EncartaLabs

Informatica Data Quality

The Informatica Data Quality - Administration training course provides delegates with the fundamental knowledge and skills to maintain an Informatica Data Quality environment. Focused on teaching individuals how to use the Informatica Administrator tool to maintain the required environment.

In Informatica Data Quality Management for Developer training course delegates will 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.

In Informatica Data Quality - Advanced Techniques training course delegates will learn to leverage advanced techniques when utilizing Developer to profile, cleanse, standardize, de-duplicate and consolidate data in an enterprise. Focused on creating and applying custom built Classifier and Probabilistic Models, utilizing advanced Parsing and Matching methods, refining Human Tasks and Workflows, automatically Associating and Consolidating matched records, applying Parameters in mappings and more.

By attending Informatica Data Quality - Administration workshop, delegates will learn to:

  • Describe core administration tasks and tools
  • Configure the Informatica Administrator tool
  • Create and configure necessary services
  • Manage Informatica security
  • Audit security access and privileges
  • Perform ongoing maintenance
  • Stop or recycle a service
  • Review domain logs

By attending Informatica Data Quality Management for Developer workshop, delegates will learn 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

By attending Informatica Data Quality - Advanced Techniques workshop, delegates will learn to:

  • Perform Join Profiling.
  • Create and apply Classification Models.
  • Parse data using advanced techniques.
  • Create and apply Probabilistic Models.
  • Apply sophisticated Grouping and Matching techniques.
  • Automatically Associate and Consolidate matched records.
  • Refine Exception and Duplicate Record Workflows used to populate Analyst inboxes.
  • Design, Implement and Test processes to manage updated exception/duplicate records.
  • Appropriate DQ Parameters.
  • Examine Performance considerations.
  • Review CRM and Dashboard & Reporting Templates.
  • Optionally/Time allowing:
    • Leverage Web Services to apply DQ mappings in Excel.
    • Perform Identity Matching.
      • Use the Universal ID store to match against master data.

For Informatica Data Quality - Administration

  • Knowledge in SQL
  • General relational database (Oracle)
  • Microsoft Windows GUI
  • Windows Command line
  • General programming

For Informatica Data Quality - Advanced Techniques

  • Attending a training on Informatica Data Quality Management for Developer course or having equivalent knowledge / experience

For Informatica Data Quality - Administration

  • Administrators

For Informatica Data Quality Management for Developer

  • Developers

For Informatica Data Quality - Advanced Techniques

  • Developers

COURSE AGENDA

Informatica Data Quality - Administration
(Duration : 3 Days)

1

Data Quality Introduction

  • Informatica Data Quality technical architecture
  • Informatica domain, nodes, and application services
  • Overview of the PowerCenter clients.
2

Best Practices

  • Configuring an environment
  • Recognized Naming Conventions
3

Installing Informatica

  • Installing Informatica
  • Reviewing installation logs
  • Using command-line utilities
4

Using the Informatica Administrator tool

  • Administration tool layout and navigation
  • Views in the Manage Tab
  • Services and Nodes
  • License key types
5

Configuring the Model Repository Service

  • Adding and moving a Model Repository Service
  • Auditing
6

Configuring the Data Integration Service

  • Creating connections
  • Home Directory location
  • Stopping and starting a service
7

Users and Groups

  • Creating user and group accounts
  • Creating accounts using scripts
  • Adding users to groups
  • Importing LDAP user accounts and groups
8

Privileges and Roles

  • Configuring roles and privileges
  • Assigning privileges and roles
  • Domain folders and services
9

Permissions

  • Assigning permissions to domains and domain objects
  • Verifying permissions
10

Configuring the Analyst Service

  • Creating connections
  • Analyst Services
  • Analyst Service folders
  • Analyst Service permissions and log
  • The Analyst command line
11

Configuring the Content Management Service

  • Connecting the Developer client to the domain
  • Data Integration Service defaults
  • MRS projects and set permissions
  • Project folders and set permissions
  • Simple mapping
  • Deploying mappings
12

Configuring the Data Director Service

  • The Scheduler Service
  • Setting a schedule
13

Domain Administration and Management

  • Create and configure a Content Management Service (CMS)
  • Install OOTB content
  • Install Identity (IMO) content
  • Install Address Doctor (AD) content
  • Install the Classifier model
14

