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Cloud Data Quality:
Cleansing and Enhancing Data

onDemand | IDMC Data Quality | Self-Paced | February 2025 Release

Course Overview

Enhance your understanding of Informatica Cloud Data Quality through in-depth analysis of its cleansing and enhancement features. Learn how to configure Data Quality Assets and effectively implement them in mappings or mapplets within Cloud Data Integration. This course equips you with the necessary skills to design and construct a Data Quality Cloud process, tailored for use in Data Migration, Data Integration, or Data Quality Projects.
Access the latest tools and methodologies with this February 2025 Release.

Objectives

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

  • Describe what Cloud Data Quality is and how it can be used to optimize your data
  • Use Cloud Administrator to define connections
  • Create Mappings using Cloud Mapping Designer
  • Collaborate with team members
  • Identify and label data using a Labeler asset
  • Configure the Cleanse asset to standardize, cleanse, and enhance data
  • Configure the Parse asset to extract or separate data
  • Use Deduplicate functionality to identify and consolidate duplicate records
  • Verify and enhance addresses using the Verify asset
  • Review available Content Bundles

Target Audience

  • Developer
  • Business User

Recommended Prerequisite

Agenda
Module 0: Course Introduction
  • Course Introduction
  • Lab: Create the Cloud User Account
  • Lab: Install the IDMC Secure Agent
  • Lab: Import the Training Content
Module 1: Informatica Intelligent Data Management Cloud Overview
  • Introduction to Informatica Intelligent Data Management Cloud (IDMC)
  • IDMC Terminology
  • IDMC Architecture
  • IDMC Services
  • Runtime Environments
  • Connections
  • The Administrator Service
  • Lab: Define a Flat File Connection
  • Lab: Create a Training Folder
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
  • Cloud Data Quality Services and Assets
Module 3: Team Collaboration
  • Profiling Data
  • What are Dictionaries and why are they used?
  • Use Rule Specifications to Cleanse and Enhance Data
  • Lab: Create a Profile and Review Team Assets
Module 4: Cloud Mapping Designer
  • Cloud Mapping Designer Overview
  • Mapping Designer Terminologies
  • Mappings and Mapplets
  • Common Transformations
Module 5: The Labeler Asset
  • Standardization Overview
  • Introduction to the Labeler Asset
  • Configure a Labeler Asset in Token Labeler Mode
  • Configure a Labeler Asset in Character Labeler Mode
  • Lab: Create a Labeler to Mask Non-Numeric Data
Module 6: The Cleanse Asset
  • Introduction to the Cleanse Asset
  • Cleansing, Standardizing, and Enhancing Data
  • Building a Mapping to Cleanse and Transform Data
  • Lab: Configuring a Mapplet to Remove Noise from a Numeric Field
  • Lab: Configuring a Multi Instance Cleanse Asset to Cleanse and Standardize the Address Fields
  • Lab: Configuring a Mapplet to Derive a Master Contact Name
  • Lab: Configuring a Mapping to Cleanse, Standardize, and Enrich Data
Module 7: The Parse Asset
  • Introduction to the Parse Asset
  • Parsing Data
  • Lab: Configure a Parse Asset using Pre-built Mode
  • Lab: Configure a Parse Asset using a Regular Expression
  • Lab: Build a Mapping to transform and merge two Datasets
  • Lab: Standardize the Data
Module 8: 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 9: The Verifier Asset
  • Introduction to the Verifier Asset
  • Verify Address Data
Module 10: Data Quality Bundles
  • Introduction to Data Quality Bundles
  • Lab: Work with Data Quality Bundles
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