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
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

Unbelievable! We have completed the study of Spark and Elasticsearch for real-time analytics via ES-Hadoop. We started with the basic concepts of Apache Hadoop. We learned how to configure ES-Hadoop for Apache Spark support. We read the data from Elasticsearch, processed it, and then wrote it back to Elasticsearch. We learned about the find_anomalies() function, which is a real-time anomaly detection routine based on the k-means model, which was created from past data using the Spark MLlib. This can tell you whether the input data is an anomaly.

The next chapter is the final chapter of this book. We will use Spring Boot to build a RESTful API to provide search and analytics backed by Elasticsearch. We will revisit what we have learned before and glue it together to make a real-world use case project. Finally, we will visualize the results produced by the project by using...