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Data Quality 10: Developer, Level 1

onDemand | Data Quality | Self-Paced

Data Quality 10: Developer, Level 1

Course Overview

Develop Data Quality processes to cleanse, standardize, identify and consolidate duplicate records using self-paced modules and labs.

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Objectives

After successfully completing this course, students should be able to:

  • Navigate the Developer Tool and collaborate on projects with Analysts.
  • Perform Column, Rule, Multi object, Comparative and Mid-Stream Profiling.
  • Manage reference tables.
  • Create standardization, cleansing and Parsing Mappings and Mapplets.
  • Identify duplicate records using Classic Data Matching.
  • Use Automatic Consolidation to consolidate duplicate records.
  • Build mappings to associate and consolidate matched records.
  • Create and execute Workflows to populate user inboxes with Exception and Duplicate record tasks.
  • Run data quality mappings in DQ Standalone.
  • Import and export Projects.

Target Audience

  • New users of Informatica Developer, including Business and Data Developers.
  • Users who wish to re-familiarize themselves with the Developer tool

Delivery Method

  • Blended Learning consisting of eLearning / ILT / onDemand Modules

Course Duration 

  • A blend of eLearning and onDemand Modules approximating 10 hours
  • Two Days of instructor-led training
  • 55% lecture, 45% hands on lab


Agenda

Prerequisite eLearning Modules:

Module 1: What to Expect

  • Where to begin and how to proceed.

Module 2: An Introduction to Informatica Developer

  • Work with Informatica Developer.
  • Introduce the GUI, Mappings, Mapplets, Transformations, Content Sets, Data Objects.
  • Demonstration: Create a project and assign permissions.
  • Demonstration: Create a connection to an Oracle table and import a flat file.
  • Demonstration: Build a mapping and configure transformations, apply a mapplet, preview and run the mapping.
  • Demonstration: Perform Column and Mid-Stream Profiling.

Module 3: Informatica Data Quality Developer Course

  • Introduction
  • An Introduction to the Data Quality Course and Data Quality Concepts and Processes.

Module 4: Project Collaboration and Reference Table Management

  • Review projects created by the Analyst.
  • Review objects, Profiles, Rules, Scorecards, Comments and Tags.
  • Work in a Team Based Development Environment.
  • Create/add Reference tables.
  • Demonstration: Review a project created by an Analyst.

 

Instructor Led Training Core Modules:

Developer Profiling and Logical Data Objects

  • Create a Logical Data Object
  • Perform:
    • Column Profiling
    • Multi Object Profiling
    • Mid-stream Profiling
    • Comparative Profiling
  • Create Mappings and work with DQ and Core Transformations 
  • Lab: Create and profile a Logical Data Object.

Data Standardization

  • Cleanse and transform data using Standardization Transformations.
  • Develop Standardization Mappings and Mapplets.
  • Perform parsing using methods such as:
    • Token Parser
    • Pattern Parser
    • NER/Probabilistic Parsing.
  • Lab: Build a Standardization Mapping and Mapplets using Standardization Transformations.

Matching

  • Become familiar with the concepts involved in Matching.
  • Learn how to best Group data.
    • Review the Match Performance Analysis Report.
  • Build Match Mappings.
    • Review the Match Cluster Analysis Report.
  • Lab: Create a Match Mapping to Group and Match the Data.

Run DQ in a Standalone environment

  • Schedule DQ Mappings to run in DQ Standalone using Informatica Scheduler.
  • Lab: Schedule Mappings to run using the Scheduler.

Post Class onDemand Modules 

Module 1: Parsing 

  • Perform Parsing using methods such as:
    • Token Parser
    • Pattern Parser
    • NER/Probabilistic parsing
  • Workshop: Build a Standardization and Parsing Mapping.

Module 2: Automatic Consolidation 

  • Use the Consolidation Transformation to automatically Consolidate matched records.
  • Be able to choose the most affective Consolidation Strategy for your data; simple, row based or advanced.
  • Lab: Build a Mapping to Match and Consolidate your data.

Module 3: Manual Exception and Consolidation Management 

  • Build and execute Mappings, using the Exception Transformation, to identify Exception and Duplicate records.
  • Lab: Build a Mapping that can be used to identify Exception data.
  • Lab: Build a Mapping that can be used to identify Duplicate data.

Module 4: Task and Workflow Management 

  • Build and execute Workflows to populate Informatica Analyst Task inboxes with Exception and Duplicate records.
  • Update Exception and Duplicate records in Informatica Analyst.
  • Lab: Build a Workflow to populate the Analyst Inbox with Exception Tasks.
  • Lab: Build a Workflow to populate the Analyst Inbox with Duplicate Record Tasks.

Module 5: Object Import/Export 

  • Import Projects using both Basic and Advanced methods.
  • Export Projects. 
  • Lab: Import a Project using the Basic method.
  • Lab: Import a Project using the Advanced Method.
  • Lab: Export a Project.
 
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onDemand | Data Quality | Self-Paced

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