Scalable Big Data Architecture: A practitioners guide to choosing relevant Big Data architecture [Paperback]

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, its 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 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 wlÓ'