Managing Data in Motion

Managing Data in Motion: Data Integration Best Practice Techniques and Technologies

Managing Data in Motion describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment.

The average enterprise’s computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired.

The management of the “data in motion” in organizations is rapidly becoming one of the biggest concerns for business and IT management. Data warehousing and conversion, real-time data integration, and cloud and “big data” applications are just a few of the challenges facing organizations and businesses today. Managing Data in Motion tackles these and other topics in a style easily understood by business and IT managers as well as programmers and architects.

• Presents a vendor-neutral overview of the different technologies and techniques for moving data between computer systems including the emerging solutions for unstructured as well as structured data types
• Explains, in non-technical terms, the architecture and components required to perform data integration
• Describes how to reduce the complexity of managing system interfaces and enable a scalable data architecture that can handle the dimensions of “Big Data”

Table of Contents

Part 1: Introduction to Data Integration
Chapter 1. The Importance of Data Integration
Chapter 2. What Is Data Integration?
Chapter 3. Types and Complexity of Data Integration
Chapter 4. The Process of Data Integration Development

Part 2: Batch Data Integration
Chapter 5. Introduction to Batch Data Integration
Chapter 6. Extract, Transform, and Load
Chapter 7. Data Warehousing
Chapter 8. Data Conversion
Chapter 9. Data Archiving
Chapter 10. Batch Data Integration Architecture and Metadata

Part 3: Real Time Data Integration
Chapter 11. Introduction to Real-Time Data Integration
Chapter 12. Data Integration Patterns
Chapter 13. Core Real-Time Data Integration Technologies
Chapter 14. Data Integration Modeling
Chapter 15. Master Data Management
Chapter 16. Data Warehousing with Real-Time Updates
Chapter 17. Real-Time Data Integration Architecture and Metadata

Part 4: Big, Cloud, Virtual Data
Chapter 18. Introduction to Big Data Integration
Chapter 19. Cloud Architecture and Data Integration
Chapter 20. Data Virtualization
Chapter 21. Big Data Integration
Chapter 22. Conclusion to Managing Data in Motion