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SQL Server Analysis Services: Hands-On

Analyzing Data for Business Intelligence

 
Course: 139     Type: Hands-On Training     Duration: 4 Days

Quick Enroll    

You Will Learn How To
  • Leverage SQL Server Analysis Services to produce Business Intelligence solutions
  • Create and deploy multidimensional data cubes
  • Extend hierarchies and exploit advanced dimension relationships
  • Slice and dice your data with advanced dimension hierarchies
  • Make smarter business decisions with data mining techniques
  • Implement Key Performance Indicators (KPIs) to monitor business objectives

Course Benefits
With the current explosion of data in today's enterprise environment, traditional methods of querying and reporting on information are no longer sufficient. This course provides the knowledge and skills to analyze and discover trends in your data warehouse. You learn to create On-Line Analytical Processing (OLAP) cubes using Business Intelligence tools and leverage the Analysis Services administrative tools to better manage and maintain your data.

Who Should Attend
Those designing, creating or developing analysis cubes from a database. Familiarity with Microsoft Business Intelligence tools or Course 146, "Microsoft Tools for Business Intelligence," is helpful.

Hands-On Training
Throughout this course, you gain extensive experience with SQL Server Analysis Services. Practical exercises include:
  • Building a data source view
  • Creating and deploying a cube
  • Modifying cube dimensions
  • Navigating hierarchies
  • Establishing relationships in the data model
  • Creating and using a perspective for browsing
  • Implementing a security policy
  • Forecasting trends with data mining techniques

Course 139 Content
Introduction to Business Intelligence (BI)
  • BI Studio for Analysis Services
  • Building data sources and views in the Unified Dimensional Model (UDM)
  • Creating data source views
Building and Modifying an OLAP Cube
Defining fact tables
  • Identifying and selecting available measures
  • Adding new measure groups and creating custom measures
  • Determining foreign key dependencies with dimensions
Creating dimensions
  • Implementing a Star and Snowflake Schema
  • Managing Slow Changing Dimensions (SCD)
  • Identifying role-play dimensions
Fact dimension
  • Building a dimension on a fact table
  • Relating the fact dimension to other dimensions
  • Browsing fact dimension data
Partitioning for optimal performance
  • Choosing between ROLAP, MOLAP and HOLAP for performance and storage requirements
  • Configuring incremental updates
  • Defining aggregate storage
  • Reviewing partitioning best practices
Modifying dimension attributes
  • Using dimension properties for specific needs
  • Implementing stored procedures for Analysis Services
  • Improving dimension usability
  • Changing granularity in a measure group
Extending the Cube with Hierarchies
Parent-child relationships
  • Declaring hierarchies
  • Grouping related attributes
  • Equal Areas
  • Clusters
  • Buckets
Creating hierarchies
  • Building hierarchies on multiple dimensions
  • Utilizing the dimension Attribute Relationship tab
  • Taking advantage of the Aggregate Transformation Editor
  • Editing grouping properties
  • Choosing between ragged, balanced and unbalanced hierarchies
Expanding the BI Data Model
Referencing relationships
  • Converting a dimension to a measure
  • Building referenced dimensions
Resolving many-to-many relationships
  • Identifying relationship anomalies
  • Implementing intermediate fact and dimension tables
Creating cube perspectives
  • Filtering business-related information
  • Slicing and dicing data
  • Working with local languages
Retrieving data with MDX
  • Composing simple MDX queries
  • Writing MDX expressions
  • Navigating hierarchies with parent, child, cousin and ancestor
Adding Attributes
Moving attributes
  • Importing attributes from the data source view
  • Removing and hiding attributes
Managing attribute properties
  • Creating composite keys
  • Building calculated members
  • Setting the sort order and sorting on an alternate key
Gaining Business Advantage with Data Mining
Finding patterns in your data
  • Correlating business trends
  • Predicting future trends with algorithms
Determining the correct model
  • Choosing between discrete and continuous attributes
  • Analyzing various data mining algorithms
Validating models
  • Training algorithms for optimal results
  • Exploring results with data mining viewers
Monitoring KPIs for Better Business Decisions
Reporting on business objectives with KPIs
  • Selecting critical performance indicators
  • Implementing KPIs with expressions
  • Running reports based on Analysis Services
Browsing the cube data with Excel Services
  • Viewing dashboard gauges of analysis data
  • Encapsulating business trends into a single view

Related Courses
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Save Up to 45% per course on the standard tuition!

SQL Server Analysis Services: Hands-On
Upcoming Dates
Jan 13 - 16, 2009
 Washington, DC (Rockville, MD)
Feb 24 - 27, 2009
 Los Angeles
Mar 17 - 20, 2009
 Ottawa
Mar 17 - 20, 2009
 Washington, DC (Alexandria, VA)
Mar 24 - 27, 2009
 New York
Mar 24 - 27, 2009
 Toronto
Apr 21 - 24, 2009
 Washington, DC (Rockville, MD)
May 19 - 22, 2009
 Washington, DC (Reston, VA)
May 26 - 29, 2009
 Chicago (Schaumburg)
Jun 16 - 19, 2009
 Ottawa

SQL Server Analysis Services: Hands-On
Bring Learning Tree On-Site

Course Tuition
$ 2,790 Standard Tuition
Tuition with a Savings Plan
$ 1,800 10-Day Pass
$ 1,670 Training Passport
$ 1,625 Premium-Pass
$ 2,200 Voucher 10-Pack
$ 2,515 Alumni Gold Discount
$ 2,484 Government Discount
 

 

SQL Server Analysis Services: Hands-On
SQL Server Analysis Services: Hands-On
Participants analyzing KPI data in a dashboard.
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SQL Server 2005 for the Enterprise


CPE 23 Credits 1 Hour(s) College Credit
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