Designing
an Analysis Solution Architecture Using Microsoft SQL Server 2005 Analysis
Services
Course 2796: Three days; Instructor-led
Introduction
Elements of this syllabus are subject to change.
The purpose of this 3-day course is to teach Business Intelligence (BI) professionals working in enterprise environments to design a multidimensional solution architecture that supports their BI solution. Students will go through the entire process—from capturing business and technical requirements to deploying a multidimensional solution to production. Students will also be taught to develop custom functionality and optimize a multidimensional solution.
Audience
This course is intended for experienced BI professionals. The target students for this course already have an understanding of how to use the SQL Server 2005 tools to implement BI functionality, but need to develop their understanding of design principles and best practices when planning, implementing, and deploying an Analysis Services solution.
At Course Completion
After completing
this course, students will be able to:
• Capture
the business and technical requirements for a BI solution.
• Design
and implement a logical Online Analytical Processing (OLAP) solution architecture.
• Design
physical storage for a multidimensional solution.
• Create
calculated members and named sets.
• Implement
Key Performance Indicators (KPIs), actions, and stored procedures.
• Design
the infrastructure for an OLAP solution.
• Deploy
and secure an Analysis Services solution in a production environment.
• Monitor
and optimize an Analysis Services solution.
• Implement
a Data Mining Solution.
Prerequisites
Before attending
this course, students must:
• Have
hands-on experience with database development tasks. For example:
• Creating
Transact-SQL queries
• Writing
and optimizing advanced queries (for example, queries that contain complex
joins or subqueries)
• Creating
database objects such as tables, views, and indexes
• Have
foundational conceptual understanding of data warehousing, data marts,
and Business Intelligence. Students must be well versed on the subjects
of data warehousing, data marts, and BI, and preferably have read at least
one book by Ralph Kimball or Bill Inmon.
• Conceptual
understanding of OLAP technologies, multi-dimensional data, MDX, and relational
database modeling. For example, know what facts, dimensions, measures,
calculated measures, and foreign keys are.
• Be
familiar with SQL Server 2005 features, tools, and technologies. In particular,
they must have built and queried a cube.
• Have
foundational understanding of Microsoft Windows security. For example,
how groups, delegation of credentials, and impersonation function in a
security context.
• Have
foundational understanding of Web-based architecture. For example, SSL,
SOAP, and IIS—what they are and what their role is.
• Must
understand the difference between replication and ETL.
• Already
know how to use:
• Microsoft
Office Visio
• Microsoft
SQL Server Business Intelligence Development Studio
• Microsoft
SQL Server Management Studio
• Performance
Monitor
• Microsoft
SQL Server Profiler
Course Outline
Module 1: Capturing Business and Technical Requirements
In this module, students will first learn about key design principles that they should consider when you define the scope of a BI project. They will then learn how to identify the business and technical requirements to ensure that their solution meets the needs of its users.
Lessons
• Planning
a Multidimensional Solution
• Identifying
Requirements and Constraints
Lab 1: Capturing
Business and Technical Requirements
• Reviewing
Solution Requirements
• Identifying
Further Information Requirements
After completing
this module, students will be able to:
• Describe
the purpose of a multidimensional solution.
• Identify
requirements and constraints when designing a multidimensional solution.
Module 2: Designing and Implementing a Logical OLAP Solution Architecture
This module describes considerations and guidelines for designing an OLAP solution, including a relational data warehouse and an Analysis Services cube.
Lessons
• Planning
a Unified Dimensional Model
• Designing
and Implementing Fact and Dimension Tables
• Designing
and Implementing Cubes
Lab 2: Designing
and Implementing a UDM
• Designing
and Implementing a Relational Database Schema
• Designing
and Implementing a Cube
• Designing
and Implementing Perspectives
After completing
this module, students will be able to:
• Describe
design considerations for a UDM solution.
• Describe
design considerations for the relational schema of an OLAP solution.
• Describe
considerations for designing and implementing OLAP cubes.
