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

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Description

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.
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Product details

  • Paperback | 204 pages
  • 187.96 x 231.14 x 12.7mm | 385.55g
  • Morgan Kaufmann Publishers In
  • San Francisco, United States
  • English
  • Illustrated; Illustrations, unspecified
  • 0123971675
  • 9780123971678
  • 1,806,441

Table of contents

Part 1: An Introduction to Data Integration

Part 2: Batch Data Integration

* ETL

* Data Warehousing

* Data Conversion

* Batch Data Integration Architecture and Metadata

Part 3: Real-Time Data Integration

* Best Practice Patterns

* Technologies

* Message Modeling

* XML

* Master Data Management

* Real Time Data Integration Architecture and Metadata

* Interactions with Legacy Applications

* External Interaction

Part 4: Cloud and Big Data Integration
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Review quote

"The highlight of the book is that the author is able to present a broad, complicated subject in a coherent, consolidated, and readable manner. This is ideal for busy information technology managers and chief technical officers who have little time for outside reading. The book is especially valuable for those managers who are not familiar with modern data management or who are fluent in general computing technology but are tasked with managing data on a larger scale."--ComputingReviews.com, November 4, 2013 "Reeve, an enterprise information consultant with EMC, describes different techniques, technologies, and best practices for managing the transfer of data between computer systems and integrating disparate databases together within a large organization."--Reference and Research Book News, August 2013 "Few if any enterprises have the luxury of a single unified integrated data platform. Yet one of the least considered areas in enterprise information management is how we should treat and manage the growing numbers of interfaces. April Reeve presents a much-needed overview and guide to the challenges of data integration."--John Ladley, Principal of IMCue Solutions, Editor of the Data Strategy Journal
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About April Reeve

April Reeve has spent the last 25 years working as an enterprise architect and program manager, primarily for large financial services firms. Currently she is working for EMC Consulting as a Business Consultant in the Enterprise Information Management practice. April is an expert in multiple Data Management disciplines including Data Conversion, Data Warehousing, Business Intelligence, Master Data Management, Data Integration, and Data Governance. She is a regular speaker on Data Management topics at Industry conferences.
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Rating details

8 ratings
3.5 out of 5 stars
5 12% (1)
4 25% (2)
3 62% (5)
2 0% (0)
1 0% (0)
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