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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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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 and Hadoop

In big data, Hadoop is a core component/technology that cannot be ignored. Elasticsearch has very good support for Hadoop and Elastic Stack has a product for achieving this integration called ES-Hadoop. We will discuss that in some detail in this section. For more details, we urge you to go to the elastic official page here: https://www.elastic.co/products/hadoop.

Hadoop is considered to be good for batch analytics but, for modern applications as well as enterprises dealing with batch and real-time data, performing meaningful analysis is crucial. For dealing with real-time data, this integration of Elasticsearch and Hadoop is apt. Elasticsearch brings the real-time analytics capability on top of Hadoop. ES-Hadoop is a connector shipped as a product by elastic along with the Elastic Stack.

This connector works with Hadoop 1.x and 2.x versions and works like a charm with a variety of...

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