Course · Training · Workshop

Elasticsearch for Software Engineers

Two to three days of hands-on training to understand Elasticsearch as distributed cluster software and integrate powerful search into your Java application.

Dive into how Elasticsearch works and learn how to integrate powerful search capabilities into your applications.

In-house course

Tailored for your team.

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Trainers

Benjamin Habegger

Instructor

Adam Furmańczuk

Instructor

What participants say

Hands-on approach with great practice environment directly integrated in their Gitlab CI/CD Pipeline. We were able to directly implement the theory with the system without worrying about consequences. Great approach!

Benedikt
Financial Analyst in AML / CFT MROS

These customers booked courses in the same topic cluster.More customers →

Content

Part 1: Elasticsearch ecosystem

- Elasticsearch, Kibana, Logstash, Beats and their functionalities - understand the most important terms (clusters, nodes, shards, replication)

Part 2: Data indexing and queries

- get started with Elasticsearch from the Dev Console using the REST API - JSON documents and indexing, CRUD operations (Create, Read, Update, Delete) - understand simple search queries and filters, query language and query DSL - advanced query techniques such as aggregations, buckets, histograms

Part 3: Elasticsearch integration into my (Java) application

- clients for different languages (Java, JavaScript, Go, .NET, ...) - connection to the cluster - the Java API Client - indexing documents, bulk operation for many documents - read documents, search documents - aggregations (sum, average, histogram, etc.)

The actual course content may differ from the above depending on the trainer, delivery, duration and the composition of participants.

Goal

In this course, you'll learn how to effectively integrate and use Elasticsearch in your (Java) application. The course provides an in-depth look at how Elasticsearch works as distributed cluster software and introduces you to the developer tools.

Duration

2 or 3 Days

Format

The course consists of theory blocks, demos, and practical exercises. At the end of the course, you will have access to a repository with many running examples that you can use as a reference for your own projects.

Target group

Software developers who want an efficient start to programming with Elasticsearch and want to get an overview of the Elasticsearch ecosystem.

Prerequisites

You should have solid basic knowledge in Java and experience with an IDE of your choice. The course examples are in Java, but can be applied to other supported languages (JavaScript, Go, .NET, ...).

Preparation

Each participant receives a questionnaire and installation instructions after registration. Depending on the course, we provide a suitable laboratory environment.

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More about Elasticsearch

Elasticsearch is a distributed, RESTful search and analytics engine built on Apache Lucene. It provides horizontal scalability, real-time search, and support for various data types through its schema-free JSON document model.

Further resources:

History

Elasticsearch was developed in 2010 by Shay Banon and was based on his earlier work on Compass. The project emerged from the need to create a scalable search platform for real-time document search. The first version was released as open-source software.

Development was accelerated by the founding of Elastic (originally Elasticsearch) by Banon and David Pilato. A major milestone was the introduction of the ELK Stack (Elasticsearch, Logstash, Kibana) for logging and analytics. The integration of Beats as lightweight data shippers further expanded the platform.

Today, Elasticsearch is the most widely used search engine and is employed by companies like Wikipedia, Netflix, and GitHub. It has evolved from a pure search engine to a comprehensive analytics platform. The introduction of features like machine learning, observability, and security has further strengthened the platform. The development of the Elastic Stack (formerly ELK Stack) has set new standards for logging, monitoring, and analytics.