Designing and Implementing OLAP Solutions with Microsoft SQL Server 2000
(Microsoft Training Course: 2074) - 5 days - £2640 exc VAT

This course provides students with the knowledge and skills necessary to design, implement, and deploy OLAP solutions by using Analysis Services

1. Introduction to OLAP and Data Warehousing
2. Designing Multidimensional Data Marts
3. Previewing OLAP Using Analysis Services
4. Understanding Analysis Services Architecture
5. Setting Up Dimensions
6. Advanced Dimension Settings
7. Advanced Data Mart Design Techniques
8. Cubes and Measures
9. Creating the Sales Reporting Cube
10. Virtual Cubes
11. Storage Optimization
12. Processing Dimensions and Cubes
13. Creating Partitions
14. Implementing Calculations Using MDX
15. Using Excel as an OLAP Client
16. Introduction to Data Mining
17. Analyzing Data with Actions, Drill-Through, and Write-Back
18. Implementing OLAP Security
19. Deploying an OLAP Application
20. Creating the Warehouse Database

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Supplementary Information OLAP Solutions are integral to Microsoft SQL Server Business Intelligence Tools. Analysis Services was introduced by Microsoft in 2000 and resulted in significant performance gains when extracting information from data warehouses using multi-dimensional analytical queries. For introduction to OLAP techniques and Microsoft Analysis Services we recommend courses 2791 or 6234.

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Module 1: Introduction to OLAP and Data Warehousing

• Why Data Warehousing
• Data Marts and Data Warehouses
• Intorduction to OLAP
• Understanding Multidimensionality
• The Microsoft Data Warehouse Solution

Skills

• Understand OLAP (online analytical processing) and data warehousing concepts and applications.
  
• Describe characteristics, goals, and applications of a data warehouse.
  
• Explain the relationship between data marts and data warehouses.
  
• Describe reasons for implementing relational and/or multidimensional data marts to meet decision support needs.
  
• Describe tools to manage data warehouse implementations.
  
• Describe components of OLAP databases.
  

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Module 2: Designing Multidimensional Data Marts

• Designing a Data Warehouse Strategy
• Introducing the Data Warehouse
• The Relational Schema Behind the OLAP Database
• OLAP and Relational Dimensions
• Cubes and Fact Tables

Skills

• Design multidimensional data marts by using star and snowflake schemas.
  
• Describe a process for designing data warehouse systems.
  
• Understand how relational dimensions and fact tables relate to OLAP dimensions and cubes.
  
• Determine OLAP dimension elements.
  
• Determine OLAP cube elements.
  

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Module 3: Previewing OLAP Using Analysis Services

• Analysis Server Basics
• Using OLAP Manager
• Understanding the Star Schema Source
• Creating the Sales Cube
• Building the Sales Cube
• Building the Dimensions
• Finalizing the Cube
• Designing Storage and Processing
• Viewing the Results

Skills

• Verify that Analysis Server is started.
  
• Create an ODBC data source for the database.
  
• Start Analysis Manager.
  
• Understand the underlying star schema source.
  
• Create a database by using Analysis Manager.
  
• Build dimensions by using the Dimension Wizard.
  
• Design a cube by using the Cube Wizard.
  
• Design storage and process a cube using the Storage Design Wizard.
  
• Browse the cube results.
  

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Module 4: Understanding Analysis Services Architecture

• Microsoft Data Warehousing Overview
• Analysis Services Architecture
• Storage Modes
• Partitioning
• Dimension Alternatives
• Large Dimension Support
• Caching and Write-Back
• How Databases Are Organized
• Other Server Side Elements
• Client Architecture
• Office 2000 OLAP Components
• Data Mining

Skills

• Understand the Analysis Server architecture.
  
• Understand the metadata repository.
  
• Know the difference between MOLAP, ROLAP, and HOLAP storage modes.
  
• Understand how Analysis Manager interfaces with the server by using DSO.
  
• Appreciate the benefits of partitioning.
  
• Understand how cubes and databases are organized.
  
• Understand client architecture and the role of PivotTable Services.
  
• Recognize Microsoft Office 2000 OLAP capabilities.
  

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Module 5: Setting Up Dimensions

• Understanding Dimension Basics
• Private Versus Shared Dimensions
• Working with Star Schema Dimensions
• Working with Snowflake Dimensions
• Working with Time Dimensions
• Working with Parent-Child Dimensions
• Creating Time Dimensions

Skills

• Understand when to use shared and private dimensions.
  
• Open and work with the dimension editor.
  
• Add levels to dimensions.
  
• Create dimensions from star and snowflake schemas.
  
• Define member properties at dimension levels.
  
• Implement time hierarchies and dimensions.
  
• Organize levels within dimensions for drill up and drill down.
  
• Develop parent-child dimensions.
  

