Cognos Analytics

IBM Cognos Framework Manager: Design Metadata Models (v11.0)

Audience

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

Prerequisites

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

Key Topics

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