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An Architecture for Fast and General Data Processing on Large Clusters Paperback - 2016
by Matei Zaharia
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- Title An Architecture for Fast and General Data Processing on Large Clusters
- Author Matei Zaharia
- Binding Paperback
- Pages 141
- Volumes 1
- Language ENG
- Publisher ACM Books
- Date 2016
- Features Bibliography
- ISBN 9781970001563 / 1970001569
- Weight 0.56 lbs (0.25 kg)
- Dimensions 9.25 x 7.5 x 0.3 in (23.50 x 19.05 x 0.76 cm)
- Library of Congress subjects Electronic data processing - Distributed, Distributed databases
- Library of Congress Catalog Number 2017471066
- Dewey Decimal Code 004.36
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An Architecture for Fast and General Data Processing on Large Clusters (ACM Books)
by Zaharia, Matei
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An Architecture for Fast and General Data Processing on Large Clusters
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Morgan & Claypool, 2016. Paperback. New. 141 pages. 9.25x7.50x0.40 inches.
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An Architecture for Fast and General Data Processing on Large Clusters
by Matei Zaharia
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An Architecture for Fast and General Data Processing on Large Clusters
by Matei Zaharia
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- Paperback
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Paperback / softback. New. This book proposes an architecture for cluster computing systems that can tackle emerging data processing workloads at scale. Whereas early cluster computing systems, like MapReduce, handled batch processing, our architecture also enables streaming and interactive queries, while keeping MapReduce's scalability and fault tolerance.
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An Architecture for Fast and General Data Processing on Large Clusters (ACM Books)
by Zaharia, Matei
- New
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- 9781970001563 / 1970001569
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ACM Books. New. Special order direct from the distributor
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An Architecture for Fast and General Data Processing on Large Clusters (ACM Books)
by Zaharia, Matei
- New
- Paperback
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- New
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- Paperback
- ISBN 10 / ISBN 13
- 9781970001563 / 1970001569
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Kraków, Poland
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Morgan & Claypool Publishers, 2016 8vo (23.5 cm), XII, 130 pp. Laminated wrappers. "This book, a revised version of the 2014 ACM Dissertation Award winning dissertation, proposes an architecture for cluster computing systems that can tackle emerging data processing workloads at scale. Whereas early cluster computing systems, like MapReduce, handled batch processing, our architecture also enables streaming and interactive queries, while keeping MapReduce's scalability and fault tolerance. And whereas most deployed systems only support simple one-pass computations (e.g., SQL queries), ours also extends to the multi-pass algorithms required for complex analytics like machine learning. Finally, unlike the specialized systems proposed for some of these workloads, our architecture allows these computations to be combined, enabling rich new applications that intermix, for example, streaming and batch processing." (from the publisher's synopsis)
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