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Informatica Data Quality (IDQ) Foundation for Developers 10.5x

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4 Days
INPC-205
Classroom Training, Online Training
Informatica Training

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Course Description

Overview

IDQ Foundation Bootcamp for Developers course provides an excellent overview of the Informatica Data Quality Developer tool using real use cases as exercises to enhance the learning experience.

 

Objectives

  • 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.

Audience

  • Developers with no Informatica experience

Prerequisites


     

Topics

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
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