Informatica Data Quality (IDQ) Foundation for Developers 10.5x

• Work with both the Analyst and Developer tools in all its aspects
• Collaboration with Business Analysts who use primarily the Analyst tool.
• Profile data in both the Developer and Analyst: Columns, Joins, Rules, Multi object and Mid-Stream Transformation profiling.
• Create and manage reference tables in the Analyst Tool
• Highlight and compare the Lookup transformation to reference tables in IDQ
• Design and create mapplets for reusability purposes in IDQ mappings.
• Design and create mappings to standardize, cleanse and parse data.
• Validate addresses and develop accelerators using various techniques.
• Create mappings to associate and consolidate matched records.
• Identify duplicate records and de-dupe them.
• Move IDQ mappings to PowerCenter and run them under the Workflow Manager
• Attendees will learn how to apply various strategies to cleanse and improve data quality.

• Developers with no Informatica experience

1. Data Quality Overview 2. Developer Tool: Physical & Logical Data Objects

• Overview
• Create a Physical Data Object

i. Relational ii. Customized iii. Flat File

• Create a Logical Data Object

Exercise #1 Creating Connections & Import Data source 3. Data Profiling with Developer

• Profiling
• Creating Profiling Rules
• Creating and running scorecards

Exercise #2 Column Profiling in Developer 4. Developing Data Quality Projects

• Overview
• Objects

5. Working with Mappings

• Mappings
• Sources
• Targets
• Linking and Running

6. Working with Transformations

• Transformations
• Ports
• Expressions
• Testing

7. Data Integration Transformations Part 1:

• Expression
• Filter
• Aggregator
• Sorter

8. Data Integration Transformations Part 2:

• Joiner
• Lookup
• Router
• Union

9. Working with Mapplets & Rules:

• Overview
• Creating
• Mapplet Rules

10. Using Reference Data:

• Overview
• User Reference Table
• Content sets

11. Data Standardization:

• Cleanse and standardize the data set using Standardization transformation:

i. Case Converter ii. Merger iii. Labeler iv. Standardizer Exercise #3 Data Standardization 12. Data Parsing:

• Parser the data set using one of the following methods:

i. Token Parser ii. Pattern Parser iii. NER/Probabilistic Model

• Decision

Exercise #4 Parsing using Pattern Based Parsing 13. Address Validation

• Overview
• Reference Data
• Work with Address Validation mappings

14. Data Matching

• Introducing Matching Concepts
• Data Grouping
• Matching Analysis
• Matching Transformation

Exercise #5 Apply Matching Techniques - Classic Matching 15. Identity Match Option

• IMO vs Classic Matching
• Overview
• IMO configuration

Exercise #6 Apply Matching Techniques - Identity Matching 16. Automatic Consolidation

• Consolidation transformation
• Automatically consolidate matched records

Exercise #7 Multi Criteria Matching, Association and Consolidation 17. Working with Workflows:

• Build a workflow that handle Exception and duplicate records.
• Human Task
• Other Tasks
• Deployment

Exercise #8 Informatica Data Quality Workflow Using Human Task 18. Analyst for the Exception records:

• Update and modify the exception/duplicate records through the Analyst tool.

19. Other Transformations:

• Comparison
• Weighted Average

20. Power Center Integration:

• Exporting to PC
• Importing from PC