scalable big data architecture von azarmi bahaaldine (16 Ergebnisse)

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Taschenbuch. Zustand: Neu. Neuware -Thisbook highlights the different types of data architecture and illustrates themany possibilities hidden behind the term 'Big Data', from the usage of No-SQLdatabases to the deployment of stream analytics architecture, machine learningand governance.ScalableBig Data Architecture coversreal-world, concrete industry use cases that leverage complex distributedapplications , which involve web applications, RESTful API, and high throughputof large amount of data stored in highly scalable No-SQL data stores such asCouchbase and Elasticsearch. This book demonstrates how data processing can bedone at scale from the usage of NoSQL datastores to the combination of Big Datadistribution.Whenthe data processing is too complex and involves different processing topologylike long running jobs, stream processing, multiple data sources correlationand machine learning, it¿s often necessary to delegate the load to Hadoop orSpark and use the No-SQLto serve processed data in real time.Thisbook shows you how to choose a relevant combination of big data technologiesavailable within the Hadoop ecosystem. It focuses on processing long jobsarchitecture, stream data patterns, log analysis, and real time analytics. Everypattern is illustrated with practical examples, which use the different opensourceprojects such as Logstash, Spark, Kafka, and so on.Traditionaldata infrastructures are built for digesting and rendering data synthesis andanalytics from large amount of data. This book helps you to understand why youshould consider using machine learning algorithms early on in the projectbefore being overwhelmed by constraints imposed by dealing with the highthroughput of Big data.ScalableBig Data Architecture is fordevelopers, data architects, and data scientists looking for a betterunderstanding of how to choose the most relevant pattern for a Big Data projectand which tools tointegrate into that pattern.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 160 pp. Englisch.

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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Thisbook highlights the different types of data architecture and illustrates themany possibilities hidden behind the term 'Big Data', from the usage of No-SQLdatabases to the deployment of stream analytics architecture, machine learning,and governance.ScalableBig Data Architecture coversreal-world, concrete industry use cases that leverage complex distributedapplications , which involve web applications, RESTful API, and high throughputof large amount of data stored in highly scalable No-SQL data stores such asCouchbase and Elasticsearch. This book demonstrates how data processing can bedone at scale from the usage of NoSQL datastores to the combination of Big Datadistribution.Whenthe data processing is too complex and involves different processing topologylike long running jobs, stream processing, multiple data sources correlation,and machine learning, it's often necessary to delegate the load to Hadoop orSpark and use the No-SQL to serve processed data in real time.Thisbook shows you how to choose a relevant combination of big data technologiesavailable within the Hadoop ecosystem. It focuses on processing long jobs,architecture, stream data patterns, log analysis, and real time analytics. Everypattern is illustrated with practical examples, which use the different opensourceprojects such as Logstash, Spark, Kafka, and so on.Traditionaldata infrastructures are built for digesting and rendering data synthesis andanalytics from large amount of data. This book helps you to understand why youshould consider using machine learning algorithms early on in the project,before being overwhelmed by constraints imposed by dealing with the highthroughput of Big data.ScalableBig Data Architecture is fordevelopers, data architects, and data scientists looking for a betterunderstanding of how to choose the most relevant pattern for a Big Data projectand which tools to integrate into that pattern. 160 pp. Englisch.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book highlights the different types of data architecture and illustrates the many possibilities hidden behind the term 'Big Data', from the usage of No-SQL databases to the deployment of stream analytics architecture, machine learning, and governance.Scalable Big Data Architecture covers real-world, concrete industry use cases that leverage complex distributed applications , which involve web applications, RESTful API, and high throughput of large amount of data stored in highly scalable No-SQL data stores such as Couchbase and Elasticsearch. This book demonstrates how data processing can be done at scale from the usage of NoSQL datastores to the combination of Big Data distribution.When the data processing is too complex and involves different processing topology like long running jobs, stream processing, multiple data sources correlation, and machine learning, it's often necessary to delegate the load to Hadoop or Spark and use the No-SQLto serve processed data in real time.This book shows you how to choose a relevant combination of big data technologies available within the Hadoop ecosystem. It focuses on processing long jobs, architecture, stream data patterns, log analysis, and real time analytics. Every pattern is illustrated with practical examples, which use the different open sourceprojects such as Logstash, Spark, Kafka, and so on.Traditional data infrastructures are built for digesting and rendering data synthesis and analytics from large amount of data. This book helps you to understand why you should consider using machine learning algorithms early on in the project, before being overwhelmed by constraints imposed by dealing with the high throughput of Big data.Scalable Big Data Architecture is for developers, data architects, and data scientists looking for a better understanding of how to choose the most relevant pattern for a Big Data project and which tools tointegrate into that pattern.

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