Informatica Data Quality
Informatica Data Quality training course content - 9.5.1
Basics:
- What is Data Quality and why it is important?
- Data Quality Concepts
- Difference between PowerCenter and Data Quality
- Data Quality Architecture diagram
- Different Data Quality components and their role of use
Administrator Console:
- Gateway node and worker node
- Model Repository Service
- Data Integration Service
- Analyst Service
- Content Management Service
Developer Client
Analyst tool:
- Navigate the Developer Tool with different options and collaborate on projects with Analysts using the
Analyst Tool - Perform Column, Rule, Join, Multi object and Mid-Stream Profiling in Analyst Tool
- Manage reference tables in the Analyst Tool and Developer client
- Design and develop Mapping/Mapplets in Developer client
- Create standardization, cleansing and Parsing methods
- Validate Addresses
- Identify duplicate/similar records
- Build mappings used to associate and consolidate matched records
List of DQ transformation coverage:
- Merge
- Case Converter
- Standardizer
- Character Labeller
- Token Labeller
- Token Parser
- Matcher(classic)
- Association
- Consolidation
- Address Validation
- Keygen tx
Others:
- Deployment of DQ mappings into Power center and their business use cases
- Trouble shooting techniques
- Real time use cases covering different scenarios which will help candidate to succeed in their
interview/project development