Elasticsearch with Python is a course in using Elasticsearch 9 through the Python client, from managing documents and designing mappings and Thai analysers to searching with Query DSL, summarising data with aggregations and ES|QL, storing logs and running vector search. It suits Python developers, system administrators and DBAs who need fast search or log analytics.
Elasticsearch is a distributed search and analytics engine built on Apache Lucene. It powers search inside applications, log analytics and vector storage for AI work, while Kibana is used to explore and visualise the data. Both have moved on a long way in Elastic Stack 9, and version 9 of the Python client now ships the DSL module in the same package, so Python developers can build search and data work on Elasticsearch in one place.
This course takes learners through Elasticsearch 9 from Python, from installing it with Docker and managing documents with Python Client 9 to designing mappings and analysers, including Thai, searching with Query DSL, tuning results, and summarising data with aggregations and ES|QL. It continues with sending ECS-formatted logs from Python into data streams with ILM, exploring them in Kibana and running vector search, and closes with a workshop that builds a search API with FastAPI. (3 days, 6 hours per day, 18 hours in total, Intermediate level.)
Course Details
This course is the 2026 edition of Elasticsearch with Python, updated for Elastic Stack 9 and Python Client 9. It runs for 3 days, 6 hours per day (18 hours in total, 09:00-16:00), as lectures with labs built on one continuous dataset throughout. Intermediate level. Day one covers installation and managing data from Python, mappings and Thai analysers. Day two covers searching, tuning results,
aggregations and ES|QL. Day three covers logging from Python, data streams with ILM, Kibana and vector search, with a workshop that builds a search API with FastAPI. Every lab runs on the learner's own machine with Docker. The course focuses on Python; developers working in .NET have a separate Elasticsearch and Kibana with .NET course. Learners take home a lab guide, source code and docker-compose files.
Section 1: Lab: Elastic Stack 9 and Installing with Docker
Section 2: Lab: Managing Documents with Python Client 9
Section 3: Reads, Writes and Concurrency
Section 4: Lab: Designing Mappings
Section 5: Text Analysis and Thai
Section 6: Lab: Term-Level Queries and the Python DSL
Section 7: Full-Text Search and Relevance
Section 8: Nested Objects and Joins
Section 9: Lab: Controlling and Improving Search Results
Section 10: Lab: Aggregations and ES|QL
Section 11: Lab: Writing Logs from Python to Elasticsearch
Section 12: Data Streams, ILM and Index Maintenance
Section 13: Lab: Exploring and Visualising with Kibana
Section 14: Vector Search and Semantic Search
Section 15: Workshop: A Search API with FastAPI
No public rounds are open right now. Join the waiting list and we will contact you first when the next round opens, or ask us on LINE. Or call 02-570-8449 or 088-807-9770
A mapping defines an index's structure: what type each field is and how it is stored. A text field goes through an analyser that splits it into terms, which suits full-text search on names or descriptions. A keyword field stores the whole value as is, which suits exact filtering, sorting and aggregations on codes or categories. In practice a multi-field often gives one field both forms.
Install version 9 of the elasticsearch package and create a client with your URL and API key. Call index to add one document at a time, or helpers.bulk for large volumes. To search, call search with Query DSL, or use the Search class in elasticsearch.dsl, which is part of the version 9 client. If your app makes many concurrent calls, for example on FastAPI, use AsyncElasticsearch.
Aggregations summarise large amounts of data in a single request, for example counts, sums and averages, or grouping by category, price range or time period. They drive reports, dashboards and facet filters on search pages. Elasticsearch also offers ES|QL, a readable piped query language that you can call from Python and load into pandas. The course practises both.
It suits Python developers who want to add search or analytics to their apps, system administrators who store and search logs, and DBAs or data engineers who look after data in Elasticsearch. You should have working Python skills, understand JSON and REST APIs, and be comfortable with basic command line and Docker. Please bring a laptop with 16 GB of RAM or more that can run Docker Desktop.
You will be able to install Elasticsearch 9 and connect to it from Python, design mappings and analysers that handle Thai, write queries and tune result ranking, summarise data with aggregations and ES|QL, write ECS-formatted logs from Python with data streams and ILM, build Kibana dashboards and run vector search. You also take home a FastAPI search API project to build on.
Yes. IT Genius Institute runs in-house training for organisations. Examples and labs can be adapted to your team's systems, such as product or Thai document search, or application log storage and search, and the emphasis can lean towards search or logging as needed. Contact the institute to request a quotation.
Articles are published in Thai.
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