Managing Data in Motion, 1st Edition

  • Published By:
  • ISBN-10: 0123977916
  • ISBN-13: 9780123977915
  • DDC: 005.74
  • Grade Level Range: College Freshman - College Senior
  • 204 Pages | eBook
  • Original Copyright 2013 | Published/Released June 2014
  • This publication's content originally published in print form: 2013

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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.

Table of Contents

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Dedication Page.
1: Biography.
2: Introduction to Data Integration.
3: The Importance of Data Integration.
4: What is Data Integration?.
5: Types and Complexity of Data Integration.
6: The Process of Data Integration Development.
7: Batch Data Integration.
8: Introduction to Batch Data Integration.
9: Extract, Transform, and Load.
10: Data Warehousing.
11: Data Conversion.
12: Data Archiving.
13: Batch Data Integration Architecture and Metadata.
14: Real Time Data Integration.
15: Introduction to Real-Time Data Integration.
16: Data Integration Patterns.
17: Core Real-Time Data Integration Technologies.
18: Data Integration Modeling.
19: Master Data Management.
20: Data Warehousing with Real-Time Updates.
21: Real-Time Data Integration Architecture and Metadata.
22: Big, Cloud, Virtual Data.
23: Introduction to Big Data Integration.
24: Cloud Architecture and Data Integration.
25: Data Virtualization.
26: Big Data Integration.
27: Conclusion to Managing Data in Motion.