INFORMATICA DATA QUALITY Training

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INFORMATICA DATA QUALITY Training


 (4.8) | 350 Ratings


Introduction


INFORMATICA DATA QUALITY Training Details
Track Regular Track Weekend Track Fast Track
Course Duration 30 Hrs 8 Weekends 5 Days
Hours 1hr/day 2 Hours a day 6 Hours a day
Training Mode Online Classroom Online Classroom Online Classroom
Delivery Instructor Led-Live Instructor Led-Live Instructor Led-Live


Course Curriculum

Informatica Data Quality Introduction

An Overview of Informatica Developer Tool

  • Overview of Data Quality and Informatica Developer covering areas such as the GUI, Mappings, Mapplets,Transformations, Content Sets, Data Objects and Tips forworking with Developer.

Analyst Collaboration

  • Review Information from the Analyst
  • Profiles/Comments/Tags
  • Create/Add to Reference Tables
  • Shared projects and team collaboration means no loss of information or duplication of work across projects

Developer Profiling

  • Perform Join Analysis Profiling
  • Perform Column Profiling
  • Multi Object Profiling
  • Build and execute a Mapping using core transformations
  • Configure Transformation Properties
  • Mid stream profiling
  • Comparative Profiling
  • Scorecard
  • Business trend analysis by using chart
  • Data quality Transformations
  • General Transformations

Data Standardization

  • Develop data standardization mapplets and mappings
  • Cleanse, transform and parse data
  • Data Quality cleansing and parsing transformations

Address Validation

  • Configuring a Reusable AV Transformation
  • AV Transformation Properties
  • AV Inputs and Ouputs
  • Define a Reusable AV Mapplet
  • Grouping Data
  • Key Generator Transformation
  • Key Gen Methods and Properties
  • Analyze Detail Report
  • DQ Matching
  • Perform matching using the Match Transformation
  • Match Properties
  • Matching Algorithms
  • Cluster Analysis Report
  • Create and update matching Mapplets
  • Additional Matching Transformations
  • Build and address validation mapping to incorporate your mapplet Matching.

Identity Matching

  • Build matching mappings using Identity Matching
  • Identity Populations and Strategies

Automatic Consolidation

  • Transformations and techniques
  • Automatically associate and consolidate your match data using a variety of association and consolidation

 Manual Consolidation Using the Data Quality Assistant

  • Perform Manual consolidation and bad record management using the DQA
  • Build mappings to create and populate the DQA tables using the new Exception Transformation

Exceptions Handling mechanism

Match and Merge mechanism

Data Consolidation Mechanism

PowerCenter Integration

  • Execute Data Quality mappings/mapplets in PowerCenter

Deployment Process

  • Learn how to export and import projects using both basic and advanced methods

DQ for Excel

  • Run Data Quality mappings on Excel spreadsheets

Parameters

  • How to use parameters in Data Quality mappings, transformations and reference tables

Content

  • What content is available with IDQ9?
  • Content Management Service
  • How to configure AV information
  • What are Accelerators?
  • What comes with the Core Accelerator?

LAB Exercises

Use Cases

Tasks

Exam & Certification

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Course Curriculum

Informatica Data Quality Introduction

An Overview of Informatica Developer Tool

  • Overview of Data Quality and Informatica Developer covering areas such as the GUI, Mappings, Mapplets,Transformations, Content Sets, Data Objects and Tips forworking with Developer.

Analyst Collaboration

  • Review Information from the Analyst
  • Profiles/Comments/Tags
  • Create/Add to Reference Tables
  • Shared projects and team collaboration means no loss of information or duplication of work across projects

Developer Profiling

  • Perform Join Analysis Profiling
  • Perform Column Profiling
  • Multi Object Profiling
  • Build and execute a Mapping using core transformations
  • Configure Transformation Properties
  • Mid stream profiling
  • Comparative Profiling
  • Scorecard
  • Business trend analysis by using chart
  • Data quality Transformations
  • General Transformations

Data Standardization

  • Develop data standardization mapplets and mappings
  • Cleanse, transform and parse data
  • Data Quality cleansing and parsing transformations

Address Validation

  • Configuring a Reusable AV Transformation
  • AV Transformation Properties
  • AV Inputs and Ouputs
  • Define a Reusable AV Mapplet
  • Grouping Data
  • Key Generator Transformation
  • Key Gen Methods and Properties
  • Analyze Detail Report
  • DQ Matching
  • Perform matching using the Match Transformation
  • Match Properties
  • Matching Algorithms
  • Cluster Analysis Report
  • Create and update matching Mapplets
  • Additional Matching Transformations
  • Build and address validation mapping to incorporate your mapplet Matching.

Identity Matching

  • Build matching mappings using Identity Matching
  • Identity Populations and Strategies

Automatic Consolidation

  • Transformations and techniques
  • Automatically associate and consolidate your match data using a variety of association and consolidation

 Manual Consolidation Using the Data Quality Assistant

  • Perform Manual consolidation and bad record management using the DQA
  • Build mappings to create and populate the DQA tables using the new Exception Transformation

Exceptions Handling mechanism

Match and Merge mechanism

Data Consolidation Mechanism

PowerCenter Integration

  • Execute Data Quality mappings/mapplets in PowerCenter

Deployment Process

  • Learn how to export and import projects using both basic and advanced methods

DQ for Excel

  • Run Data Quality mappings on Excel spreadsheets

Parameters

  • How to use parameters in Data Quality mappings, transformations and reference tables

Content

  • What content is available with IDQ9?
  • Content Management Service
  • How to configure AV information
  • What are Accelerators?
  • What comes with the Core Accelerator?

LAB Exercises

Use Cases

Tasks

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