Book Image

Advanced Elasticsearch 7.0

By : Wai Tak Wong
Book Image

Advanced Elasticsearch 7.0

By: Wai Tak 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
Section 1: Fundamentals and Core APIs
Section 2: Data Modeling, Aggregations Framework, Pipeline, and Data Analytics
Section 3: Programming with the Elasticsearch Client
Section 4: Elastic Stack
Section 5: Advanced Features

Machine learning with Elastic Stack

Earlier, one of the main issues related to machine learning while using Elasticsearch is that of solving anomaly detection. It can be traced back to the hot topics discussed in 2014 and earlier (see and Basically, anomaly detection is a statistical problem that can be solved in a simple way, by marking the irregularities from the common statistical properties of the input data distribution. However, we can solve the problem with machine learning-based approaches, such as cluster-based anomaly detection and support vector machine-based anomaly detection. The machine learning feature provided by Elastic Stack can involve...