Accelerating Digital Transformation on Z Using Data Virtualization
An IBM Redpaper publication
Published 17 December 2018, updated 13 April 2021
IBM Form #: REDP-5523-00
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Authors: Blanca Borden, Calvin Fudge, Jen Nelson, Jim Porell
This IBM® Redpaper™ publication introduces a new data virtualization capability that enables IBM z/OS® data to be combined with other enterprise data sources in real-time, which allows applications to access any live enterprise data anytime and use the power and efficiencies of the IBM Z® platform.
Modern businesses need actionable and timely insight from current data. They cannot afford the time that is necessary to copy and transform data. They also cannot afford to secure and protect each copy of personally identifiable information and corporate intellectual property.
Data virtualization enables direct connections to be established between multiple data sources and the applications that process the data. Transformations can be applied, in line, to enable real-time access to data, which opens up many new ways to gain business insight with less IT infrastructure necessary to achieve those goals. Data virtualization can become the backbone for advanced analytics and modern applications.
The IBM Data Virtualization Manager for z/OS (DVM) can be used as a stand-alone product or as a utility that is used by other products. Its goal is to enable access to live mainframe transaction data and make it usable by any application. This enables customers to use the strengths of mainframe processing with new agile applications.
Additionally, its modern development environment and code-generating capabilities enable any developer to update, access, and combine mainframe data easily by using modern APIs and languages. If data is the foundation for building new insights, IBM DVM is a key tool for providing easy, cost-efficient access to that foundation.
Table of contents
Chapter 1. Business value of data virtualization
Chapter 2. Virtualization on IBM Z
Chapter 3. IBM Data Virtualization Manager in action
Chapter 4. Data Virtualization conclusion
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