Course MS6234A
Implementing and Maintaining
Microsoft SQL Server2008 Analysis Services
Microsoft Official Learning Material
Duration
3 days
(Instructor-led)
Introduction
Elements of this syllabus are subject to change.
This
three-day instructor-led course teaches students how to implement an Analysis
Services solution in an organization. The course discusses how to use the
Analysis Services development tools to create an Analysis Services database and
an OLAP cube, and how to use the Analysis Services management and
administrative tools to manage an Analysis Services solution.
Audience
The primary
audience for this course is individuals who design and maintain business
intelligence solutions for their organization. These individuals work in
environments where databases play a key role in their primary job and may
perform database administration and maintenance as part of their primary job
responsibilities.
The secondary
audience for this course is individuals who develop applications that deliver
content from SQL Server Analysis Services to the organization.
At Course Completion
After completing this course, students will be able to:
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Describe how SQL Server Analysis Services can be used to implement
analytical solutions. |
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Create multidimensional analysis solutions with SQL Server Analysis
Services. |
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Implement dimensions and cubes in an Analysis Services solution. |
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Implement measures and measure groups in an Analysis Services
solution. |
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Query a multidimensional Analysis Services solution. |
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Customize an Analysis Services cube. |
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Deploy and secure an Analysis Services database. |
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Maintain a multidimensional Analysis Services solution. |
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Implement a Data Mining solution. |
Prerequisites
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Before attending this course, students must have:
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Course
Outline
Module 1: Introduction to Microsoft SQL
Server Analysis Services
This module
introduces common analysis scenarios and describes how Analysis Services
provides a powerful platform for multidimensional OLAP solutions and data
mining solutions. The module then describes the main considerations for
installing Analysis Services.
Lessons
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Lesson
1: Overview of Data Analysis Solutions |
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Lesson
2: Overview of SQL Server Analysis Services |
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Lesson
3: Installing SQL Server Analysis Services |
Lab: Using SQL
Server Analysis Services
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Exercise
1: (Level 200) Installing SQL Server Analysis Services |
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Exercise
2: (Level 200) Verifying Installation |
After completing
this module, students will be able to:
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Describe
data analysis solutions. |
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Describe
the key features of SQL Server Analysis Services. |
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Install
SQL Server Analysis Services. |
Module 2: Creating Multidimensional
Analysis Solutions
This module
introduces the development tools you can use to create an Analysis Services
multidimensional analysis solution, and describes how to create data sources,
data source views, and cubes.
Lessons
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Lesson
1: Developing Analysis Services Solutions |
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Lesson
2: Creating Data Sources and Data Source Views |
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Lesson
3: Creating a Cube |
Lab: Creating
Multidimensional Analysis Solutions
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Exercise
1: (Level 200) Creating a Data Source |
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Exercise
2: (Level 200) Creating and Modifying a Data Source View |
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Exercise
3: (Level 200) Creating and Modifying a Cube |
After completing
this module, students will be able to:
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Develop
Analysis Services solutions. |
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Create
a data source and a data source view. |
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Create
a cube. |
Module 3: Working with Cubes and
Dimensions
This module
describes how to edit dimensions and to configure dimensions, attributes, and
hierarchies.
Lessons
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Lesson
1: Configuring Dimensions |
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Lesson
2: Defining Attribute Hierarchies |
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Lesson
3: Sorting and Grouping Attributes |
Lab: Working with
Cubes and Dimensions
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Exercise
1: (Level 200) Configuring Dimensions |
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Exercise
2: (Level 200) Defining Relationships and Hierarchies |
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Exercise
3: (Level 200) Sorting and Grouping Dimension Attributes |
After completing
this module, students will be able to:
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Configure
dimensions. |
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Define
hierarchies. |
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Sort
and group attributes. |
Module 4: Working with Measures and
Measure Groups
This module
explains how to edit and configure measures and measure groups.
