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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

Elasticsearch ecosystem

The Elasticsearch ecosystem does have some very important components and some of the important ones, especially for us are as detailed in this section.

Elasticsearch analyzers

Elasticsearch stores data in a very systematic and easily accessible and searchable fashion. To make data analysis easy and data more searchable, when the data is inducted into Elasticsearch, the following steps are done:

  1. Initial tidying of the string received (sanitizing). This is done by a character filter in Elasticsearch. This filter can sanitize the string before actual tokenization. It can also take out unnecessary characters or can even transform certain characters as needed.
  2. Tokenize the string into terms for creating an Inverted Index. This is done by Tokenizers...
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