Trainingen

Course content

Introduction to IBM Cognos Analytics

• 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

Identifying common data structures

• 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

Defining requirements

• 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

Creating a baseline project

• 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

Preparing reusable metadata

• Verify relationships and query item properties
• Create efficient filters by configuring prompt properties

Modeling for predictable results: Identifying reporting Issues

• 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

Modeling for predictable results: Virtual star schemas

• 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

Modeling for predictable results: consolidate metadata

• 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

Creating calculations and filters

• 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

Implementing a time dimension

• 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

Specifying determinants

• Use determinants to specify multiple levels of granularity and prevent double-counting

Creating the presentation view

• 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
• Rapidly create a model using the Model Design Accelerator

Working with different query subject types

• 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

Setting Security in Framework Manager

• Examine the IBM Cognos security environment
• Restrict access to packages
• Create and apply security filters
• Restrict access to objects in the model

Creating Analysis objects

• 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

Managing OLAP Data Sources

• 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

Advanced generated SQL concepts and complex queries

• 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

Using advanced parameterization techniques in Framework Manger

• Identify environment and model session parameters
• Leverage session, model, and custom parameters
• Create prompt macros
• Leverage macro functions associated with security

Model maintenance and extensibility

• 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

Optimizing and tuning Framework Manager models

• 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

Working in a Multi-Modeler Environment

• Segment and link a project
• Branch a project and merge results

Managing packages in Framework Manager

• Specify package languages and function sets
• Control model versioning
• Nest packages

Appendix A. Additional modeling techniques

• Leverage a user defined function
• Identify the purpose of query sets
• Use source control to manage Framework Manager files

Appendix B. Modeling multilingual metadata

• Customize metadata for a multilingual audience

Previous knowledge

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• 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)