Beyond the Data Warehouse: Architectural Options for Data Integration

Published: 5 September 2007

Data warehousing used to be IT's weapon of choice for corralling the "islands of data" and bringing order to the decentralized information chaos. Shifting business priorities, outsourcing’s popularity, and new technologies have changed that...

This course is available at the TDWI World Conference in Orlando. For more information, and to register, visit www.tdwi.org/orlando2007.

M4
Data Integration
Prerequisite: An understanding of fundamental technology architectures

Evan Levy, CBIP
Partner and Co-Founder, Baseline Consulting

You Will Learn

• The standard alternatives for data integration
• EAI, EII, and ETL—and how they’re different
• How data integration solutions and metadata co-exist
• How CDI and MDM solve the problem
• Real-world examples and case studies
• Architectures that work

Geared To

• CIOs
• Data management staff
• Program and project managers
• Center of excellence staff
• Application developers
• Data warehouse architects
• IT architects

Data warehousing used to be IT's weapon of choice for corralling the "islands of data" and bringing order to the decentralized information chaos. However, shifting business priorities, outsourcing’s popularity, and the emergence of a new set of technology solutions have changed the landscape and the complexity of managing the abundance of enterprise data.

Data access and delivery technologies such as EII (enterprise information integration), EAI (enterprise application integration), and ETL (extract, transformation, and load) are offering companies ways to be clever and more deliberate about delivering data to systems and users more effectively. And with the emergence of customer data integration (CDI) and master data management (MDM) solutions, there's an entirely new set of offerings to consider when integrating corporate information from across packaged applications, core platforms, and legacy systems.

In this session, Evan Levy will identify the architectural trade-offs and issues associated with each solution—from performance and functionality to flexibility and efficiency. He will present examples and case studies where these new integration architectures and methods have been implemented. Along the way, he'll pepper the course with architectural examples that illustrate new ways of solving often age-old data integration dilemmas.

 

Course Outline

1. The Classic Integration Alternatives

• Myths and challenges of data integration
• Business needs and costs for data integration
• The tools and weapons of data integration
• Data warehouses, marts, and the ODS

2. Out with the Old: New Techniques for Data Integration

• Extract, transformation, and loading (ETL)
• Enterprise application integration (EAI)
• Enterprise information integration (EII)
• Master data management (MDM)
• Customer data integration (CDI)

3. A Checklist of Architectural Considerations