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Data Lake for Enterprises

You're reading from  Data Lake for Enterprises

Product type Book
Published in May 2017
Publisher Packt
ISBN-13 9781787281349
Pages 596 pages
Edition 1st Edition
Languages
Authors (3):
Vivek Mishra Vivek Mishra
Profile icon Vivek Mishra
Tomcy John Tomcy John
Profile icon Tomcy John
Pankaj Misra Pankaj Misra
Profile icon Pankaj Misra
View More author details

Table of Contents (23) Chapters

Title Page
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Part 1 - Overview
Part 2 - Technical Building blocks of Data Lake
Part 3 - Bringing It All Together
Introduction to Data Comprehensive Concepts of a Data Lake Lambda Architecture as a Pattern for Data Lake Applied Lambda for Data Lake Data Acquisition of Batch Data using Apache Sqoop Data Acquisition of Stream Data using Apache Flume Messaging Layer using Apache Kafka Data Processing using Apache Flink Data Store Using Apache Hadoop Indexed Data Store using Elasticsearch Data Lake Components Working Together 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 in Elasticsearch. Various types of tokenizers exist that can do the job of actually splitting the string to terms/tokens.
  3. Normalize the data and search terms to make the search easier and relevant (further filtering and sanitizing...
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