Articles liés à Big Data SMACK: A Guide to Apache Spark, Mesos, Akka,...
- ÉditeurApress
- Date d'édition2016
- ISBN 10 1484221745
- ISBN 13 9781484221747
- ReliureBroché
- Langueanglais
- Numéro d'édition1
- Nombre de pages292
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Big Data SMACK: A Guide to Apache Spark, Mesos, Akka, Cassandra, and Kafka
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
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Big Data SMACK : A Guide to Apache Spark, Mesos, Akka, Cassandra, and Kafka
Vendeur : Chiron Media, Wallingford, Royaume-Uni
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PF. Etat : New. N° de réf. du vendeur 6666-IUK-9781484221747
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Big Data SMACK: A Guide to Apache Spark, Mesos, Akka, Cassandra, and Kafka
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Etat : New. In English. N° de réf. du vendeur ria9781484221747_new
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Big Data SMACK
impression à la demandeVendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Learn how to integrate full-stack open source big data architecture and to choose the correct technology-Scala/Spark, Mesos, Akka, Cassandra, and Kafka-in every layer.Big data architecture is becoming a requirement for many different enterprises. So far, however, the focus has largely been on collecting, aggregating, and crunching large data sets in a timely manner. In many cases now, organizations need more than one paradigm to perform efficient analyses.Big Data SMACK explains each of the full-stack technologies and, more importantly, how to best integrate them. It provides detailed coverage of the practical benefits of these technologies and incorporates real-world examples in every situation. This book focuses on the problems and scenarios solved by the architecture, as well as the solutions provided by every technology. It covers the six main concepts of big data architecture and how integrate, replace, and reinforce every layer:The language: ScalaThe engine: Spark (SQL, MLib, Streaming, GraphX)The container: Mesos, DockerThe view: AkkaThe storage: CassandraThe message broker: KafkaWhat You Will Learn:Make big data architecture without using complex Greek letter architecturesBuild a cheap but effective cluster infrastructureMake queries, reports, and graphs that business demandsManage and exploit unstructured and No-SQL data sourcesUse tools to monitor the performance of your architectureIntegrate all technologies and decide which ones replace and which ones reinforceWho This Book Is For:Developers, data architects, and data scientists looking to integrate the most successful big data open stack architecture and to choose the correct technology in every layer 292 pp. Englisch. N° de réf. du vendeur 9781484221747
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Big Data SMACK: A Guide to Apache Spark, Mesos, Akka, Cassandra, and Kafka
impression à la demandeVendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
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Paperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 584. N° de réf. du vendeur C9781484221747
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Big Data Smack: A Guide to Apache Scala/Spark, Mesos, Akka, Cassandra, and Kafka
Edité par
Apress, Incorporated, 2016
ISBN 10 : 1484221745
ISBN 13 : 9781484221747
Neuf
Couverture souple
Vendeur : Books Puddle, New York, NY, Etats-Unis
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Etat : New. pp. 264. N° de réf. du vendeur 26374909994
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Big Data Smack: A Guide to Apache Scala/Spark, Mesos, Akka, Cassandra, and Kafka
Edité par
Apress, Incorporated, 2016
ISBN 10 : 1484221745
ISBN 13 : 9781484221747
Neuf
Couverture souple
Vendeur : Majestic Books, Hounslow, Royaume-Uni
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Etat : New. Print on Demand pp. 264. N° de réf. du vendeur 372184053
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Big Data Smack: A Guide to Apache Spark, Mesos, Akka, Cassandra, and Kafka
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Paperback. Etat : Brand New. 264 pages. 9.00x6.00x1.00 inches. In Stock. N° de réf. du vendeur x-1484221745
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Big Data SMACK : A Guide to Apache Spark, Mesos, Akka, Cassandra, and Kafka
impression à la demandeVendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Learn how to integrate full-stack open source big data architecture and to choose the correct technology-Scala/Spark, Mesos, Akka, Cassandra, and Kafka-in every layer.Big data architecture is becoming a requirement for many different enterprises. So far, however, the focus has largely been on collecting, aggregating, and crunching large data sets in a timely manner. In many cases now, organizations need more than one paradigm to perform efficient analyses.Big Data SMACK explains each of the full-stack technologies and, more importantly, how to best integrate them. It provides detailed coverage of the practical benefits of these technologies and incorporates real-world examples in every situation. This book focuses on the problems and scenarios solved by the architecture, as well as the solutions provided by every technology. It covers the six main concepts of big data architecture and how integrate, replace, and reinforce every layer:The language: ScalaThe engine: Spark (SQL, MLib, Streaming, GraphX)The container: Mesos, DockerThe view: AkkaThe storage: CassandraThe message broker: KafkaWhat You Will Learn:Make big data architecture without using complex Greek letter architecturesBuild a cheap but effective cluster infrastructureMake queries, reports, and graphs that business demandsManage and exploit unstructured and No-SQL data sourcesUse tools to monitor the performance of your architectureIntegrate all technologies and decide which ones replace and which ones reinforceWho This Book Is For:Developers, data architects, and data scientists looking to integrate the most successful big data open stack architecture and to choose the correct technology in every layer. N° de réf. du vendeur 9781484221747
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Big Data SMACK
impression à la demandeVendeur : moluna, Greven, Allemagne
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The first book presenting the SMACK stackA practical guide teaching how to incorporate big dataCovers the full stack of big data architecture, discussing the practical benefits of each technology. N° de réf. du vendeur 123474193
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