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Books > Computing & IT > Applications of computing > Databases
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Scalable Big Data Architecture - A practitioners guide to choosing relevant Big Data architecture (Paperback, 1st ed.)
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Scalable Big Data Architecture - A practitioners guide to choosing relevant Big Data architecture (Paperback, 1st ed.)
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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-SQL to 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 to integrate into that pattern.
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