 Analytics Security Security Manager Provides options for defining users and repository groups Groups allow membership to users and other groups Simplifies administration of large numbers of users Provides a set of security attributes Users Groups Module 14: Analytics Security 12 of 30

345 Analytics Security /3 Implement Object-Level Security For repository objects: 1. Create a new user 2. Create a repository group 3. Define permissions 14 Module 14: Analytics Security 13 of 30

346 Analytics Security /3 1. Create a New User Use Security Manager, in the Administration Tool, to create a user in the repository Select Manage > Security Select Users Right-click white space, select New User Module 14: Analytics Security 14 of 30

347 Analytics Security /3 2. Creating a Repository Group Is similar to creating users Define group name 14 Right-click white space, select New Security Group Click to add users and groups to the new group Module 14: Analytics Security 15 of 30

348 Analytics Security /3 3. Define Permissions Set permissions from the Presentation Table properties dialog box Example: Restrict Customers, Periods, and Products access to User 2 Double-click object Click Permissions Default setting for all objects is Everyone Click box to deny User 2 access Module 14: Analytics Security 16 of 30

349 Analytics Security /4 Implement Object-Level Security For Web catalog objects: 1. Create a Web catalog group 2. Assign users 3. Define permissions Assign privileges Module 14: Analytics Security 17 of 30

350 Analytics Security /4 1. Create a Web Catalog Group From Siebel Answers, select Admin > Manage Web Groups and Users to create groups Name of Web Group should not match any name of a user Click the link to access Group definition page Assign either existing dashboard or create new one User or group with rights to modify dashboard Module 14: Analytics Security 18 of 30

351 Analytics Security /4 2. Assign Users Select the Web group, in the Manage Web Groups and Users site, to assign users to a Web group Users are created in the repository Displays a list of users and groups you can assign to the Web group 14 Click Add to assign user to the group Module 14: Analytics Security 19 of 30

352 Analytics Security /4 3. Define Permissions Click the Manage Intelligence Dashboards link to define permissions Locate Web group you wish to define permissions for Permissions: Read, Change/Delete, Full Control, No Access, Traverse Folder Click the Permissions icon Default settings Click the link to toggle to appropriate permission Module 14: Analytics Security 20 of 30

353 Analytics Security /4 4. Assign Privileges Click the Manage Privileges link to assign privileges Locate the privileges you wish to assign to users or Web groups Privileges: Read or No Access 14 Notice that privileges are denied to the Everyone group and granted to the Example group Select user or group name to access the Privileges page Click to give Read or No Access privileges Module 14: Analytics Security 21 of 30

354 Analytics Security /4 Considerations Privileges Bypassing Siebel Analytics security Query limits Disable query functionality Module 14: Analytics Security 22 of 30

355 Analytics Security /4 Privileges Can be: Granted to users and groups explicitly This has precedence over privileges granted through groups Granted or denied to users through memberships in groups A user that is a direct member of two or more groups, with conflicting privileges, is granted the least restrictive privileges of the groups 14 Module 14: Analytics Security 23 of 30

356 Analytics Security /4 Bypassing Siebel Analytics Security In the security section of the NQSConfig.ini file, you have the option to bypass security Option 3 Notice that the bypass authentication parameter is commented out by default Remove pound sign, save file, and restart the Siebel Analytics Server to implement change Module 14: Analytics Security 24 of 30 Bypass Security Option 3 allows users to be authenticated against the database to which their queries are sent, using the submitted user name and password. For example, if a user runs a query tool against the Siebel Analytics Server with username = TEST and password = TEST, this username and password are used to connect to the underlying database server. If this represents a valid user to the underlying database server, the user is authenticated.

357 Analytics Security /4 Query Limits Multiple queries running simultaneously can hinder performance May consume too many resources Affects performance Administrators can set a limit on the time it takes the system to execute a query If a query exceeds the limit, it will terminate and free up resources 14 Module 14: Analytics Security 25 of 30

358 Analytics Security /4 Setting Query Limits Set query limits from Security Manager Limits can be set at either the user or group level Selecting an object in the left pane will list its associated groups and users in the right pane Double-click the object to navigate to the Group dialog box Click Permissions to define security for all members of the group Module 14: Analytics Security 26 of 30

359 Analytics Security /4 Setting Query Limits Continued Set limit for a query based on number of rows or time Enable: Enforces restriction and cancels the query Warn: Logs message in NQServer.log and NQQuery.log if row limit is reached Ignore: Limits will be inherited from the parent groups If there is no row limit to inherit, does not enforce limit 14 Click ellipsis to define a disable query functionality period Specify maximum value in field Module 14: Analytics Security 27 of 30

360 Analytics Security /4 Disable Query Functionality Prohibits users from being able to query Allows for other production tasks to be performed, such as batch reporting and table updating, without hindering performance Identify the day and time for restriction and click in the box Click to allow/disallow highlighted restriction period Module 14: Analytics Security 28 of 30

