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Modern Big Data Processing with Hadoop

You're reading from   Modern Big Data Processing with Hadoop Expert techniques for architecting end-to-end big data solutions to get valuable insights

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781787122765
Length 394 pages
Edition 1st Edition
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Concepts
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Authors (3):
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Manoj R Patil Manoj R Patil
Author Profile Icon Manoj R Patil
Manoj R Patil
Prashant Shindgikar Prashant Shindgikar
Author Profile Icon Prashant Shindgikar
Prashant Shindgikar
V Naresh Kumar V Naresh Kumar
Author Profile Icon V Naresh Kumar
V Naresh Kumar
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Toc

Table of Contents (12) Chapters Close

Preface 1. Enterprise Data Architecture Principles FREE CHAPTER 2. Hadoop Life Cycle Management 3. Hadoop Design Consideration 4. Data Movement Techniques 5. Data Modeling in Hadoop 6. Designing Real-Time Streaming Data Pipelines 7. Large-Scale Data Processing Frameworks 8. Building Enterprise Search Platform 9. Designing Data Visualization Solutions 10. Developing Applications Using the Cloud 11. Production Hadoop Cluster Deployment

Analyzer

We have already learned about an inverted index. We know that Elasticsearch stores a document into an inverted index. This transformation is known as analysis. This is required for a successful response of the index search query.

Also, many of the times, we need to use some kind of transformation before sending that document to Elasticsearch index. We may need to change the document to lowercase, stripping off HTML tags if any from the document, remove white space between two words, tokenize the fields based on delimiters, and so on.

Elasticsearch offers the following built-in analyzers:

  • Standard analyzer: It is a default analyzer. This uses standard tokenizer to divide text. It normalizes tokens, lowercases tokens, and also removes unwanted tokens.
  • Simple analyzer: This analyzer is composed of lowercase tokenizer.
  • Whitespace analyzer: This uses the whitespace tokenizer...
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