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Informatica Data Quality training course content - 9.5.1
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Basics:
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- 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
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Administrator Console:
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- Gateway node and worker node
- Model Repository Service
- Data Integration Service
- Analyst Service
- Content Management Service
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Developer Client
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Analyst tool:
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- 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
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List of DQ transformation coverage:
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- Merge
- Case Converter
- Standardizer
- Character Labeller
- Token Labeller
- Token Parser
- Matcher(classic)
- Association
- Consolidation
- Address Validation
- Keygen tx
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Others:
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- 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
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