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Module 6: Advanced Dimension Settings

• Creating Custom Hierarchies
• Nuances of Levels
• Hierarchies and Dimensions
• Understanding Virtual Dimensions
• Creating Cube with Financial Accounts
• Creating Cube with Large Dimensions
• Creating Cube with Forecasting Data
• Validating and Optimizing the Cube Structure

Skills

• Use the Dimension Editor and Dimension Wizard to build and fine-tune dimensions.
  
• Make use of various dimension properties.
  
• Work with dimension levels and hierarchies.
  
• Create virtual dimensions from member properties.
  
• Create custom member and rollup formulas.
  
• Manage very large, flat dimensions.
  
• Disable levels of a shared dimension.
  

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Module 7: Advanced Data Mart Design Techniques

• Sharing Dimensions Among Cubes With Different Granularity
• Handling Nulls In the Source Data
• Managing Slowly Changing Dimensions
• Implementing Summary Fact Tables
• Managing Various Dimension Scenarios
• Optimization Tuning

Skills

• Apply advanced OLAP dimension and cube design techniques.
  
• Share dimensions across cubes with different granularity using relational and multidimensional design techniques.
  
• Handle nulls in the source data using relational and multidimensional design techniques.
  
• Manage slowly changing dimensions using relational and multidimensional design techniques.
  
• Implement summary fact tables.
  

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Module 8: Cubes and Measures

• Understanding Cube Basics
• Working with Cubes
• Working with Measures
• Defining Measure Properties
• Creating Calculations
• Defining Dimension Properties

Skills

• Create cubes by using the Cube Editor.
  
• Add and delete measures from a cube.
  
• Add and delete dimensions from a cube.
  
• Set up a measure by using each of the five aggregation functions.
  
• Format measures.
  
• Define an internal measure.
  
• Create simple calculated members.
  
• Administer dimension properties within the Cube Editor.
  

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Module 9: Creating the Sales Reporting Cube

• Building the Sales Reporting Cube
• Modifying the Sales Reporting Cube

Skills

• Create a cube based upon end-user requirements.
  
• Build dimensions given the dimension tables and expected levels.
  
• Use various dimension types.
  
• Use expressions to create dimension member names.
  
• Create measures.
  
• Build simple calculated members.
  
• Design aggregations and process the cube.
  
• Verify cube results by using the Cube Browser.
  

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Module 10: Virtual Cubes

• Understanding Virtual Cubes
• Obtaining Logical Results
• Building a Virtual Cube
• Creating Calculated Members

Skills

• Understand when to use virtual cubes and know their benefits.
  
• Understand the limitations of using virtual cubes.
  
• Know the rules for constructing meaningful virtual cubes.
  
• Build virtual cubes by using the Virtual Cube Wizard.
  
• Define calculated members in virtual cubes by using the Calculated Member Builder.
  

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Module 11: Storage Optimization

• Analysis Server Storage
• Analysis Server Aggregations
• The Storage Design Wizard
• Aggregation Details
• Usage-Based Optimization
• Optimization Tuning

Skills

• Explain the pros and cons of the three data storage modes.
  
• Describe how aggregations work.
  
• Use the Storage Design Wizard to set storage design.
  
• Design aggregations for cubes.
  
• Describe the contents of a single aggregation.
  
• Describe the concepts and mechanics of usage-based optimization.
  
• Override aggregation settings per dimension.
  

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Module 12: Processing Dimensions and Cubes

• Overview of Schema and Data
• Processing Dimensions
• Rebuilding Dimensions
• Incrementally Updating a Dimension
• Processing Cubes
• The Full Process
• Refreshing a Cube
• Incrementally Updating a Cube
• Troubleshooting Cube Problems
• Optimizing Cube Processing

Skills

• Rebuild shared dimensions.
  
• Handle new and deleted members.
  
• Understand the difference between rebuilding and incrementally updating dimensions.
  
• Process a cube using the three methods.
  
• Explain the implications of the three cube processing types.
  
• Perform an incremental data load using a database filter.
  
• See how changes are reflected in OLAP cubes after changing data within the source RDBMS.
  

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Module 13: Creating Partitions

• Partitioning Overview
• Creating Partitions
• Fact Table Considerations
• Working with Partitions
• Merging Partitions

Skills

• Explain the benefits of partitioning.
  
• Describe the pros and cons of portioning source fact tables.
  
• Describe the mechanics of the Partition Wizard.
  
• Explain when to define slices and when to define filters.
  
• Describe the purpose and mechanics of merging partitions.
  

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Module 14: Implementing Calculations Using MDX

• Understanding Calculated Members
• Defining Calculated Members
• Members, Tuples, and Sets
• Calculated Members in Non-Measure Dimensions
• Using Functions Within Calculated Members
• Understanding Solve Order

Skills

• Describe how calculated members work.
  
