Elasticsearch for Software Engineers 

Course & Training

A short, in-depth introduction to programming with Elasticsearch

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

In-House Course:

We are happy to conduct tailored courses for your team - on-site, remotely or in our course rooms.

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Public Course Schedule:

   

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.)


Disclaimer: The actual course content may vary from the above, depending on the trainer, implementation, duration and constellation of participants.

Whether we call it training, course, workshop or seminar, we want to pick up participants at their point and equip them with the necessary practical knowledge so that they can apply the technology directly after the training and deepen it independently.

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.


Form:

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 Audience:

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


Requirements:

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.

Request In-House Course:

In-House Kurs Anfragen

Waitinglist for public course:

Sign up for the waiting list for more public course dates. Once we have enough people on the waiting list, we will determine a date that suits everyone as much as possible and schedule a new session. If you want to participate directly with two colleagues, we can even plan a public course specifically for you.

Waiting List Request

(If you already have 3 or more participants, we will discuss your preferred date directly with you and announce the course.)

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.




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.





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