Module 3: Designing Physical Storage for a Multidimensional Solution
In this module, students will learn how to design an effective physical storage solution for a multidimensional application.
Lessons
• Designing
Physical Storage
• Partitioning
Relational Data
• Partitioning
Multidimensional Data
Lab 3: Designing
and Implementing Physical Storage
• Designing
and Implementing a Storage Solution
• Designing
and Implementing Relational Partitioning
• Designing
and Implementing Multidimensional Partitioning
• Testing
the Solution
After completing
this module, students will be able to:
• Design
an effective physical storage solution for dimensions and measures.
• Partition
relational data.
• Partition
multidimensional data.
Module 4: Creating Calculations
In this module, students will learn how to create Multidimensional Expression (MDX) calculations. The module describes how to create calculated members, named sets, and scoped assignments.
Lessons
• Implementing
Calculated Members
• Implementing
Named Sets
• Implementing
Scoped MDX Scripts
Lab 4: Implementing
Calculations
• Creating
Calculated Members
• Creating
Named Sets
• Creating
a Scoped MDX Script
After completing
this module, students will be able to:
• Create
calculated members.
• Create
named sets.
• Create
scoped assignments.
Module 5: Extending Cube Functionality
In this module, students will learn about the benefits of KPIs, actions, and stored procedures. They will also learn how to implement KPIs, actions, and stored procedures in an Analysis Services cube.
Lessons
• Key
Performance Indicators
• Actions
• Stored
Procedures
Lab 5: Implementing
Advanced Functionality
• Creating
KPIs
• Creating
Actions
• Creating
Stored Procedures
After completing
this module, students will be able to:
• Create
KPIs.
• Create
actions.
• Create
stored procedures.
Module 6: Designing an Analysis Services Infrastructure
In this module, students will learn how to design an appropriate infrastructure for an OLAP application.
Lessons
• Considerations
for Analysis Services Resource Requirements
• Considerations
for Analysis Services Scalability
• Considerations
for Analysis Services Availability
Lab 6: Designing
and Implementing Analysis Services Infrastructure
• Planning
Production System Infrastructure
• Installing
Analysis Services in a Cluster
After completing
this module, students will be able to:
• Specify
appropriate hardware and software resources for an Analysis Services solution.
• Design
an Analysis Services infrastructure that supports high scalability.
• Design
an Analysis Services infrastructure that supports high availability.
Module 7: Deploying a Multidimensional Solution into Production
In this module, students will learn about and compare the different deployment methods available in SQL Server 2005 Analysis Services. They will also learn about how security in Analysis Services functions and how to protect their company’s critical business information.
Lessons
• Deploying
an Analysis Services Database
• Managing
Analysis Services Security
Lab 7: Deploying
Analysis Services into Production
• Deploying
an Analysis Services Database
• Enabling
User Access
After completing
this module, students will be able to:
• Deploy
an Analysis Services solution.
• Secure
an Analysis Services solution.
Module 8: Optimizing an OLAP Solution
In this module, students will learn how to monitor Analysis Services and how to optimize performance of their Analysis Services solutions.
Lessons
• Monitoring
Analysis Services
• Optimizing
Performance
Lab 8: Optimizing
Analysis Services
• Monitoring
Analysis Services
• Optimizing
the Relational Database
• Optimizing
Queries
After completing
this module, students will be able to:
• Monitor
Analysis Services.
• Optimize
Analysis Services performance.
Module 9: Implementing Data Mining
In this module, students will learn what a data mining solution is and how to design and implement data mining functionality with SQL Server Analysis Services.
Lessons
• Introduction
to Data Mining
• Implementing
a Data Mining Solution
• Using
Data Mining in a BI Solution
Lab 5: Implementing
Data Mining
• Creating
a Data Mining Structure
• Validating
a Data Mining Structure
After completing
this module, students will be able to:
• Plan
a data mining solution.
• Implement
a data mining solution.
• Use
data mining in a BI solution.
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