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Cloud Data Quality

Instructor Led | IDMC Data Quality | 3 Days | April 2024 Release

From January 2025, this course will split into two courses (view new Training Path here):

1. Cloud Data Quality: Profiling and Monitoring
2. Cloud Data Quality: Cleansing and Enhancing Data


Course Overview

Learn the fundamentals of Informatica Cloud Data Quality including the Intelligent Data Management Cloud (IDMC) Architecture and GUI, Data Quality Assets/Transformations and Cloud Mapping Designer. This course enables you to design and build your Data Quality Cloud Process for use in Data Migration, Data Integration or Data Quality Projects.
This course is applicable to April 2024 Release.

Objectives

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

  • Describe Informatica Intelligent Data Management Cloud Architecture
  • Describe what Cloud Data Quality is and how it can be used
  • Use Cloud Administrator to define connections
  • Create Mappings using Cloud Mapping Designer
  • Profile data to identify anomalies
  • Create Dictionaries to hold reference data for verification and standardization routines
  • Use Rule Specifications to build rules
  • Identify and label data in fields using a Labeler asset
  • Configure the Cleanse asset to standardize and cleanse data
  • Configure the Parse asset to parse data
  • Use the Deduplicate functionality to identify and consolidate duplicate records
  • Verify and enhance Addresses using the Verify asset
  • Identify Exception Records and download them for manual correction

Target Audience

  • Developer
  • Business User

Prerequisites

  • None
Agenda
Module 1: Informatica Intelligent Data Management Cloud Overview
  • Introduction to Informatica Intelligent Data Management Cloud (IDMC)
  • Informatica Intelligent Data Management Cloud Terminology
  • Informatica Intelligent Data Management Cloud Architecture
  • Informatica Intelligent Data Management Cloud Services
  • Runtime Environments
  • Connections
  • The Administrator Service
  • Lab: Defining Connections
Module 2: Cloud Data Quality Overview
  • What is Data Quality?
  • Discuss the Data Quality Management Process Cycle
  • List and explain the Dimensions of Data Quality
  • Describe Data Quality functions, inputs, and outputs
  • Cloud Data Quality Services and Assets
Module 3: Cloud Mapping Designer
  • Cloud Mapping Designer Overview
  • Mapping Designer Terminologies
  • Mappings and Mapplets
  • Common Transformations
  • Lab: Create your training folder
  • Lab: Create and run a mapping to load data into a SQL table
Module 4: Cloud Data Profiling
  • Profile Data
  • Review Profiling Results and identify anomalies
  • Profile Features
  • Lab: Profiling Data
  • Lab: Profiling Insights
Module 5: Dictionaries
  • What are Dictionaries and why are they used?
  • Creating Dictionaries
  • Lab: Create a Dictionary to standardize data
  • Lab: Copy and update an existing Dictionary to validate data
  • Lab: Create a Dictionary to enhance data
Module 6: Rule Specifications
  • Introduction to Rule Specifications
  • Building Rule Specifications
  • Lab: Create a Rule Specification to validate the Company field
  • Lab: Create a Rule Specification with multiple rules
Module 7: Scorecards
  • Scorecard Overview
  • Update a Profile and define Rule Occurrences
  • Review Scorecards
  • Lab: Apply Rules to a Profile and Review
  • Lab: Create a Scorecard
Module 8: The Labeler Asset
  • Standardization Overview
  • Introduction to the Labeler Asset
  • Configuring a Labeler Asset in Token Labeler mode
  • Configuring a Labeler Asset in Character Labeler mode
  • Lab: Create a Labeler to mask nonnumeric data
Module 9: The Cleanse Asset
  • Introduction to the Cleanse Asset
  • Cleanse, standardize and enhance data
  • Build a mapping to cleanse and transform data
  • Lab: Create a mapplet to cleanse and standardize the Company name
  • Lab: Configure a mapplet to derive a Master Contact name
  • Lab: Configure a mapplet to remove noise from a numeric field
  • Lab: Configure a mapping to cleanse, standardize and enrich data
Module 10: The Parse Asset
  • Introduction to the Parse Asset
  • Parsing data
  • Lab: Configure a Parse Asset using Prebuilt Mode
  • Lab: Configure a Parse Asset using a Regular Expression
  • Lab: Update the Load Mapping to include both datasets
  • Lab: Reprofile and standardize the data
Module 11: The Deduplicate Asset
  • Introduction to the Deduplicate Asset
  • Matching Theory
  • Identify matching or related records
  • Configure the Deduplicate Asset to consolidate matched data
  • Lab: Configure the Deduplicate Asset to identify duplicate or related records
  • Lab: Create a mapping to identify duplicate records
  • Lab: Update the deduplicate asset to consolidate matched records
Module 12: The Verifier Asset
  • Introduction to the Verifier Asset
  • Verify Address Data
  • Lab: Configure a Verifier Asset to verify and correct US master records
  • Lab: Create a mapping to verify data US master records
Module 13: Exception Management
  • The Exception Management Process
  • Configure an Exception Task
  • Lab: Identify Exception Records
  • Lab: Export Project Assets and Delete the Contents and Folder
Enroll Now

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