How This Course Is Organised

This course teaches techniques and skills to build data integration systems that can meet today’s needs and evolve to meet demands of the future. Starting with the right requirements, using the right technologies, and designing for adaptability are central themes throughout the course.

Analysis techniques to capture data integration requirements, including those for source data, data consolidation, data quality, data granularity, data currency, and historical data
How the alphabet soup of integration technologies—ETL, EII, EAI, MDM, and CDI—fits into overall data integration architecture.

Design techniques for the mainstream of data integration, including source-to-target mapping, source data capture, data transformation and cleansing, and database loading
Techniques to enrich the data integration design with processes for automated scheduling, execution monitoring, metadata capture, restart and recovery, and more
Tips to design for the complex issues of data integration, including detecting data changes, identifying data quality defects, managing complex schedule dependencies, meeting real-time data demands, and more


Continuing Professional Education Credits: 5


Audience:
Business intelligence and data warehousing architects
Data integration process designers and developers
Data Science and Data Engineering Architects
Business intelligence and data warehousing program and project managers


Objectives:

Understand Data Integration Concepts
Understand Data Engineering Concepts
Understand The Challenges of Data Integration
Understand Data Integration Platforms
Understand Data Integration Architectures
Understand Data Integration Technologies
Understand Requirements Analysis for Data Integration
Understand Data Integration Functional Design
Understand Data Integration Technical Design
Understand Data Quality
Understand Construction, Deployment, and Operation of Data Integration Projects



Course Outline

Understanding ETL and Data Integration Platforms
ETL Framework and Components
The Evolution of ETL
Code Generators versus Engines
Database-Centric ETL
 
Data Integration Platforms
ETL versus EAI
Understanding the Data Integration Tool Market
Functional Capabilities of Data Integration Tools

Data Integration Concepts
· The Need for Data Integration
o Why We Integrate Data
o A Projects Perspective
· The Challenges of Data Integration
o Understanding Data Sources
o Choosing the Right Data Sources
o Data Quality
o Data Availability
· Data Integration Architectures
o Integration Hub
o Integration Bus
o Integration Services
· Data Integration Projects
o Kinds of Projects
o Project Activities
· Data Integration Technologies
o Extract-Transform-Load (ETL)
o Enterprise Information Integration (EII)
o Enterprise Application Integration (EAI)
o Master Data Management (MDM) and More

Requirements Analysis for Data Integration
· Integration Requirements Concepts
o Overview
· Source Data Requirements
o An Overview
o Kinds of Data Sources
o Evaluating Data Sources
o Source Data Analysis and Profiling
o Choosing Data Sources
· Data Unification Requirements
o Subject Orientation
o Entity Consolidation
o Identity Consolidation
o Relationship Consolidation
o Attributes and Values Consolidation
· Data Aggregation and Summary Requirements
o Levels of Detail
· Data Quality Requirements
o Data Correctness
o Timeliness
o Data Integrity
· Data Capture Requirements
o Frequency of Data Capture
o Collecting Historical Data
o Level of Detail
· Audit, Balance and Control Requirements
o ABC’s of Data Integration
· Metadata Capture Requirements
o Data About Integration Processes
· Service Level Requirements
o Meeting Expectations

Data Integration Functional Design
· Functional Design Concepts
o Overview
· Source/Target Mapping
o Mapping Techniques
o Entity Mapping
o Data Store Mapping
o Data Element Mapping
o The Full Set of Data Elements
· Data Capture Design and Specification
o An Overview
o Kinds of Data
o Push vs. Pull
o All Data vs. Changed Data
o Changed Data Detection
o Data Extraction
o Data Replication
o Transaction Logging
o Messaging
o Storing Captured Data
· Data Transformation Design and Specification
o Kinds of Transformations
o Data Selection and Filtering
o Conversion and Translation
o Derivation and Summarization
o Identifying Transformations
o Specifying Transformation Logic
· Data Cleansing Design and Specification
o Detecting Data Quality Defects
o Repairing Data Quality Defects
o Quality Metadata and the ABCs of Cleansing
· Identity and Key Management
o De-Duplication
o Surrogate Key Assignment
· Design for Integrated Data Delivery
o Choosing the Right Delivery System
· Data Integration Process Design
o Requirements – Driven Processing

 
Data Integration Technical Design
· Technical Design Concepts
o Overview
o Comprehensive Processing Design
· Data Flow Design
o Moving Data through the Integration Pipeline
o Data Capture and Data Staging
o Transformation Processes
o Transformation Sequence and Dependencies
o End-to-End Data Flow
· Work Flow Design
o Extending Data Flow with Events

· Service Level Design
o Performance and More
· Process Management Design
o Metadata Capture and Event Logging
o Balancing and Audits
o Error and Exception Handling
o Communication

 
Construction, Deployment, and Operation
· Construction, Deployment, & Operations Concepts
o Overview
· Building Data Integration Systems
o Tools and Technology
o Standards, Frameworks, Templates, and Reuse
o System Management and Data Integration
o System Testing and Data Integration
· Implementing Data Integration Systems
o One-Time Data Consolidation
o Ongoing Data Consolidation
· Operating Data Integration Systems
o Integration System Operations
o Customer and User Support
o Change Management

     

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