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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

Indexed Data Store using Elasticsearch

In the previous chapter on Hadoop, we persisted the data in hand onto Hadoop (HDFS). Reading/querying data from Hadoop at a fast pace is an issue, and that's when an indexed data store such as Elasticsearch and its significance come forth in our Data Lake implementation.

As in other chapters in this part of the book, we will start off the chapter by explaining the layer where this technology will be used. We will then explain the reason for choosing this technology for this capability and start diving deep into Elasticsearch and its working. We will cover enough details on Elasticsearch so that you have adequate details to understand this technology. As always we will only give enough details and full deep dive is beyond the scope of this book.
We would then take you through a hands-on coding session, where you will first learn to install this technology and then...

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