TDWI Data Integration Principles and Practices: Creating Information Unity from Data Disparity Training

Today’s business managers depend heavily on data analysis and decision-speed information, raising the stakes for data integration. At the same time, the work of integrating data has become increasingly complex. The simple processes of extract, transform, and load (ETL) integration for structured enterprise data no longer meet the need. Unstructured data, big data, departmental data, end-user data, and external data all challenge old models for data integration. Meeting modern data integration challenges calls for data integration strategy and architecture.

Get ready to build reliable and adaptable data integration systems and to make the most of recent advances in data integration technologies by following the path of strategy first, architecture next, and then integration systems and technology.

BI, MDM, and data warehousing program and project managers; data integration architects, designers, and developers; data and technology architects.

The role, purpose, and issues of data integration strategy
How to fit unstructured data into integration strategy, architecture, and systems
How to use integration architecture and patterns to handle large volume data challenges
How to apply architecture and patterns for enterprise, departmental, and local data
How to select, mix-and-match, and apply several data integration methods including ETL, federated, service-oriented, and virtualized
Techniques to collect and manage data integration requirements
Tips and techniques for success throughout the data integration lifecycle—strategy, architecture, systems development, and operations

Module 1 - Data Integration Strategy

What Is Data Integration Strategy?
- Strategy Defined
- Data Integration Strategy Defined
Elements of Data Integration Strategy
- Data Consumers and Data Sources
- Data Integration Processes
- Data Stores
- Access and Navigation
- Control Objectives
- The Big Picture
Creating Data Integration Strategy
- Goals and States
- Analysis
Applied Data Integration Strategy
- Planning and Projects
- Data Governance

Module 2 - Data Integration Architecture

What Is Data Integration Architecture?
- Architecture Defined
- Data Integration Architecture Defined
Integration Patterns
- Methods of Information Sharing
- Shared Databases and Replication
- Gateways and Portals
- Entity Aggregation
- Data Services
- Data Services & Messaging
- Publish and Subscribe, File Transfer
- Virtualization
- Virtualization and Federation
Architecture Concepts and Components
- Constructs in Data Integration Architecture
- Reference Architecture

Module 3 - Creating Data Integration Architecture

Data Sources
- Source Systems
- Structural Variations
- Source Data Locations and Technologies
Data Flow
- Data Acquisition
- TDWI Data Integration Principles and Practices: Creating Information Unity from
- Data Disparity
- Data Integration Functions
- Data Stores
- Data Integration Methods
- Data Delivery
- Metadata Flow
Data Consumers
- People
- Applications
- Requirements
Architecture Design and Definition
- Define the End Points
- Structure the Middle
- Define Data Access
- Architecture Validation
Data Integration Approaches
- Many Types and Levels of Integration
- ETL/ELT
- Streaming
- o Virtualization
- Federation
- Big Data Integration
- Self-Service Integration
- Cloud Integration
- Hybrid Integration

Module 4 - Using Data Integration Architecture

Systems and Data Integration Architecture
- Existing Systems
- New Systems
Development and Data Integration Architecture
- Requirements Analysis
- Design and Construction
- Testing
Management and Data Integration Architecture
- Management and Governance
- Change Management
- Support and Operations
Next Steps
- What Will You Do Next?

Upcoming Trainings

Join our public courses in our Istanbul, London and Ankara facilities. Private class trainings will be organized at the location of your preference, according to your schedule.

05 June 2023