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Data Quality: Data Quality Management for the Developer 

Instructor-led | Data Quality | 4 Days

Data Quality: Data Quality Management for the Developer

Course Overview

Gain the skills and knowledge necessary to implement and automate a data quality assurance process with the Informatica Data Quality platform. In addition, learn how to cleanse, standardize, and enhance data, students will learn to test and troubleshoot their Data Quality solutions.

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Objectives

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

  • Describe the Data Quality Management Process
  • Illustrate the Data Quality Architecture
  • Differentiate between the Analyst and Developer Roles and Tools.
  • Navigate the Developer Tool 
  • Collaborate on projects 
  • Perform Column, Rule, Multi object, Comparative and Mid-Stream Profiling
  • Manage Reference Tables
  • Develop standardization, cleansing and parsing Mappings and Mapplets
  • Identify duplicate records using Classic Data Matching
  • Create and execute Workflows to populate user inboxes with Exception and Duplicate record tasks
  • Describe the deployment options available when executing Mappings outside of Informatica Developer
  • Troubleshoot issues that may appear during development

Target Audience

  • Developers

Agenda

Module 1: Course Introduction

  • Course topics
  • Modules and content

Module 2: Data Quality Process Overview

  • Data Quality Management Process Cycle
  • Dimensions of Data Quality 
  • Data Quality Processes 
  • Developer and Analyst Roles and Tools
  • Data Quality Architecture

Module 3: Data Quality Projects and Solutions

  • Customer Data Quality Use Cases
  • Projects that benefit from cleansed and standardized data
  • Data Quality and typical DI/DQ projects
  • Reporting, Gating and Cleansing projects
  • Solution Architecture for Projects with Data Quality

Module 4: Project Collaboration and Reference Table Management

  • Developer Interface
  • Understanding Analyst projects, Data Objects, Profiles, Rules, Scorecards, Comments and Tags
  • Reference Tables and the Data Quality Process
  • Creating Reference Tables 
  • Lab: Review a project created by an Analyst
  • Lab: Build Reference Tables

Module 5: Working in the Developer Tool

  • Tasks in the Developer Tool
  • Working with Physical and Logical Data Objects
  • Connecting to a table
  • Importing and flat file
  • Creating logical data objects
  • Developer Transformations
  • Mappings and mapplets
  • Content sets and their uses
  • Developer Tips and Tricks
  • Lab: Create a project and assign permissions
  • Lab: Create a connection to an Oracle table and import a flat file
  • Lab: Build a Logical Data Object

Module 6: Profiling, Mapplets and Rules

  • Column Profiling 
  • Mapplets and Scorecards
  • Profiling techniques to debug and improve development 
  • Updating Scorecards with Rules
  • Lab: Create a Rule to measure the Accuracy of data in a field.
  • Lab: Using Informatica Analyst, apply the rule to a Scorecard and review the results.

Module 7: Standardizing, Cleansing and Enhancing Data

  • Standardizing, cleansing and enhancing data.
  • Mappings that cleanse, standardize and enhance data
  • Developing standardization mapplets
  • Configuring standardization transformations
  • Lab: Build a Standardization Mapping and Mapplets using Standardization Transformations.

Module 8: Parsing Data

  • The Parsing Process 
  • Parsing techniques 
  • Key parsing transformations
  • Lab: Perform Parsing using a variety of Parsing Transformations
  • Lab: Complete a Standardization Mapping

Module 9: Matching Data

  • Match Data definition
  • The DQ matching process
  • The different stages of Matching 
  • Grouping and its effect on matching
  • Grouping methods
  • Grouping results and refining a grouping strategy 
  • Match algorithms
  • Lab: Build and fine tune a grouping and matching mapping

Module 10: Manual Exception and Consolidation Management

  • Exception and Duplicate record management 
  • Exception Management Process.
  • Populating tables with exception and duplicate record tasks
  • 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 11: Building, Managing and Deploying Workflows

  • Workflows and Workflow Tasks
  • Human Tasks and Steps
  • Identifying exception and duplicate records
  • Deploying and executing workflows
  • Verifying Tasks 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 12: Deploying: Executing Mappings outside of the Developer tool

  • Deployment options.
  • Mappings as applications
  • Scheduling mappings, profiles and Scorecards 
  • Lab: Schedule Mappings to run using Informatica Scheduler.

Module 13: Importing and Exporting Project Objects

  • Export/import project use cases
  • Basic and Advanced Import options
  • Exporting a project 
  • Lab: Import a Project using the Basic method.
  • Lab: Import a Project using the Advanced Method.
  • Lab: Export a Project.

Module 14: Troubleshooting

  • Common Developer errors
  • Common Mapping and Transformation configuration issues
  • Common Workflow configuration errors
  • Tips for working with the Developer tool 
  • Lab: Optional. Troubleshoot Mapping configuration issues
 
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Instructor-led | Data Quality | 4 Days

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