
Install Elasticsearch and Kibana 8.x on Mac, Windows, and Linux by downloading, extracting, and running the bin files; configure Kibana using the enrollment token.
Explore Kibana overview and DevTools use for querying Elasticsearch, learn to import sample data, create and navigate dashboards, and monitor index health through stack management.
Explore essential Elasticsearch and Kibana configurations, including Elasticsearch.yml settings for cluster and node names, data and log paths, memory, network hosts and ports, and Kibana integration.
Identify a node as an Elasticsearch instance and a cluster as its container. Documents reside as JSON inside indices with mappings, aliases, and settings.
Elasticsearch uses replicas to provide fault tolerance by duplicating primary shards across different nodes, enabling replicas to serve searches and boost performance; configure replica counts at index creation.
Index a document in Elasticsearch using post, automatically creating the products index if needed, and learn how to set a custom id with put and retrieve the doc.
Learn how to retrieve a document from the products index using the get method by its id, viewing fields under _doc, and contrast known versus random generated ids.
Learn how Elasticsearch 8 updates documents using the update API, which replaces the existing document with new field changes, adding fields like stock and tags while preserving the ID.
Learn to perform script updates in Elasticsearch 8, using _source, params, and conditional logic to modify fields such as in_stock with subtraction and dynamic values.
Learn how upsert updates an existing document or inserts a new one in the index using a script, demonstrated with a product stock example.
Delete a doc shows deleting a document by id 1001 in Elasticsearch, noting versioning after delete and recreate, and reinforces how to create, get, delete, and update documents.
When people ask, “what is Elasticsearch?”, some may answer that it’s “an index”, “a search engine”, an “analytics database”, “a big data solution”, that “it’s fast and scalable”, or that “it’s kind of like Google”. Depending on your level of familiarity with this technology, these answers may either bring you closer to an ah-ha moment or further confuse you. But the truth is, all of these answers are correct and that’s part of the appeal of Elasticsearch. Over the years, Elasticsearch and the ecosystem of components that’s grown around it called the “Elastic Stack” has been used for a growing number of use cases, from simple search on a website or document, collecting and analyzing log data, to a business intelligence tool for data analysis and visualization
Elasticsearch is a distributed, open-source search and analytics engine built on Apache Lucene and developed in Java. It started as a scalable version of the Lucene open-source search framework then added the ability to horizontally scale Lucene indices. Elasticsearch allows you to store, search, and analyze huge volumes of data quickly and in near real-time and give back answers in milliseconds. It’s able to achieve fast search responses because instead of searching the text directly, it searches an index. It uses a structure based on documents instead of tables and schemas and comes with extensive REST APIs for storing and searching the data. At its core, you can think of Elasticsearch as a server that can process JSON requests and give you back JSON data.
This is the best overview/starter point for those who are keen to learn about elasticsearch and kibana.
By the end of this course you should be able to interact with elasticsearch and tweak it to some extend.