Managing Data in Motion book cover

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.

Audience

Data Warehouse Professionals; Data Modelers and Architects; Database and Network Administrators; ETL and Application Programmers; Project Managers; IT and Data Center Managers; CIO/CTO

Paperback, 204 Pages

Published: March 2013

Imprint: Morgan Kaufmann

ISBN: 978-0-12-397167-8

Reviews

  • "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


Contents

  • Part 1: Introduction

    • An Explosion of New Technologies for Managing the Movement and Integration of Big Data, Cloud Processing, and Virtual Data

    • The Importance of Data Integration in Data and Application Management

    • The Differences and Similarities in Managing Data in Motion and Persistant Data

    • Types and Complexity of Data Integration

    Part 2: Batch Data Integration

    • Extract, Transformation, and Load 

    • Data Warehousing

    • Data Conversion

    • Data Archiving

    • Batch Data Integration Architecture and Metadata

    Part 3: Real-Time Data Integration

    • Data Integration Patterns

    • Enterprise Service Bus (ESB)

    • Service Oriented Architecture (SOA)

    • Extensible Markup Language (XML) and other formats

    • Data Replication

    • Modeling

    • Master Data Management

    • Data Warehousing with Real Time Updates

    • Real Time Data Integration Architecture and Metadata

    • Interactions with Legacy Systems

    • External Interaction

    Part 4: Cloud and Big Data Integration

    • Cloud Architecture and Data Integration

    • Big Data Integration

    • Data Virtualization

    • In-Memory Data

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