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Data Lake for Enterprises

You're reading from   Data Lake for Enterprises Lambda Architecture for building enterprise data systems

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Product type Paperback
Published in May 2017
Publisher Packt
ISBN-13 9781787281349
Length 596 pages
Edition 1st Edition
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Authors (3):
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Pankaj Misra Pankaj Misra
Author Profile Icon Pankaj Misra
Pankaj Misra
Tomcy John Tomcy John
Author Profile Icon Tomcy John
Tomcy John
Vivek Mishra Vivek Mishra
Author Profile Icon Vivek Mishra
Vivek Mishra
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Toc

Table of Contents (13) Chapters Close

Preface 1. Introduction to Data FREE CHAPTER 2. Comprehensive Concepts of a Data Lake 3. Lambda Architecture as a Pattern for Data Lake 4. Applied Lambda for Data Lake 5. Data Acquisition of Batch Data using Apache Sqoop 6. Data Acquisition of Stream Data using Apache Flume 7. Messaging Layer using Apache Kafka 8. Data Processing using Apache Flink 9. Data Store Using Apache Hadoop 10. Indexed Data Store using Elasticsearch 11. Data Lake Components Working Together 12. Data Lake Use Case Suggestions

Elasticsearch deployment options

Elasticsearch supports a scale-out deployment architecture, with the main priority on availability. The Elasticsearch cluster is composed of nodes playing master and data node roles. Quorum based availability drives cluster availability in general.

While the most common deployment is that of single data center deployment, which can comprise dedicated master nodes and dedicated data nodes, for larger clusters, multiple data center deployments are also required for very high availability of critical applications.

It is generally not a very good idea to deploy Elasticsearch clusters across data centers, since the Elasticsearch leader election algorithm and data node selection are based on network distance, which assumes that all the nodes are identical in terms of all resources; it is expected that in a cluster all nodes are equidistant. This can go wrong in cross data center clusters...

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