Book Image

Modern Big Data Processing with Hadoop

By : V Naresh Kumar, Manoj R Patil, Prashant Shindgikar
Book Image

Modern Big Data Processing with Hadoop

By: V Naresh Kumar, Manoj R Patil, Prashant Shindgikar

Overview of this book

The complex structure of data these days requires sophisticated solutions for data transformation, to make the information more accessible to the users.This book empowers you to build such solutions with relative ease with the help of Apache Hadoop, along with a host of other Big Data tools. This book will give you a complete understanding of the data lifecycle management with Hadoop, followed by modeling of structured and unstructured data in Hadoop. It will also show you how to design real-time streaming pipelines by leveraging tools such as Apache Spark, and build efficient enterprise search solutions using Elasticsearch. You will learn to build enterprise-grade analytics solutions on Hadoop, and how to visualize your data using tools such as Apache Superset. This book also covers techniques for deploying your Big Data solutions on the cloud Apache Ambari, as well as expert techniques for managing and administering your Hadoop cluster. By the end of this book, you will have all the knowledge you need to build expert Big Data systems.
Table of Contents (12 chapters)


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...