Book Image

Advanced Elasticsearch 7.0

By : Wong
Book Image

Advanced Elasticsearch 7.0

By: Wong

Overview of this book

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.
Table of Contents (25 chapters)
Free Chapter
1
Section 1: Fundamentals and Core APIs
8
Section 2: Data Modeling, Aggregations Framework, Pipeline, and Data Analytics
13
Section 3: Programming with the Elasticsearch Client
16
Section 4: Elastic Stack
20
Section 5: Advanced Features

Summary

In this chapter, we used different techniques from Elasticsearch to gain insight about data modeling using real-world examples from IEX. We used the same examples to practice with different techniques. From the general recommendation, using data denormalization can provide easier management and maintenance and compose queries for search.

In the next chapter, we will introduce the aggregation framework. Aggregation can be thought of as a technique to build analytics on a set of documents. The framework consists of many building blocks that can be combined to build a complex summary of the data selected by the search query. There are many different types of aggregations, and we will cover the common methods, such as metric aggregation, bucket aggregation, pipeline aggregation, and matrix aggregation.