Course Overview
This Informatica Data Quality Foundation Bootcamp for Developers course provides attendees with an excellent overview of the Informatica Data Quality Developer tool that includes real-life exercises to enhance their learning of the tool.
Informatica Data Quality Developer is a thick client tool based on Eclipse. It enables developers to profile data, interact, and apply all business rules provided by business analysts. Using the Developer, users implement business rules by creating mappings that include, in addition to standard ETL transformations, data quality-specific transformations such as standardization, matching, address validation, parsing, and consolidation. Attendees will learn how to apply various strategies to cleanse and improve data quality.
Collaboration with analysts via the repository/metadata will also be discussed and implemented.
Target audiences
- Developers with no Informatica experience
Curriculum
- 1 Section
- 19 Lessons
- 4 Days
- Topics19
- 1.11. Data Quality Overview 2. Developer Tool: Physical & Logical Data Objects Overview Create a Physical Data Object Relational Customized Flat File Create a Logical Data Object Exercise #1 Creating Connections & Import Data source
- 1.23. Data Profiling with Developer Profiling Creating Profiling Rules Creating and running scorecards Exercise #2 Column Profiling in Developer
- 1.34. Developing Data Quality Projects Overview Objects
- 1.45. Working with Mappings Mappings Sources Targets Linking and Running
- 1.56. Working with Transformations Transformations Ports Expressions Testing
- 1.67. Data Integration Transformations Part 1: Expression Filter Aggregator Sorter
- 1.78. Data Integration Transformations Part 2: Joiner Lookup Router Union
- 1.89. Working with Mapplets & Rules: Overview Creating Mapplet Rules
- 1.910. Using Reference Data: Overview User Reference Table Content sets
- 1.1011. Data Standardization: Cleanse and standardize the data set using Standardization transformation: Case Converter, Merger, Labeler, Standardizer Exercise #3 Data Standardization
- 1.1112. Data Parsing: Parser the data set using one of the following methods: Token Parser, tern ParserNER/Probabilistic Model ? Dec, ision Exercise #4 Parsing using Pattern Based Parsing
- 1.1213. Address Validation Overview Reference Data Work with Address Validation mappings
- 1.1314. Data Matching Introducing Matching Concepts Data Grouping Matching Analysis Matching Transformation Exercise #5 Apply Matching Techniques – Classic Matching
- 1.1415. Identity Match Option IMO vs Classic Matching Overview IMO configuration Exercise #6 Apply Matching Techniques – Identity Matching
- 1.1516. Automatic Consolidation Consolidation transformation Automatically consolidate matched records Exercise #7 Multi Criteria Matching, Association and Consolidation
- 1.1617. 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
- 1.1718. Analyst for the Exception records: Update and modify the exception/duplicate records through the Analyst tool.
- 1.1819. Other Transformations: Comparison Weighted Average
- 1.1920. Power Center Integration: Exporting to PC Importing from PC
Instructor
