Course Description

This course provides participants with introductory to advanced knowledge of metadata modeling concepts, and how to model metadata for predictable reporting and analysis results using Framework Manager. Participants will learn the full scope of the metadata modeling process, from initial project creation, to publishing of metadata to the Web, enabling end users to easily author reports and analyze data.


Please refer to course overview


Developers who design metadata models for use in IBM Cognos Analytics.


Knowledge of common industry-standard data structures and design.Experience with SQLExperience gathering requirements and analyzing data.IBM Cognos Analytics: Author Reports Fundamentals (recommended) 


  1. Describe IBM Cognos Analytics and its position within an analytics solution
  2. Describe IBM Cognos Analytics components
  3. Describe IBM Cognos Analytics at a high level
  4. Explain how to extend IBM Cognos
  1. Define the role of a metadata model in Cognos Analytics
  2. Distinguish the characteristics of common data structures
  3. Understand the relative merits of each model type
  4. Examine relationships and cardinality
  5. Identify different data traps
  6. Identify data access strategies
  1. Examine key modeling recommendations
  2. Define reporting requirements
  3. Explore data sources to identify data access strategies
  4. Identify the advantages of modeling metadata as a star schema
  5. Model in layers
  1. Follow the IBM Cognos and Framework Manager workflow processes
  2. Define a project and its structure
  3. Describe the Framework Manager environment
  4. Create a baseline project
  5. Enhance the model with additional metadata
  1. Verify relationships and query item properties
  2. Create efficient filters by configuring prompt properties
  1. Describe multi-fact queries and when full outer joins are appropriate
  2. Describe how IBM Cognos uses cardinality
  3. Identify reporting traps
  4. Use tools to analyze the model
  1. Understand the benefits of using model query subjects
  2. Use aliases to avoid ambiguous joins
  3. Merge query subjects to create as view behavior
  4. Resolve a recursive relationship
  5. Create a complex relationship expression
  1. Create virtual dimensions to resolve fact-to-fact joins
  2. Create a consolidated modeling layer for presentation purposes
  3. Consolidate snowflake dimensions with model query subjects
  4. Simplify facts by hiding unnecessary codes
  1. Use calculations to create commonly-needed query items for authors
  2. Use static filters to reduce the data returned
  3. Use macros and parameters in calculations and filters to dynamically control the data returned
  1. Make time-based queries simple to author by implementing a time dimension
  2. Resolve confusion caused by multiple relationships between a time dimension and another table
  1. Use determinants to specify multiple levels of granularity and prevent double-counting
  1. Identify the dimensions associated with a fact table
  2. Identify conformed vs. non-conformed dimensions
  3. Create star schema groupings to provide authors with logical groupings of query subjects
  4. Rapidly create a model using the Model Design Accelerator
  5. Rapidly create a model using the Model Design Accelerator
  1. Identify the effects of modifying query subjects on generated SQL
  2. Specify two types of stored procedure query subjects
  3. Use prompt values to accept user input
  1. Examine the IBM Cognos security environment
  2. Restrict access to packages
  3. Create and apply security filters
  4. Restrict access to objects in the model
  1. Apply dimensional information to relational metadata to enable OLAP-style queries
  2. Sort members for presentation and predictability
  3. Define members and member unique names
  4. Identify changes that impact a MUN
  1. Connect to an OLAP data source (cube) in a Framework Manager project
  2. Publish an OLAP model
  3. Publish a model with multiple OLAP data sources
  4. Publish a model with an OLAP data source and a relational data source
  1. Governors that affect SQL generation
  2. Stitch query SQL
  3. Conformed and non-conformed dimensions in generated SQL
  4. Multi-fact/multi-grain stitch query SQL
  5. Variances in IBM Cognos Analytics – Reporting generated SQL
  6. Dimensionally modeled relational SQL generation
  7. Cross join SQL
  8. Various results sets for multi-fact queries
  1. Identify environment and model session parameters
  2. Leverage session, model, and custom parameters
  3. Create prompt macros
  4. Leverage macro functions associated with security
  1. Perform basic maintenance and management on a model
  2. Remap metadata to another source
  3. Import and link a second data source
  4. Run scripts to automate or update a model
  5. Create a model report
  1. Identify how minimized SQL affects model performance
  2. Use governors to set limits on query execution
  3. Identify the impact of rollup processing on aggregation
  4. Apply design mode filters
  5. Limit the number of data source connections
  6. Use the quality of service indicator
  1. Segment and link a project
  2. Branch a project and merge results
  1. Specify package languages and function sets
  2. Control model versioning
  3. Nest packages
  4. Leverage a user defined function
  5. Identify the purpose of query sets
  6. Use source control to manage Framework Manager files
  7. Customize metadata for a multilingual audience