SSAS Course Content

Building and Modifying an OLAP Cube

• Designing a Unified Dimension Model (UDM)
  • Identifying measures and their suitable granularities
  • Adding new measure groups and creating custom measures
• Creating dimensions
  • Implementing a Star and Snowflake Schema
  • Managing Slow Changing Dimensions (SCD)
  • Identifying role-play dimensions

Extending the Cube with Hierarchies

• Creating hierarchies
  • Building natural hierarchies
  • Many-to-many hierarchies
  • Creating attribute relationships
  • Distinguishing between ragged, balanced and unbalanced hierarchies
  • Discretizing attribute values with the Clusters and Equal Areas algorithms
• Parent-child relationships
  • Defining parent and key attributes
  • Generating level captions with the Naming Template feature
  • Removing repeated entries with the Members With Data property

Exploiting Advanced Dimension Relationships

• Storing dimension data in fact tables
  • Building a degenerate dimension
  • Configuring fact relationships
• Saving space with referenced dimension relationships
  • Identifying candidates for referenced relationships
  • Utilizing the Dimension Usage tab to configure referenced relationships
• Including dimensions with many-to-many relationships
  • Implementing intermediate measure groups and dimensions
  • Reporting on many-to-many dimensions without double counting

Designing Optimal Cubes

• Assembling cube components
  • Selecting the appropriate fact tables
  • Adding cube dimensions
  • Distinguishing between additive, semiadditive and nonadditive measures
• Designing storage and aggregations
  • Choosing between ROLAP, MOLAP and HOLAP
  • Partitioning cubes for improved performance
  • Designing aggregations with the Aggregation Design Wizard
  • Leveraging the Usage-Based Optimization Wizard
• Automating processing
  • Exploiting XMLA scripts and SSIS
  • Refreshing cubes with Proactive Caching

Performing Advanced Analysis with MDX

• Retrieving data with MDX
  • Defining tuples, sets and calculated members
  • Querying cubes with MDX
  • Navigating hierarchies with MDX and utilizing set functions
• Monitoring business performance with KPIs
  • Building goal, status and trend expressions
  • Using PARALLELPERIOD to compare with past time periods
• Creating calculations with MDX
  • Adding runtime calculations to the cube
  • Comparing MDX calculations with DSV calculated columns

Securing Cube Data

• Securing data and simplifying the user interface
  • Distinguishing between perspective feature and security
  • Creating roles for administrative privileges
  • Securing dimension data
  • Implementing cell-level security

Gaining Business Advantage with Data Mining

• Determining the correct model
  • Identifying business tasks for data mining
  • Training and testing data mining algorithms
  • Comparing algorithms with the accuracy chart and classification matrix
  • Optimizing returns with the Profit Chart
• Performing real-world predictions
  • Classifying with the Decision Trees, Neural Network and Naive Bayes algorithms
  • Predicting with the Time Series algorithm
• Deploying models
  • Predicting new cases with algorithms
  • Utilizing DMX to perform batch and singleton predictions
  • Exploring results with data mining viewers