Module 1: Course Introduction

• Course introduction and objectives
• Course agenda

Module 2: Data Quality Projects and Solution Architecture

• Data Engineering Quality Architecture and Runtime Environments
• Runtime Environments; Spark and Blaze
• Dimensions of Data Quality
• Data Quality Projects
• Reporting and Gating Architectures

Module 3: Data Quality Process Overview

• Data Quality Management
• Dimensions of Data Quality
• Developer and Analyst roles and tools
• Data Quality Processes
• Profiling, Standardization, Matching and Consolidation

Module 4: Analyst Collaboration and Reference Table Management

• Developer GUI
• Repository, Projects, and Folders
• Analyst Collaboration
• Scorecards
• Reference Table Management
• Lab: Project Collaboration
• Lab: Review the Content project
• Lab: Create Reference Tables

Module 5: Working in the Developer Tool

• Repository, Projects, and Folders
• Data Objects
• Mappings and Mapplets
• Mapplets and Rules
• Content Sets
• Transformations
• Preview Data and Mapping Execution
• Lab: Create a project in the Developer tool
• Lab: Connect to an Oracle table
• Lab: Import a Flat File data object
• Lab: Create a logical data object using core transformations and a prebuilt mapplet

Module 6: Profiling and Creating Mapplets and Rules

• Developer Profiling
• Profiling Rules
• Column Profiling options and Runtime Settings
• Profiling Results
• Mid-Stream Profiling
• Comparative Profiles
• The Analyst Tool
• Mapplets and Rules in Profiles and Scorecards
• Lab: Profile the new Logical Data Object
• Lab: Create a rule to measure the accuracy of data in the Company column
• Lab: Apply the rule to the profile in the Developer tool
• Lab: Update Scorecards in Informatica Analyst
• Lab: Measure the format of the Addr4 field

Module 7: Cleansing, Standardizing, and Enhancing Data

• Standardize, cleanse, and enhance data
• Standardization transformations
• Configure Standardization transformations
• Standardization mapplets
• Lab: Review the Profile_LDO_ALLCUSTOMERS object
• Lab: Build a mapping to cleanse, standardize, and enhance data
• Lab: Build a standardization mapplet to cleanse a numeric field

Module 8: Parsing Data

• Parsing Functions
• The Parser Transformation
• Token and Pattern based parsing
• Parsing Outputs
• Probabilistic Parsing
• Lab: Parse the contact using the Token Parser
• Lab: Use mid-stream profiling to update Reference Tables
• Lab: Parse the contact using Pattern based parsing
• Lab: Build a Mapplet to enhance and standardize the contact
• Lab: Complete the standardization mapping

Module 9: Matching Data

• The Data Quality Matching Process
• Grouping and grouping strategies
• The Key Generator Transformation
• Key creation strategies
• Mid-Stream Profiling for group analysis
• Matching Data
• Matching Strategies
• Match Outputs
• Lab: Perform Grouping and Classic Matching on Customer data

Module 10: Managing Exception and Duplicate Records

• The Exception Management Process
• The Exception Management Process running Natively
• The Exception Management Process running on Hadoop
• Data Quality Workflows
• The Exception Transformation
• Lab: Identify and gate bad records
• Lab: Use the Exception transformation to create and populate the bad record management table
• Lab: Use the Exception transformation to create and populate the duplicate record management table

Module 11: Managing and Deploying Workflows

• Workflows and workflow tasks
• Workflow Objects
• Building Workflows and managing Tasks
• Human Tasks and Steps
• Deploying Workflows and Applications
• Monitoring Application Execution
• Lab: Build the Workflow to run the Bad Record/Exception Mapping and assign Tasks to users in Informatica Analyst
• Lab: Deploy, Execute and Monitor the workflow
• Lab: Build a workflow to run the Duplicate Records Mapping and assign Tasks to users in Informatica Analyst
• Lab: Deploy, Execute and Monitor the workflow
• Lab: Verify that the Workflows ran correctly by checking that Tasks were generated in Informatica Analyst

Module 12: Deploying: Executing Mappings outside of the Developer tool

• Running DQ Mappings in PowerCenter
• Export Compatibility and Options
• Schedule mappings, profiles, and scorecards
• Monitor Application Execution
• Lab: Running DQ mappings in a standalone environment

Module 13: Importing and Exporting Project Objects

• Object Import Overview
• Basic Import Wizard
• Advanced Import Wizard
• Dependency Resolution Options
• Project Export
• Lab: Import a new project
• Lab: Import a project using the Advanced Method
• Lab: Export a project

Module 14: Troubleshooting

• Sample DQ Errors
• Troubleshooting Tips
• Administrator Logs and Monitor Jobs
• Troubleshooting System and Mapping Issues
• Workflow configuration issues
• Lab (optional): Troubleshooting issues