361 Analytics Security Summary This module showed you how to: Define authentication and authorization Describe the different levels at which security is enforced Configure security using Security Manager Configure query-governing controls 14 Module 14: Analytics Security 29 of 30

362 Analytics Security Lab In the lab you will: Set up a new user using Security Manager Create new groups and add users Set up external database authentication Use Security Manager to disallow queries that consume too many resources Module 14: Analytics Security 30 of 30

363 Cache Management 15.1 Module 15: Cache Management 15

364 Cache Management 15.2 Module Objectives After completing this module you will be able to: Manage cache files Why you need to know: Cache management techniques are useful for maintaining and enhancing query performance Module 15: Cache Management 2of 19

365 Cache Management 15.3 Caching Query Results Query execution sometimes requires large amounts of database processing Siebel Analytics Server saves query results in cache files A query is evaluated to determine if it qualifies for a cache hit Fulfilling a user s query from existing results stored in the cache instead of being satisfied by the database Cache files are stored in location as defined in the NQSConfig.INI file 15 Module 15: Cache Management 3of 19

366 Cache Management 15.4 Caching Advantages Eliminates redundant queries run on the database Query performance Using cached results eliminates having to access database Frees up database server to perform other tasks Faster response time when fulfilling a query from the cache versus searching through the database Network Conserves network resources by avoiding connection to the database server Module 15: Cache Management 4of 19

367 Cache Management 15.5 Query Cache Location where query results are stored Each cache file stores metadata and results of the request Cache metadata is evaluated to determine if new query can: Use stored results, referred to as a cache hit, or Use database for new results User s query request is translated into logical request Logical Request The metadata is searched to identify a match (cache hit) Siebel Analytics Server Cache Metadata (Cache hit?) Yes No Server Database If cache miss, the request is queried against the database; results are stored in cache and sent to user 15 If there is a match, results are retrieved from the cache and sent to the user Query Cache Return Result Module 15: Cache Management 5of 19

368 Cache Management 15.6 Configuring Query Cache Enable and configure cache storage in NQSConfig.INI file Specify directory for query cache storage Should be local, high-performance, high-reliability storage device Specifies directory for query cache storage Set enable parameter to YES File stores metadata about queries stored in cache Module 15: Cache Management 6of 19

369 Cache Management 15.7 Cache Files Always produce the same results, even after the database has been updated Issues with retaining cache files may arise Not purging outdated caches, known as stale caches, can potentially return inaccurate results over time Sizable consumption of disk space Can be managed by defining the maximum rows per cache entry and maximum number of cache entries in NQSConfig.INI 15 Module 15: Cache Management 7of 19

370 Cache Management /8 Cache Management Techniques Configuring cache parameters Restricting tables as non-cacheable Using Cache Manager Modifying column display Inspecting cache report Purging cache entries Inspecting query SQL Using event polling tables Module 15: Cache Management 8of 19

371 Cache Management /8 Configuring Cache Parameters In the NSQConfig file, specify the query volume, size of the query results, and disk space allocated for caching When disk space has reached its limit, entries least recently used (LRU) are discarded to make space for new entries 15 Specify limits allowed in the cache Module 15: Cache Management 9of 19

372 Cache Management /8 Restricting Tables as Non-Cacheable By default, all tables in the database are cacheable Cache entry is created if the table has been queried against Supplier database tables Remove check so that any query made against this table is not cached Time until a cache entry is purged from file Module 15: Cache Management 10 of 19

373 Cache Management /8 Using Cache Manager Monitors the entire query cache Administrators can view cache entries by category (for example, repository, subject area, and user) Allows administrators to manually purge entries by user or by physical table Active in Online mode only Parent object displays entire list Cache queries 15 Selecting child object displays only the queries associated to it Module 15: Cache Management 11 of 19

374 Cache Management /8 Modifying Column Display Administrators can modify columns displayed in the Cache Manager Uncheck a column to remove it from display Promote or demote the order in which a column displays Select Edit > Options Click Up to promote or Down to demote selected column Notice that the Business Model column has been promoted Module 15: Cache Management 12 of 19

375 Cache Management /8 Inspecting Cache Report Provides administrators with performance information about the system Select Action > Show Info from the Cache Manager window Monitor both space used and available, delete cached queries when necessary 15 Queries not satisfied may affect performance Module 15: Cache Management 13 of 19

376 Cache Management /8 Purging Cache Entries Allows administrators to manually purge selected entries or the entire query cache Use the CTRL key to specify certain cache entries Select Edit > Select All to select entire cache Module 15: Cache Management 14 of 19

377 Cache Management /8 Inspecting Query SQL Is useful in development or troubleshooting a potential issue Useful in evaluating cache statistics 15 To view the Query SQL: 1. Select the query 2. Select SQL > Show Module 15: Cache Management 15 of 19

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