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

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-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 the 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
Frameworks and patterns 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

Geared To

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

Technical resources.

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