Informatica Data Quality training course content - 9.5.1
  • 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
  • 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