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Framework Manager
Cognos Training Overview

IBM Cognos Framework Manager: Design Metadata Models can be as short as one day and as long as five days, depending upon your needs. You will receive Introductory to advanced knowledge of metadata modeling concepts, and how to model metadata for predictable reporting and analysis results using Framework Manager. You 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

This intermediate course is for developers who design metadata models for use in IBM Cognos. It is suggested but not required that you complete IBM Cognos Report Studio: Author Professional Reports Fundamentals prior to taking IBM Cognos Framework Manager: Design Metadata Models

Cognos Training Course duration

1 to 5 days

Cognos Training Course outline

Overview of IBM Cognos
  • Consider IBM Cognos and Performance Management
  • What are the IBM Cognos components?
  • Portray IBM Cognos architecture, 10,000 foot view
  • What is the difference between groups and roles
Review Data Structures
  • What is an operational/transactional (production) database?
  • What is an operational data store – ODS?
  • What is a star schema?
  • What is a Data Warehouse?
  • What is a Data Mart?
  • What is a Data Cube?
  • Examine OLAP data sources
  • What is SQL?
  • What is MDX?
Create a Base Project
  • What is the IBM Cognos BI Framework Manager work flow processes?
  • What is a project? What is the structure of a project?
  • How to navigate the Framework Manager environment
  • Create a project
  • How and when do you add data to a project?
Prepare Reusable Metadata
  • Examine relationships between query subjects
  • Define properties for every query item
  • Filters, parameters, and prompts are part of the same animal. Control that animal by setting the properties of prompts
Work with Different Query Subject Types
  • Identify key differences and recommendations for using data sources and model query subjects
  • Identify the effects on generated SQL when modifying query subjects, SQL settings and relationships
Model for Predictable Results
  • What is modeling?
  • What are Predictable Results?
  • What is an outer join? Why do we care? Why do you want to avoid outer joins? When is an outer join appropriate?
  • What is a multi-fact query?
  • What is a multi-grain query?
  • What is cardinality?
  • What tools are provided to analyze the model?
Create virtual facts and dimensions
  • Simplify queries by creating virtual fact query subjects
  • Remove all fact-to-fact joins by creating virtual dimensions query subjects
  • Create a consolidated modeling layer for presentation purposes
  • Consolidate snowflake dimensions with model query subjects
  • Simplify facts by hiding unnecessary codes
Add Business Rules (Calculations and Filters)
  • Add query items (data items) not in the data sources by using calculations to implement business rules
  • Add imbedded filters that the reports must use
  • Add standalone filters that the report author/consumer can opt to use
  • Use macros and parameters in calculations and filters to dynamically control the data returned
Identify Determinants
  • Prevent double-counting by using determinants to resolve multiple levels of granularity
Implement a Time Dimension
  • Implementing a time dimension to aid with confusing time based queries
  • Use role playing dimensions to remove query traps caused by multiple dates fields in a fact query subject
  • Use a time dimension to resolve unpredictable results within a multi-fact multi-grain query
Model for Predictable Results: Virtual Star Schemas
  • Identify the advantages of modeling metadata as a star schema
  • Model in layers
  • Create aliases to avoid ambiguous joins
  • Merge query subjects to create as view behavior
Create MDR (Multi-Dimensional Relational) Query Subjects
  • What is a conformed dimension?
  • What is a Regular Dimension?
  • What is a Fact Dimension?
  • How do you associate Regular Dimensions with Fact Dimensions?
  • Use Scope to deal with multi-fact multi grain queries
  • Define sort and presentation properties
Administer Cubes (OLAP Data Sources)
  • Add a cube to a Framework Manager project
  • Publish a cube – OLAP data source
  • Models with multiple cubes
  • Models with cubes AND relational data. Why & How?
Member Unique Names
  • SQL vs. MDX
  • What is a member (category), what is a MUN?
  • Why do you care about MUNs?
Advanced Generated SQL Concepts and Complex Queries
  • Set Governors to keep your queries under control
  • Stitch queries, your friend and mine
  • Check out the SQL behind data mart queries. Conformed?
  • What to look for in the SQL used by Multi-fact/multi-grain stitch queries
  • Report Studio SQL vs. Framework Manager SQL
  • DMR SQL vs. OLAP MDX
  • Cross joins
  • Result sets used by multi-fact queries
Parameterization Practices
  • Session parameters and Model parameters
  • Take advantage of session, model, and custom parameters
  • How to create a prompt macros
Model Maintenance and Sharing
  • Basic maintenance and management
  • Remap metadata to a different data source
  • Use multiple data sources• Scripts• Lineage
  • Model reports – how to make an auditor happy
Optimization and Tuning Techniques
  • Theory vs. Reality
  • Options
Multi-Modeler Situation
  • How to employ repository control
  • Segmentation and linking within a project and multiple projects
  • Branch and merge a project
Administer Framework Manager Packages
  • Function sets
  • Model versioning
Set Security in Framework Manager
  • Examine the IBM Cognos BI security environment
  • Restrict access to packages
  • Create and apply security filters
  • Restrict access to objects in the model
  • Macro functions and security
Model for Drill-Through in Framework Manager (optional)
  • Identify conformed values between data sources
  • Define a report drill through
  • Define a package-based drill through
  • Identify drill-through values

 
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