IBM InfoSphere Streams: assembling continuous insight in the information revolution
In this IBM® Redbooks® publication, we discuss and describe the positioning, functions, capabilities, and advanced programming techniques for IBM InfoSphere™ Streams (V2), a new paradigm and key component of IBM Big Data platform. Data has traditionally been stored in files or databases, and then an...
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| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
[San Jose, California]
IBM Corporation
[2011]
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| Series: | Redbooks
|
| Subjects: | |
| Links: | https://learning.oreilly.com/library/view/-/0738436151/?ar |
| Summary: | In this IBM® Redbooks® publication, we discuss and describe the positioning, functions, capabilities, and advanced programming techniques for IBM InfoSphere™ Streams (V2), a new paradigm and key component of IBM Big Data platform. Data has traditionally been stored in files or databases, and then analyzed by queries and applications. With stream computing, analysis is performed moment by moment as the data is in motion. In fact, the data might never be stored (perhaps only the analytic results). The ability to analyze data in motion is called real-time analytic processing (RTAP). IBM InfoSphere Streams takes a fundamentally different approach to Big Data analytics and differentiates itself with its distributed runtime platform, programming model, and tools for developing and debugging analytic applications that have a high volume and variety of data types. Using in-memory techniques and analyzing record by record enables high velocity. Volume, variety and velocity are the key attributes of Big Data. The data streams that are 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. This book is intended for professionals that require an understanding of how to process high volumes of streaming data or need information about how to implement systems to satisfy those requirements. See: http://www.redbooks.ibm.com/abstracts/sg247865.html for the IBM InfoSphere Streams (V1) release. |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | 1 Online-Ressource (456 Seiten) illustrations |
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| spelling | Ballard, Chuck VerfasserIn aut IBM InfoSphere Streams assembling continuous insight in the information revolution Chuck Ballard, Kevin Foster, Andy Frenkiel, Bugra Gedik, Michael P. Koranda, Senthil Nathan, Deepak Rajan, Roger Rea, Mike Spicer, Brian Williams, Vitali N. Zoubov [San Jose, California] IBM Corporation [2011] 1 Online-Ressource (456 Seiten) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Redbooks Includes bibliographical references and index In this IBM® Redbooks® publication, we discuss and describe the positioning, functions, capabilities, and advanced programming techniques for IBM InfoSphere™ Streams (V2), a new paradigm and key component of IBM Big Data platform. Data has traditionally been stored in files or databases, and then analyzed by queries and applications. With stream computing, analysis is performed moment by moment as the data is in motion. In fact, the data might never be stored (perhaps only the analytic results). The ability to analyze data in motion is called real-time analytic processing (RTAP). IBM InfoSphere Streams takes a fundamentally different approach to Big Data analytics and differentiates itself with its distributed runtime platform, programming model, and tools for developing and debugging analytic applications that have a high volume and variety of data types. Using in-memory techniques and analyzing record by record enables high velocity. Volume, variety and velocity are the key attributes of Big Data. The data streams that are 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. This book is intended for professionals that require an understanding of how to process high volumes of streaming data or need information about how to implement systems to satisfy those requirements. See: http://www.redbooks.ibm.com/abstracts/sg247865.html for the IBM InfoSphere Streams (V1) release. Streaming technology (Telecommunications) Real-time data processing Parallel processing (Electronic computers) En continu (Télécommunications) Temps réel (Informatique) Parallélisme (Informatique) Parallel processing (Electronic computers) (OCoLC)fst01052928 Real-time data processing (OCoLC)fst01091219 Streaming technology (Telecommunications) (OCoLC)fst01134637 Foster, Kevin VerfasserIn aut Frenkiel, Andy VerfasserIn aut Gedik, Buğra VerfasserIn aut Koranda, Michael P. VerfasserIn aut Nathan, Senthil VerfasserIn aut Rajan, Deepak VerfasserIn aut Rea, Roger VerfasserIn aut Spicer, Mike VerfasserIn aut Williams, Brian VerfasserIn aut Zoubov, Vitali N. VerfasserIn aut |
| spellingShingle | Ballard, Chuck Foster, Kevin Frenkiel, Andy Gedik, Buğra Koranda, Michael P. Nathan, Senthil Rajan, Deepak Rea, Roger Spicer, Mike Williams, Brian Zoubov, Vitali N. IBM InfoSphere Streams assembling continuous insight in the information revolution Streaming technology (Telecommunications) Real-time data processing Parallel processing (Electronic computers) En continu (Télécommunications) Temps réel (Informatique) Parallélisme (Informatique) Parallel processing (Electronic computers) (OCoLC)fst01052928 Real-time data processing (OCoLC)fst01091219 Streaming technology (Telecommunications) (OCoLC)fst01134637 |
| subject_GND | (OCoLC)fst01052928 (OCoLC)fst01091219 (OCoLC)fst01134637 |
| title | IBM InfoSphere Streams assembling continuous insight in the information revolution |
| title_auth | IBM InfoSphere Streams assembling continuous insight in the information revolution |
| title_exact_search | IBM InfoSphere Streams assembling continuous insight in the information revolution |
| title_full | IBM InfoSphere Streams assembling continuous insight in the information revolution Chuck Ballard, Kevin Foster, Andy Frenkiel, Bugra Gedik, Michael P. Koranda, Senthil Nathan, Deepak Rajan, Roger Rea, Mike Spicer, Brian Williams, Vitali N. Zoubov |
| title_fullStr | IBM InfoSphere Streams assembling continuous insight in the information revolution Chuck Ballard, Kevin Foster, Andy Frenkiel, Bugra Gedik, Michael P. Koranda, Senthil Nathan, Deepak Rajan, Roger Rea, Mike Spicer, Brian Williams, Vitali N. Zoubov |
| title_full_unstemmed | IBM InfoSphere Streams assembling continuous insight in the information revolution Chuck Ballard, Kevin Foster, Andy Frenkiel, Bugra Gedik, Michael P. Koranda, Senthil Nathan, Deepak Rajan, Roger Rea, Mike Spicer, Brian Williams, Vitali N. Zoubov |
| title_short | IBM InfoSphere Streams |
| title_sort | ibm infosphere streams assembling continuous insight in the information revolution |
| title_sub | assembling continuous insight in the information revolution |
| topic | Streaming technology (Telecommunications) Real-time data processing Parallel processing (Electronic computers) En continu (Télécommunications) Temps réel (Informatique) Parallélisme (Informatique) Parallel processing (Electronic computers) (OCoLC)fst01052928 Real-time data processing (OCoLC)fst01091219 Streaming technology (Telecommunications) (OCoLC)fst01134637 |
| topic_facet | Streaming technology (Telecommunications) Real-time data processing Parallel processing (Electronic computers) En continu (Télécommunications) Temps réel (Informatique) Parallélisme (Informatique) |
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