This three-day Test Data Management training is applicable for version 10.2. Learn to discover and mask sensitive data on non-production environments and gain the skills required to define data domains to discover sensitive data based on data and metadata patterns. Use the out-of-the box masking techniques to mask data while retaining the original characteristics and define custom masking rules to handle complex data masking requirements.

Target Audience

  • Developers

Prerequisites

  • None

Agenda

Module 1: Introduction to Test Data Management

  • Sensitive Data
  • Government regulations
  • Persistent data masking use cases
  • Dynamic Data Masking use cases
  • TDM Data masking and terminology
  • Architecture and deployment
  • TDM user interface
  • TDM Project and permissions
  • Lab: Create connections
  • Lab: Import data sources to a project

Module 2: Data Discovery

  • Data discovery overview
  • Profiling types
  • Discover constraints
  • Discover entities
  • Discover data domains
  • Table classification
  • Lab: Discover constraints
  • Lab: Discover entities
  • Lab: Discover sensitive data
  • Lab: Discover data domains and profile

Module 3: Data Subset

  • Define entities
  • Configure major and minor relationships
  • Create functional data subsets
  • Lab: Create subset with minor relationships
  • Lab: Create subset with major relationships
  • Lab: Create a subset with major and minor relationships

Module 4: Data Masking

  • Data Masking process and components
  • Data domains and profiling
  • Data masking rules
  • Table classification and cascade masked values
  • Override masking parameters
  • Configure out of the box masking rules
  • Lab: Credit card masking
  • Lab: Key masking
  • Lab: Nullification and shuffle masking
  • Lab: Random and Key masking
  • Lab: Encryption masking
  • Lab: Expression masking
  • Lab: SSN masking
  • Lab: Substitution masking
  • Lab: Conditional substitution masking
  • Lab: Email masking
  • Lab: Advanced masking

Module 5: Test Data Generation

  • Describe data generation use cases
  • Define data generation rules
  • Generate synthetic data
  • Lab: Generate test data

Module 6: Test Data Warehouse

  • Describe Test data warehouse
  • Define test data warehouse components
  • Define datasets
  • Restore datasets
  • Lab: Define dataset
  • Lab: Restore dataset

Module 7: Dynamic Data Masking

  • Dynamic Data Masking Terminology and
  • Architecture and Components
  • Dynamic Data Masking Integration options and use cases
  • Protect a relational system
  • Connection rules
  • Match rule sets
  • Lab: Getting started with DDM
  • Lab: Deny database access within a time
  • Lab: Protect CUSTOMER and CUSTOMER_SERVICE tables
  • Lab: Disable, export and delete rules
  • Lab: Replace an incoming SQL Query
  • Lab: Edit an incoming SQL Query
Summary
Review Date
Reviewed Item
Informatica ILM Training
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