Managing Data In Motion
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Managing Data in Motion
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. - 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 - Explains, in non-technical terms, the architecture and components required to perform data integration - Describes how to reduce the complexity of managing system interfaces and enable a scalable data architecture that can handle the dimensions of "Big Data"
IBM InfoSphere Streams Harnessing Data in Motion
In this IBM® Redbooks® publication, we discuss and describe the positioning, functions, capabilities, and advanced programming techniques for IBM InfoSphereTM Streams (V1). See: http://www.redbooks.ibm.com/abstracts/sg247970.html for the newer InfoSphere Streams (V2) release. Stream computing is a new paradigm. In traditional processing, queries are typically run against relatively static sources of data to provide a query result set for analysis. With stream computing, a process that can be thought of as a continuous query, that is, the results are continuously updated as the data sources are refreshed. So, traditional queries seek and access static data, but with stream computing, a continuous stream of data flows to the application and is continuously evaluated by static queries. However, with IBM InfoSphere Streams, those queries can be modified over time as requirements change. IBM InfoSphere Streams takes a fundamentally different approach to continuous processing and differentiates itself with its distributed runtime platform, programming model, and tools for developing continuous processing applications. The data streams consumable by IBM InfoSphere Streams can originate from sensors, cameras, news feeds, stock tickers, and a variety of other sources, including traditional databases. It provides an execution platform and services for applications that ingest, filter, analyze, and correlate potentially massive volumes of continuous data streams.
Data Management courseware based on CDMP Fundamentals
Besides the courseware publication (ISBN: 9789401807999), you are advised to obtain the DAMA DMBOK publication (ISBN: 9781634622349). Optionally, you can use the publication Data management: a gentle introduction (ISBN: 9789401805506) as inspiration for examples and quotes about the field of data management. This material is intended to prepare participants for the CDMP exam by DAMA International. The courseware can only be ordered by partners and is based on the current version of the DAMA DMBOK. The material will be updated when new versions of DMBOK are published. DAMA DMBOK is the industry reference for data management. It is published by DAMA International and is currently in its second version. The DMBOK is developed by professionals and can be seen as a collection of best practices. The domain of data management is divided into functional areas which are discussed in terms of definitions (what is it), goals (what are we trying to achieve), steps (what are typical activities), inputs/outputs, and participating roles. Developing and sustaining an effective data management function is far from an easy task. The DMBOK framework is adopted by many organizations as the foundation for their data management function: standardized language and good practices speed up the learning process. After the training, you have an overview of the field of data management, its terminology, and current best practices.