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Advanced Elasticsearch 7.0: A practical guide to designing, indexing, and querying advanced distributed search engines

Wong

$19.99 per month

3.5 (4 Ratings)

Paperback Aug 2019 560 pages 1st Edition

Wong

$19.99 per month

3.5 (4 Ratings)

Paperback Aug 2019 560 pages 1st Edition

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View table of contents Preview Book Download Code

Key benefits

Master the latest distributed search and analytics capabilities of Elasticsearch 7.0
Perform searching, indexing, and aggregation of your data at scale
Discover tips and techniques for speeding up your search query performance

Description

Building enterprise-grade distributed applications and executing systematic search operations call for a strong understanding of Elasticsearch and expertise in using its core APIs and latest features. This book will help you master the advanced functionalities of Elasticsearch and understand how you can develop a sophisticated, real-time search engine confidently. In addition to this, you'll also learn to run machine learning jobs in Elasticsearch to speed up routine tasks. You'll get started by learning to use Elasticsearch features on Hadoop and Spark and make search results faster, thereby improving the speed of query results and enhancing the customer experience. You'll then get up to speed with performing analytics by building a metrics pipeline, defining queries, and using Kibana for intuitive visualizations that help provide decision-makers with better insights. The book will later guide you through using Logstash with examples to collect, parse, and enrich logs before indexing them in Elasticsearch. By the end of this book, you will have comprehensive knowledge of advanced topics such as Apache Spark support, machine learning using Elasticsearch and scikit-learn, and real-time analytics, along with the expertise you need to increase business productivity, perform analytics, and get the very best out of Elasticsearch.

Who is this book for?

This book is for Elasticsearch developers and data engineers who want to take their basic knowledge of Elasticsearch to the next level and use it to build enterprise-grade distributed search applications. Prior experience of working with Elasticsearch will be useful to get the most out of this book.

What you will learn

Pre-process documents before indexing in ingest pipelines
Learn how to model your data in the real world
Get to grips with using Elasticsearch for exploratory data analysis
Understand how to build analytics and RESTful services
Use Kibana, Logstash, and Beats for dashboard applications
Get up to speed with Spark and Elasticsearch for real-time analytics
Explore the basics of Spring Data Elasticsearch, and understand how to index, search, and query in a Spring application

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50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

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Product Details

Publication date : Aug 23, 2019

Length: 560 pages

Edition : 1st

Language : English

ISBN-13 : 9781789957754

Category :

Data

Languages :

Java

Concepts :

Enterprise Search

Tools :

