IBM Cognos Framework Manager: Design Metadata Models (v11.0) SPVC - J4052G

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.

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1. 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
2. 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
3. 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
4. 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
5. Preparing reusable metadata
Verify relationships and query item properties
Create efficient filters by configuring prompt properties
6. 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
7: 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
8. 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
9. 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
10. 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
11. Specifying determinants
Use determinants to specify multiple levels of granularity and prevent double-counting
12. 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
13. 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
14. 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
15. 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
16. 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
17. 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
18. 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
19. 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
20. 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
21. Working in a Multi-Modeler Environment
Segment and link a project
Branch a project and merge results
22. 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

Aim

Please refer to course overview

Participant requirements

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)

Participant

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