Lessons
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Lesson
1: Working With Measures |
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Lesson
2: Working with Measure Groups |
Lab: Working with
Measures and Measure Groups
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Exercise
1: (Level 200) Configuring Measures |
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Exercise
2: (Level 200) Defining Dimension Usage and Relationships |
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Exercise
3: (Level 200) Configuring Measure Group Storage |
After completing
this module, students will be able to:
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Work
with measures. |
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Work
with measure groups. |
Module 5: Querying Multidimensional
Analysis Solutions
This module
introduces multidimensional expressions (MDX) and describes how to implement
calculated members and named sets in an Analysis Services cube.
Lessons
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Lesson
1: MDX Fundamentals |
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Lesson
2: Adding Calculations to a Cube |
Lab: Querying
Multidimensional Analysis Solutions
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Exercise
1: (Level 200) Querying a Cube by Using MDX |
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Exercise
2: (Level 200) Creating a Calculated Member |
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Exercise
3: (Level 200) Defining a Named Set |
After completing
this module, students will be able to:
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Describe
Multidimensional Expression (MDX) fundamentals. |
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Add
calculations to a cube. |
Module 6: Customizing Cube
Functionality
This module
explains how to customize a cube by implementing key performance indicators
(KPIs), actions, perspectives, and translations.
Lessons
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Lesson
1: Implementing Key Performance Indicators |
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Lesson
2: Implementing Actions |
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Lesson
3: Implementing Perspectives |
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Lesson
4: Implementing Translations |
Lab: Customizing
Cube Functionality
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Exercise
1: (Level 200) Implementing a KPI |
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Exercise
2: (Level 200) Implementing an Action |
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Exercise
3: (Level 200) Implementing a Perspective |
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Exercise
4: (Level 200) Implementing a Translation |
After completing
this module, students will be able to:
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Implement
Key Performance Indicators (KPIs). |
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Implement
actions. |
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Implement
perspectives. |
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Implement
translations. |
Module 7: Deploying and Securing an
Analysis Services Database
This module
describes how to deploy an Analysis Services database to a production server,
and how to implement security in an Analysis Services multidimensional
solution.
Lessons
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Lesson
1: Deploying an Analysis Services Database |
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Lesson
2: Securing an Analysis Services Database |
Lab: Deploying and
Securing an Analysis Services Database
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Exercise
1: (Level 200) Deploying an Analysis Services Database |
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Exercise
2: (Level 200) Securing an Analysis Services Database |
After completing
this module, students will be able to:
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Deploy
an Analysis Services database. |
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Secure
an Analysis Services database. |
Module 8: Maintaining a
Multidimensional Solution
This module
discusses the maintenance tasks associated with an Analysis Services solution,
and describes how administrators can use the Analysis Services management tools
to perform them.
Lessons
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Lesson
1: Configuring Processing |
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Lesson
2: Logging, Monitoring, and Optimizing an Analysis Services Solution |
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Lesson
3: Backing Up and Restoring an Analysis Services Database |
Lab: Maintaining a
Multidimensional Solution
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Exercise
1: (Level 200) Configuring Processing |
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Exercise
2: (Level 200) Implementing Logging and Monitoring |
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Exercise
3: (Level 200) Backing Up and Restoring an Analysis Services Database |
After completing
this module, students will be able to:
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Configure
processing settings. |
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Log,
monitor, and optimize an Analysis Services solution. |
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Back
up and restore an Analysis Services database. |
Module 9: Introduction to Data Mining
This module
introduces data mining, and describes how to implement data mining structures
and models. It then explains how to validate data model accuracy.
Lessons
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Lesson
1: Overview of Data Mining |
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Lesson
2: Creating a Data Mining Solution |
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Lesson
3: Validating Data Mining Models |
Lab: Introduction
to Data Mining
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Exercise
1: (Level 200) Creating a Data Mining Structure |
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Exercise
2: (Level 200) Adding a Data Mining Model |
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Exercise
3: (Level 200) Exploring Data Mining Models |
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Exercise
4: (Level 200) Validating Data Mining Models |
After completing
this module, students will be able to:
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Describe
data mining. |
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Create
a data mining solution. |
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Validate
data mining models. |