Könyv Managing Data in Motion April Reeve

Managing Data in Motion

Szerző: April Reeve
Nyelv: Angol
Kötés: Puha kötésű
Elérhetőség: Kiadói készleten rendelésre
Küldés 28-34 napon belül
18 386 Ft
Managing Data in Motion describes techniques that have been developed for significantly reducing the...

Információk a könyvről

Szerző
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2013
oldal
204
EAN
9780123971678
ISBN
0123971675
Enbook ID
01260639
Súly
420
Méretek
191 x 234 x 12

Teljes leírás

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. It 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. It explains, in non-technical terms, the architecture and components required to perform data integration. It describes how to reduce the complexity of managing system interfaces and enable a scalable data architecture that can handle the dimensions of "Big Data".

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