Curriculum
- 1 Section
- 11 Lessons
- 4 Days
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- Topics11
- 1.11. 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
- 1.25. Working with Mappings Mappings Sources Targets Linking and Running
- 1.36. Working with Transformations Transformations Ports Expressions Testing
- 1.47. Data Integration Transformations Part 1: Expression Filter Aggregator Sorter
- 1.58. Data Integration Transformations Part 2: Joiner Lookup Router Union
- 1.69. Working with Mapplets & Rules: Overview Creating Mapplet Rules
- 1.710. Using Reference Data: Overview User Reference Table Content sets
- 1.811. 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
- 1.914. 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.
- 1.1019. Other Transformations: Comparison Weighted Average
- 1.1120. Power Center Integration: Exporting to PC Importing from PC
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.
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20. Power Center Integration: Exporting to PC Importing from PC
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