• Describe the impact of calculated members on cube size and performance.
  
• Explain the mechanics of the Calculated Member Builder.
  
• Build simple calculated members.
  
• Understand the importance of calculation solve order.
  

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Module 15: Using Excel as an OLAP Client

• Overview of Office 2000 OLAP
• Creating an Excel PivotTable
• Fine Tuning PivotTables
• Working with PivotCharts
• Working with Local Cubes
• Creating OLAP Enabled Web Pages

Skills

• Create a PivotTable from an OLAP cube.
  
• Interact with a PivotTable through pivots, drill-downs, and filters.
  
• Perform PivotTable formatting.
  
• Create PivotCharts.
  
• Create local cube files.
  
• Create Web pages containing Pivot web components.
  

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Module 16: Introduction to Data Mining

• Understanding Data Mining
• Creating A Decision Tree Model Using OLAP Data
• Creating a Decision Tree Model Using Relational Data
• Editing an Existing Model
• Creating a Clustering Model Using OLAP Data
• Creating a Clustering Model Using Relational Data

Skills

• Define data mining.
  
• Understand how data mining fits within OLAP and the Microsoft data warehousing framework.
  
• Describe the decision tree and clustering algorithms.
  
• Use data mining to discover data patterns.
  
• Segment data by using data mining.
  
• Create a data mining model using the decision tree algorithm.
  
• Edit an existing model.
  
• Explore the decision tree and look for predictable indicators in the results.
  

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Module 17: Analyzing Data with Actions, Drill-Through, and Write-Back

• Understanding Actions
• Creating Actions
• Drill-Through Fundamentals
• Enabling Drill-Through
• Cube Write-Back

Skills

• Create and manage actions.
  
• Invoke an action that was already created.
  
• Enable cube drill-through.
  
• Understand the mechanics of cube drill-through.
  
• Set up a cube for write-back.
  

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Module 18: Implementing OLAP Security

• Analysis Services Security Overview
• Using Windows 2000 Security
• Managing Roles
• Using Virtual Cubes for Security
• Defining Dimension Security
• Administering Cell Level Security

Skills

• Understand how Analysis Services security is linked to Windows 2000 security.
  
• Add a security role to a database via the Analysis Manager.
  
• Assign roles to a cube.
  
• Implement dimension security.
  
• Develop cell-level security by using simple MDX.
  

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Module 19: Deploying an OLAP Application

• DTS Overview
• Executing and Scheduling Packages
• Analysis Services Processing Task
• Database Migration and Disaster Recovery

Skills

• Describe the role of Data Transformation Services (DTS) within OLAP applications.
  
• Create a DTS Package.
  
• Define an Analysis Services processing task.
  
• Schedule the processing of an OLAP dimension or cube.
  
• Move from testing to production environments.
  
• Perform disaster recovery on OLAP databases.
  

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Module 20: Creating the Warehouse Database

• Building the Warehouse Cube
• Building the Sales Cube
• Building the Warehouse and Sales Virtual Cube
• Deploying the Warehouse and Sales Cubes

Skills

• Create cubes and virtual cubes based upon end-user requirements.
  
• Build dimensions given the dimension tables and expected levels.
  
• Create partitions by using different fact tables.
  
• Use various dimension types.
  
• Build calculated members.
  

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Before attending this course, students must have the following pre-requisites:

• Basic understanding of database design, administration, and implementation concepts.
• Satisfactory level of comfort within the Microsoft Windows 2000 environment

At the end of the course, students will be able to define the term OLAP and its role within data warehousing, design multidimensional data marts by using star and snowflake schemas, recognize the fundamental components of a cube, understand the architecture of Analysis Services, create dimensions from relational dimension tables, understand the many types of dimensions, utilize various dimension properties and settings, design OLAP dimensions based upon underlying source data, create cubes by using the Cube Wizard and Cube Editor, create and manipulate measures, develop and understand virtual cubes, design cube storage and aggregations, update dimensions and cubes when source data changes, optimize the processing of dimensions and cubes, create partitions within cubes, implement simple calculations by using MDX and calculated members, use Microsoft Excel 2000 as an OLAP front-end application, understand how data mining fits within OLAP and the Microsoft data warehousing framework, employ actions, drill-through, and write-back for data analysis, design and implement cube and dimension security, automate the processing of dimensions and cubes through Data Transformation Services (DTS), and create cubes and virtual cubes based upon end-user requirements

OLAP Solutions are integral to Microsoft SQL Server Business Intelligence Tools. Analysis Services was introduced by Microsoft in 2000 and resulted in significant performance gains when extracting information from data warehouses using multi-dimensional analytical queries. For introduction to OLAP techniques and Microsoft Analysis Services we recommend courses 2791 or 6234.