TDWI Data Integration Principles and Practices: Creating Information Unity from Data Disparity
Duration: One Day Course
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.
You Will Learn
- The role, purpose, and issues of data integration strategy
- Integration patterns and a framework for data integration architecture
- 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
- BI, MDM, and data warehousing program and project managers
- Data integration architects, designers, and developers
- Data and technology architects