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


Bravo! We have tried the major components of the Elastic Stack. You should now understand the basic concepts of the powerful Elastic Stack. We ran an example on Kibana to visualize some sample flight data from Elasticsearch. We also learned how to use Logstash to collect and parse log data from the system log file. We extended the use of Logstash as a central log-processing center by using Filebeat. We also played with the popular deployment technique of running the applications using the officially supported Elastic Stack Docker images.

In the next chapter, we will introduce Elasticsearch SQL. Yes, Elasticsearch also speaks SQL. You will learn the SQL semantics supported in Elasticsearch. You will also perform a SQL REST API with SQL statements. Also, you will work with JDBC (Java Database Connectivity), the software industry standard for databases.