Elasticsearch Engineer training course

Learn how to use Elasticsearch to store, search, and analyze huge volumes of data quickly

JBI training course London UK

"Good introduction to Apache Spark. The trainer was great at talking us through the information, specifically optimisation methods. He spoke slowly and concisely which really got his points across. He effectively tailored the course to our specifications which we also appreciated."

RL, Financial Crime Technologist, Apache Spark, April 2021

Public Courses

17/08/26 - 4 days
£2500 +VAT
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28/09/26 - 4 days
£2500 +VAT
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09/11/26 - 4 days
£2500 +VAT
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Customised Courses

* Train a team
* Tailor content
* Flex dates
From £1200 / day
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JBI training course London UK
  • Data Modeling
  • Data Management
  • Data Processing
  • Cluster Management
  • Developing Search Applications
  • Shards

Course Details

Getting started

Learn how Elasticsearch and the components of
the Elastic Stack work together to solve a myriad of use cases and real-world problems.
Understand the type of data Elasticsearch users typically work with. Learn the many ways to send data into Elasticsearch. Learn the basics of how Kibana can be used to search and analyze data.

You know, for search

There are many ways to search using
Elasticsearch — Elasticsearch’s own Query DSL, the new and easy-to-use EQL, Elasticsearch SQL, or Kibana’s KQL, just to name a few. Learn when and where to use which search language and how to search across multiple clusters.
Build some basic queries and aggregations and compose more complex search requests,
including some that use Painless scripting or
search templates.

Data modeling

Use a mapping to configure how Elasticsearch should store and access data. Learn about commonly used field data types and some specialized types designed for various use cases in search or analytics. Learn about
the Elastic Common Schema and how to use it. Learn about runtime fields, analyzers, and how to build custom analyzers. Learn to use tools like dynamic templates and dynamic field mapping to automate mapping
creation.

Data processing

Mappings are great for optimizing data storage. But if the data itself needs to be changed, it passes through a data processor before it gets indexed.
Learn about different tools in the Elastic Stack that can be used to process data and in which situation you should use them. Learn about two important use cases for processing data and what tools to use for them: enriching data to denormalize relational data and converting event-centric data to entity-centric data.

The one about shards

Data in indices is stored in one or more shards. Learn how shards make distributed search possible and how replica shards can be used for high availability and to improve search performance. Learn about cross-cluster replication, which can be used for disaster recovery but also for other scenarios like centralized reporting and data  localization. Learn when to scale your cluster up or down.

Data management

Learn how to manage indices, from creation to migration to cold storage and backup. See how features like data streams, index templates, andindex lifecycle management  can be used to automate the whole process.

Cluster management

Learn how to optimize search performance by debugging complex queries or improving slow search requests. Learn about search and filter
contexts. Learn about relevance and relevance tuning. Learn about different cluster topologies and in which scenarios they can be used.
Learn how to understand the health of your cluster, diagnose health issues, and troubleshoot. Learn about various cluster deployment types and different considerations for each.

Developing search applications

Learn to build your own search application using some of the most popular supported client libraries like NodeJS and Python.

JBI training course London UK

Attendees should have the following :

  • Familiarity with Python and basic programming concepts
  • Basic knowledge of SQL, including writing queries
5 star

4.8 out of 5 average

"Good introduction to Apache Spark. The trainer was great at talking us through the information, specifically optimisation methods. He spoke slowly and concisely which really got his points across. He effectively tailored the course to our specifications which we also appreciated."

RL, Financial Crime Technologist, Apache Spark, April 2021



“JBI  did a great job of customizing their syllabus to suit our business  needs and also bringing our team up to speed on the current best practices. Our teams varied widely in terms of experience and  the Instructor handled this particularly well - very impressive”

Brian F, Team Lead, RBS, Data Analysis Course, 20 April 2022

 

 

JBI training course London UK

Certification


Every delegate will be entitled to a certificate of achievement on completion of the course.

If you are missing your certificate - please use the link below to apply - you can also use this link to sign up for the JBI Training newsletter to receive technology tips directly from our instructors - Analytics, AI, ML, DevOps, Web, Backend and Security.
 



