Introduction
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:
| • |
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.
|
| • |
Implement dimensions and cubes in
an Analysis Services solution.
|
| • |
Implement measures and measure
groups in an Analysis Services solution.
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| • |
Query a multidimensional Analysis
Services solution.
|
| • |
Customize an Analysis Services
cube.
|
| • |
Deploy and secure an Analysis
Services database.
|
| • |
Maintain a multidimensional
Analysis Services solution.
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| • |
Implement a Data Mining solution.
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Prerequisites
Before attending this course, students
must have:
| • |
Conceptual understanding of OLAP
solutions.
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| • |
Experience navigating the
Microsoft Windows Server environment.
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| • |
Experience with Windows services
(starting and stopping).
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| • |
Experience creating service
accounts and permissions.
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| • |
Experience with Microsoft SQL
Server, including:
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| • |
SQL Server Agent.
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| • |
SQL Server query language (SELECT,
UPDATE, INSERT, and DELETE).
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| • |
SQL Server System tables.
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| • |
SQL Server accounts (users and
permissions).
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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
| • |
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
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Lab: Using SQL Server Analysis Services
| • |
Exercise 1: (Level 200) Installing
SQL Server Analysis Services
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| • |
Exercise 2: (Level 200) Verifying
Installation
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After completing this module, students
will be able to:
| • |
Describe data analysis solutions.
|
| • |
Describe the key features of SQL
Server Analysis Services.
|
| • |
Install SQL Server Analysis
Services.
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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
| • |
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
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Lab: Creating Multidimensional Analysis
Solutions
| • |
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
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After completing this module, students
will be able to:
| • |
Develop Analysis Services
solutions.
|
| • |
Create a data source and a data
source view.
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| • |
Create a cube.
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Module 3: Working with Cubes and
Dimensions
This module describes how to edit
dimensions and to configure dimensions, attributes, and
hierarchies.
Lessons
| • |
Lesson 1: Configuring Dimensions
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| • |
Lesson 2: Defining Attribute
Hierarchies
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| • |
Lesson 3: Sorting and Grouping
Attributes
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Lab: Working with Cubes and Dimensions
| • |
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
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After completing this module, students
will be able to:
| • |
Configure dimensions.
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| • |
Define hierarchies.
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| • |
Sort and group attributes.
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Module 4: Working with Measures and
Measure Groups
This module explains how to edit and
configure measures and measure groups.
Lessons
| • |
Lesson 1: Working With Measures
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| • |
Lesson 2: Working with Measure
Groups
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Lab: Working with Measures and Measure
Groups
| • |
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
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After completing this module, students
will be able to:
| • |
Work with measures.
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| • |
Work with measure groups.
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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
| • |
Lesson 1: MDX Fundamentals
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| • |
Lesson 2: Adding Calculations to a
Cube
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Lab: Querying Multidimensional Analysis
Solutions
| • |
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
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After completing this module, students
will be able to:
| • |
Describe Multidimensional
Expression (MDX) fundamentals.
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| • |
Add calculations to a cube.
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Module 6: Customizing Cube Functionality
This module explains how to customize a
cube by implementing key performance indicators (KPIs),
actions, perspectives, and translations.
Lessons
| • |
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
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Lab: Customizing Cube Functionality
| • |
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
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After completing this module, students
will be able to:
| • |
Implement Key Performance
Indicators (KPIs).
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| • |
Implement actions.
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| • |
Implement perspectives.
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| • |
Implement translations.
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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
| • |
Lesson 1: Deploying an Analysis
Services Database
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| • |
Lesson 2: Securing an Analysis
Services Database
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Lab: Deploying and Securing an Analysis
Services Database
| • |
Exercise 1: (Level 200) Deploying
an Analysis Services Database
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| • |
Exercise 2: (Level 200) Securing
an Analysis Services Database
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After completing this module, students
will be able to:
| • |
Deploy an Analysis Services
database.
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| • |
Secure an Analysis Services
database.
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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
| • |
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
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Lab: Maintaining a Multidimensional
Solution
| • |
Exercise 1: (Level 200)
Configuring Processing
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| • |
Exercise 2: (Level 200)
Implementing Logging and Monitoring
|
| • |
Exercise 3: (Level 200) Backing Up
and Restoring an Analysis Services Database
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After completing this module, students
will be able to:
| • |
Configure processing settings.
|
| • |
Log, monitor, and optimize an
Analysis Services solution.
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| • |
Back up and restore an Analysis
Services database.
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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
| • |
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
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Lab: Introduction to Data Mining
| • |
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
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After completing this module, students
will be able to:
| • |
Describe data mining.
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| • |
Create a data mining solution.
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| • |
Validate data mining models.
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