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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
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Tomcy John
Vivek Mishra Vivek Mishra
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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

Elastic Stack

With Elasticsearch 1.x and Elasticsearch 2.x, the various products which work together have to be chosen and dealt with individually. Many aspects are purely decided based on prior Elastic Stack experience. With Elastic Stack (5.x), these products (Elasticsearch, Logstash, Kibana, Beats and X-Pack) have all come together and now really form a stack (platform) with minimal trial and error on various versions. This aspect has made implementing Elasticsearch very easier as compared to earlier versions.

This figure shows the Elastic Stack with harmonized working of all components:


Figure 09: Elastic Stack 5.x (all icons courtesy of https://www.elastic.co/v5)

In the next subsections, we will discuss each of the components forming Elastic Stack in adequate detail for your understanding. Again, these wouldn't delve too deep into each component, but would give adequate details for playing with the examples...

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