More about this course

This Elasticsearch course is designed for both new Elasticsearch users and Elasticsearch professionals. It begins with the basics for getting started with the Elastic Stack, then quickly dives deep into topics ranging from optimizing search performance to building efficient clusters. You will learn about various ways to ingest and process data, how to write complex search requests and work with their responses, strategies for scaling your clusters up or down, managing indices in large clusters and multiple clusters, general cluster management and troubleshooting recommendations, and building your own custom search application that uses Elasticsearch on the back end

FAQs

JBI Training offers two courses in this group. Elasticsearch Engineer is a four-day comprehensive programme covering the full Elasticsearch stack for engineers who need to deploy, manage, and query Elasticsearch in production. Data Analysis with Kibana is a two-day course focused on using Kibana — Elasticsearch's visualisation and analytics layer — for data exploration, dashboard creation, and operational monitoring. Both courses are available as scheduled classroom sessions in London, as live online instructor-led training, or as customised onsite programmes for engineering and data teams.
Elasticsearch is an open-source, distributed search and analytics engine built on Apache Lucene and developed by Elastic. It is designed to store, search, and analyse large volumes of data in near real-time. It is widely used for full-text search across applications and websites, log and event data analysis, security information and event management (SIEM), application performance monitoring, e-commerce product search, and operational intelligence dashboards. Elasticsearch is the core component of the Elastic Stack (also known as the ELK Stack), which combines Elasticsearch with Logstash (data ingestion), Kibana (visualisation), and Beats (lightweight data shippers).
The four-day Elasticsearch Engineer course provides comprehensive coverage of Elasticsearch for engineers responsible for deploying, administering, and querying Elasticsearch in real environments. Topics include the Elasticsearch architecture and cluster model, indexing and mapping data, the Elasticsearch Query DSL for search and aggregation, data ingestion using Logstash and Beats, cluster management and administration, performance tuning and optimisation, security configuration, snapshot and restore for backup, and scaling Elasticsearch for production workloads. The course includes hands-on labs throughout and is suited to DevOps engineers, backend developers, data engineers, and infrastructure professionals who work with or are adopting Elasticsearch.
The two-day Data Analysis with Kibana course focuses on using Kibana — the visualisation and analytics front-end for the Elastic Stack — to explore, analyse, and present data stored in Elasticsearch. Topics covered include the Kibana interface and Discover tool for data exploration, creating visualisations including bar charts, line charts, maps, and metric panels, building and sharing interactive dashboards, using Kibana Lens for intuitive drag-and-drop analytics, working with time-series data, Canvas for pixel-perfect visual reporting, and an introduction to Kibana's machine learning and alerting features. It is suited to data analysts, operations teams, and DevOps professionals who need to derive insights and build monitoring dashboards from Elasticsearch data.
Traditional relational databases such as MySQL, PostgreSQL, and SQL Server store structured data in tables with defined schemas and are optimised for transactional workloads — inserting, updating, and retrieving records with precise queries. Elasticsearch is a document store optimised for full-text search, unstructured or semi-structured data, and analytical queries across very large datasets in near real-time. It excels at searching across free-text fields, handling variable data structures, and aggregating across millions of documents quickly. Many organisations use both — a relational database for transactional data and Elasticsearch as a search and analytics layer on top of that data, often populated via a data pipeline using Logstash or Beats.
Yes. Both courses can be delivered as customised onsite or online programmes for corporate engineering and data teams. Content and lab exercises can be tailored to the team's existing Elastic Stack version, deployment environment — whether on-premises, on Elastic Cloud, or hosted on AWS, Azure, or Google Cloud — and specific use cases such as log analytics, security monitoring, application search, or operational dashboards. JBI has delivered Elasticsearch and DevOps-related training for engineering teams at organisations including the BBC, NHS, RBS, Sky, EDF, and Cisco.
Yes. The Elastic Stack is updated regularly with new features across Elasticsearch, Kibana, Logstash, and Beats, and JBI's training content is continuously reviewed to reflect the latest stable release. This includes updates to Kibana Lens and the Kibana interface, new Elasticsearch query and aggregation capabilities, developments in Elastic's AI and machine learning features — including semantic search and vector search capabilities that are increasingly central to modern Elasticsearch deployments — and changes to Elastic's cloud and licensing model. Delegates learn skills that are current and applicable to the version of the Elastic Stack in use in their organisation today.

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