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Cloud Data Quality

onDemand | Data Quality / iPaas | Self-Paced | R36

Cloud Data Quality

Course Overview

This course is applicable to version R36. Gain the skills to design and build Data Quality Cloud Processes for use in Data Migration, Data Integration, or Data Quality Projects. Learn the fundamentals of Informatica Intelligent Cloud Data Quality, including the Cloud Architecture and GUI, Data Quality Assets/Transformations, and Cloud Mapping Designer.

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Objectives

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

  • Describe Informatica Cloud Architecture
  • Install the secure agent
  • Describe Cloud Data Quality and how it is used
  • Define Connections using Cloud Administrator
  • Create Mappings
  • Profile data to identify anomalies
  • Create Dictionaries to store reference data for verification and standardization
  • Build rules to identify bad data using Rule Specifications
  • Configure the Cleanse Asset to cleanse bad data identified during profiling
  • Configure the Parse Transformation/Asset to parse data
  • Verify and enhance Addresses
  • Identify and consolidate duplicate records

Target Audience

  • Developer
  • End User

Prerequisites

  • None

Agenda

Module 1: Informatica Cloud Overview

  • Informatica Cloud Services (IICS) as an iPaaS solution
  • Informatica Cloud Terminologies
  • Informatica Cloud Architecture
  • Informatica Cloud Services
  • The Administrator Service
  • Runtime Environments
  • Connections
  • Lab: Defining Connections
  • Lab: Using Cloud Help

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 the Data Quality functions, inputs and outputs
  • Describe the Cloud Data Quality Services and Assets that are available

Module 3: Cloud Mapping Designer

  • Cloud Mapping Designer Overview
  • Mapping Designer Terminologies
  • Common Transformations
  • Lab: Create your training folder
  • Lab: Create and run a mapping to load data into a table

Module 4: Cloud Data Profiling

  • Profile Data
  • Review Profiling Results
  • Identify Anomalies
  • Lab: Profiling Data

Module 5:  Dictionaries

  • What are Dictionaries and why are they used?
  • Creating Dictionaries
  • Lab: Create a Dictionary using Value Frequencies from a profile
  • Lab: Create a Dictionary to standardize data
  • Lab: Copy and edit a 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
  • Lab: Create a Rule Specification with multiple rules
  • Lab: Apply Rule Specifications to a profile and review
  • Lab: Create a Rule Specifications to standardize data

Module 7: 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
  • Lab: Configure a mapplet to enrich data
  • Lab: Configure a mapping to cleanse, standardize and enrich data

Module 8: The Parse Asset

  • Introduction to the Parse Asset
  • Use the Parse Asset to parse data
  • Lab: Configure the Parse Asset using PreBuilt Mode
  • Lab: Configure the Parse Asset using a Regular Expression
  • Lab: Update the Load Mapping to include both datasets
  • Lab: Reprofile and standardize the data

Module 9: The Deduplicate Asset

  • Introduction to the Deduplicate Asset
  • Identify matching or related records
  • Configure the Deduplicate Asset to consolidate matched data
  • Lab: Configure the Deduplicate Asset to identify duplicate records
  • Lab: Create a mapping to identify duplicate records
  • Lab: Update the deduplicate asset to consolidate records

Module 10: The Verify Asset

  • Introduction to the Verify Asset
  • Verify Address Data
  • Lab: Configure the Verify Asset
  • Lab: Create a mapping to verify data
  • Lab: Deleting Objects
 
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onDemand | Data Quality / iPaas | Self-Paced | Version R36

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