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Cloud Data Quality

onDemand | IDMC Data Quality | Self-Paced | July 2023

Course Overview

Learn the fundamentals of Informatica Intelligent 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 July 2023 version.


Important note regarding this onDemand course: Many students will need their personal laptop/PC to set up the lab environment and perform lab exercises. Laptops provided by your employer may not allow downloading external tools. To execute labs in full, students need to download and install the following tool. See Agenda below to view more details. If you are unable to complete this training due to these requirements, please consider Live Training.

  • Informatica Cloud Secure Agent

Objectives

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

  • Describe Informatica Intelligent Data Management Cloud Architecture
  • Download and install the Secure Agent
  • 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 define rules
  • Describe the Scorecarding process
  • 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
To execute all labs within this course, students should download and install the following:
    • Informatica Cloud Secure Agent: 
      The Informatica Cloud Secure Agent is required to create connections to connect to various data sources with IDMC.

Steps required to set up the lab environment are provided in the Getting Started lab guides for the course. Students must perform the Getting Started labs before executing the course lab exercises.



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
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 edit 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
  • Lab: Apply rules to a profile and review
Module 7: Scorecards
  • Scorecard Overview
  • Update a Profile and define Rule Occurrences
  • Review Scorecards
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 and standardize data
Module 10: The Parse Asset
  • Introduction to the Parse Asset
  • Parsing data
  • Lab: Configure the Parse asset in prebuilt mode
  • Lab: Configure the Parse asset using a regular expression
  • Lab: Create a Mapping to join 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 a 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
Module 13: Exception Management
  • The Exception Management Process
  • Configure an Exception Task
  • Lab: Export Project Assets and Delete the Contents and Folder
 

Back to Course Overview

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