Monitoring and Troubleshooting

  • The Monitoring view
  • Configuring Log Management properties
  • Filtering logs
  • Auditing user activity
Informatica Data Quality Management for Developer
(Duration : 4 Days)

1

Introduction

  • Course topics
  • Modules and content
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
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
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
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
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.
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.
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
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
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
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
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.
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.
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
Informatica Data Quality - Advanced Techniques
(Duration : 4 Days)

1

Introduction

  • Course Introduction, Agenda and Overview
2

Developer Review & Join Profiling

  • A quick review of Informatica Developer
  • Use Enterprise Discovery to create Join Profiles.
  • Lab: Perform Join Profiling using an Enterprise Discovery Profile
3

Standardizing and Classifying Data

  • Review Standardization Techniques
  • Build, refine and apply a Classifier Model
  • Labs: Create, refine and apply Classifier Model
4

Advanced Parsing Techniques

  • What is Probabilistic Labeling and Parsing?
  • Build, refine and apply a Probabilistic Model.
  • Additional Parsing Techniques:
    • Build regular expressions.
  • Labs: Build, refine and apply a Probabilistic Model
  • Lab: Review an example of Advanced Parsing
  • Lab: Generate and test Regular Expressions
5

Grouping & Matching Data

  • Additional Grouping Techniques
    • Using Composite keys
  • Advanced Matching Techniques
    • Matched pairs outputs.
    • Working with Match Mapplets.
    • Manipulating the matched data using the Driver ID
    • Perform Dual Matching
  • Lab: Create a Match mapping using Matched Pairs
  • Lab: Create and update a Match Mapplet
  • Lab: Manipulating Matched Data using the Driver ID
  • Lab: Perform Dual Matching using a Master Dataset.
6

Automatically Associate and Consolidate Matched Data

  • Overview of the Consolidation Process
  • Use the Consolidation Transformation to consolidate matched data.
  • Use the Association Transformation to link matched data ahead of Consolidation.
  • Lab: Automatically Consolidate matched data.
  • Lab: Perform multi-criteria Matching, Association and Consolidation.
7

Task and Workflow Management

  • Additional Task and Workflow functionality:
    • Permission settings for data access and editing
    • Notifications including Human Task Notification Variables
    • Setting Timeouts
    • Reviewing Tasks
    • Configuring Workflow Recovery
  • Lab: Update the Exception Workflow
  • Lab: Review the Consolidation Workflow
8

Processing Updated Exception and Cluster Data

  • How to process updated exception records
  • How to process consolidated records
  • Fields of Interest
  • Lab: Create a mapping to process updated exception data
  • Lab: Create a mapping to process consolidated data
  • Lab: Update and deploy Exception and Cluster Workflows
9

Analyst Tasks

  • Update exception and duplicate records in Informatica Analyst
  • Lab: Update records and push the Tasks through the Exception Process
  • Lab: Update records and push the Tasks through the Consolidation Process
10

Parameterization

  • Explain the difference between System and User defined parameters
  • Use Parameters in Data Quality mappings.
  • Lab: Create a parameterized mapping
  • Lab: Build and deploy an Application
  • Lab: Create and execute parameter files
11

Performance tips and tricks

  • General Installation and Memory Information
  • DQ Component Configuration
    • Service Settings
  • DQ Transformations
    • Configuration Settings
12

Identity Matching

  • Match Data using Identity Matching
    • Use UID to match data against a Master Data Store
  • Lab: Use Identity to match customer data
  • Mixed Matching Workshop
  • Lab: Universal ID, Create and load the Persistent Data Store
  • Lab: Match and update new records to the Store.

Encarta Labs Advantage

  • One Stop Corporate Training Solution Providers for over 4,000 Modules on a variety of subjects
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  • Get jumpstarted from newbie to production ready in a matter of few days
  • Trained more than 50,000 Corporate executives across the Globe
  • All our trainings are conducted in workshop mode with more focus on hands-on sessions

View our other course offerings by visiting http://encartalabs.com/course-catalogue-all.php

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