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Course Outline

 

IBM - B5252 - IBM Cognos Framework Manager Design Metadata Models (V10.2) Training

Overview:

IBM Cognos Framework Manager: Design Metadata Models (V10.2) 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. 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. TLG Learning has formed a relationship with LearnQuest to provide IBM training. LearnQuest is one of a handful of providers selected by IBM to be an Authorized Global Training Provider.

Pre-requisite(s):

This advanced offering is intended for developers who design metadata models for use in IBM Cognos.

Audience:

This course is intended for Developers.

Length/Cost:

5 Days, $3500

Outline:

Lesson 1 : Overview of IBM Cognos BI

Discuss IBM Cognos and Performance Management
Describe IBM Cognos components
Describe IBM Cognos architecture at a high level
Define IBM Cognos groups and roles
Explain how to extend IBM Cognos

Lesson 2 : Identify Common Data Structures

Examine the characteristics of operational databases and databases designed for reporting
Examine relationships and cardinality
Identify different data traps
Examine dimensional data sources

Lesson 3 : Gather Requirements

Examine key modeling recommendations
Define reporting requirements
Explore data sources to identify data access strategies

Lesson 4 : Create a Baseline Project

Examine the IBM Cognos BI 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

Lesson 5 : Prepare Reusable Metadata

Identify facts and dimensions
Examine relationships, and data traps
Verify relationships and query item properties
Ensure efficient filters by configuring prompt properties

Lesson 6 : Model for Predictable Results: Identify Reporting Issues

Describe multi-fact queries and when full outer joins are appropriate
Describe how IBM Cognos BI uses cardinality
Identify reporting traps
Use tools to analyze the model

Lesson 7 : 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

Lesson 8 : Model for Predictable Results: Consolidate Metadata

Create virtual facts to simplify writing queries
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

Lesson 9 : 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

Lesson 10 : Implement 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

Lesson 11 : Specify Determinants

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

Lesson 12 : Create 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

Lesson 13 : Work with Different Query Subject Types

Identify key differences and recommendations for using data source, model, and stored procedure query subjects
Identify the effects on generated SQL when modifying query subjects, SQL settings and relationships

Lesson 14 : 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

Lesson 15 : Create Analysis Objects

Apply dimensional information to relational Metadata to enable OLAP-style queries
Define members and member unique names
Identify changes that impact a MUN
Sort members for presentation and predictability

Lesson 16 : Manage 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

Lesson 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 Report Studio generated SQL
Dimensionally modeled relational SQL generation
Cross join SQL
Various results sets for multi-fact queries

Schedule:

= "Guaranteed to Run"
ILT - Instructor-Led Training | MLT - Mentor-Led Training | Anywhere LIVE OLT - Live Online Training | Bellevue LIVE OLT - Live Online Training


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