Elasticsearch

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24 Chapters

Section 1: Fundamentals and Core APIs

Overview of Elasticsearch 7

Preparing your environment

Running Elasticsearch

Talking to Elasticsearch

Elasticsearch architectural overview

Migration between versions

Summary

Index APIs

Index management APIs

Advanced index management APIs

Summary

Document APIs

The Elasticsearch document life cycle

Single document management APIs

Multi-document management APIs

Migration from a multiple mapping types index

Summary

Mapping APIs

Dynamic mapping

Meta fields in mapping

Field datatypes

Mapping parameters

Refreshing mapping changes for static mapping

Typeless APIs working with old custom index types

Summary

Anatomy of an Analyzer

An analyzer's components

Search APIs

Indexing sample documents

Search APIs

Query DSL

The multi-search API

Other search-related APIs

Summary

Section 2: Data Modeling, Aggregations Framework, Pipeline, and Data Analytics

Modeling Your Data in the Real World

The Investor Exchange Cloud

Modeling data and the approaches

Practical considerations

Summary

Aggregation Frameworks

ETF historical data preparation

Aggregation query syntax

Matrix aggregations

Metrics aggregations

Bucket aggregations

Pipeline aggregations

Post filter on aggregations

Summary

Preprocessing Documents in Ingest Pipelines

Ingest APIs

Accessing data in pipelines

Processors

Conditional execution in pipelines

Handling failures in pipelines

Summary

Using Elasticsearch for Exploratory Data Analysis

Business analytics

Operational data analytics

Sentiment analysis

Summary

Section 3: Programming with the Elasticsearch Client

Elasticsearch from Java Programming

Overview of Elasticsearch Java REST client

The Java low-level REST client

The Java high-level REST client

Spring Data Elasticsearch

Summary

Elasticsearch from Python Programming

Overview of the Elasticsearch Python client

The Python low-level Elasticsearch client

The Python high-level Elasticsearch library

Summary

Section 4: Elastic Stack

Using Kibana, Logstash, and Beats

Overview of the Elastic Stack

Running Elasticsearch in a Docker container

Running Kibana in a Docker container

Running Logstash in a Docker container

Running Beats in a Docker container

Summary

Working with Elasticsearch SQL

Overview

Getting started

Elasticsearch SQL language

Elasticsearch SQL REST API

Elasticsearch SQL JDBC

Summary

Working with Elasticsearch Analysis Plugins

What are Elasticsearch plugins?

Working with the ICU Analysis plugin

Working with the Smart Chinese Analysis plugin

Working with the IK Analysis plugin

Summary

Section 5: Advanced Features

Machine Learning with Elasticsearch

Machine learning with Elastic Stack

Machine learning using Elasticsearch and scikit-learn

Summary

Spark and Elasticsearch for Real-Time Analytics

Overview of ES-Hadoop

Apache Spark support

Real-time analytics using Elasticsearch and Apache Spark

Summary

Building Analytics RESTful Services

Building a RESTful web service with Spring Boot

Integration with the Bollinger Band

Building a Java Spark ML module for k-means anomaly detection

Testing Analytics RESTful services

Working with Kibana to visualize the analytics results

Summary

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Customer reviews

Rating distribution

3.5

(4 Ratings)

5 star 50%

4 star 0%

3 star 25%

2 star 0%

1 star 25%

Dan Aug 18, 2020

I just got the book today. I'm only at page 25 and I'm already frustrated by the quality of the diagrams. The book uses blurry screen shots for diagrams, Elasticsearch input and output (Postman). The textual information provided thus far is informative and I'll update this entry once I've finished the book. But I'm already miffed at paying US$50 for a new book only to get crappy images.

Amazon Verified review

prashanth - Hyderabad Mar 01, 2020

A must have who wants to understand in depth. Recommended for developers. Kindle edition is good.

Amazon Verified review

Kevin Dec 06, 2019

I had a look at the contents and the number of pages and bought the kindle version. I did so especially since the credentials of the author were so impressive. The book reads like a Quick / Get started tutorial. The author offers little or no technical insight, design principles etc. Coverage of topics are at the bare minimum. I found a better instructional video on linkedin/learning. Will never buy another book from the publisher. I spent $42 on this book and find more value in the free online Elasticsearch online documentation. Note to self...stick to manning books!

Amazon Verified review

Bill Sep 29, 2019

Although the book title suggests advanced readers, it actually starts from the beginning making no assumption the reader with any prior knowledge of elasticsearch. I knew very little about elasticsearch although I have used Lucene engine years ago. I also used Kibana interface for logs in OpenShift, but did not know how elasticsearch worked behind. This book helped me greatly understanding how elasticsearch is used in real life.The first part of the book covers in great detail about all the elasticsearch APIs. The dataset is real from TD Ameritrade. I particularly like the screenshot from Postman performing a real search. The research result and format are very helpful in understanding the API. All parameters are listed and explained in a table view. Although it may look tedious by piling all the APIs and parameters, it can be used as a handbook - book serves two purposes!This book provides a lot of examples for real use. We may not need to use all of them. But if we ever have a need to use elasticsearch we can certainly find an example close to our real situation. I am particularly interested in the topic of machine learning with elasticsearch inspired by reading the advanced section in section 5.

Amazon Verified review

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About the author

Wong

Dr. Timothy Wong is a 30 years IT veteran. He holds a PhD in networking from University of Manchester Institute of Science and Technology, UK. He has deep experience in wireless and wireline networking, digital a/v, software development, sales and consulting. He frequently works with telcos, banks, governments and enterprises. He is also a professor on Big Data, Wireless and IoT at Humber College, Toronto, Canada. As an entrepreneur, he co-founded a number of companies in the past 20 years. He infuses business and technical knowledge to pursue successes.

See other products by Wong

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Advanced Elasticsearch 7.0: A practical guide to designing, indexing, and querying advanced distributed search engines

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