Elasticsearch and Kibana

Elasticsearch is a distributed search and analytics engine, scalable data store, and vector database built on Apache Lucene. It’s optimized for speed and relevance on production-scale workloads. Use Elasticsearch to search, index, store, and analyze data of all shapes and sizes in near real time.

You can deploy Elasticsearch as a standalone service to build custom search and analytics solutions or deploy it together with other Elastic products, using various deployment options.

Explore the full list of Elasticsearch features on the product webpage.

To learn more about the internals of the data store, refer to The Elasticsearch data store.

Kibana is the graphical user interface for Elasticsearch. It’s a powerful tool for visualizing and analyzing your data, and for managing and monitoring the Elastic Stack.

Together, Elasticsearch and Kibana form the core of the Elastic Stack.

They power all Elastic solutions and use cases:

• Observability
• Security
• Search

The Elastic Stack is used for a wide and growing range of use cases. Here are a few examples:

Observability

• Logs, metrics, and traces: Collect, store, and analyze logs, metrics, and traces from applications, systems, and services.
• Application performance monitoring (APM): Monitor and analyze the performance of business-critical software applications.
• Real user monitoring (RUM): Monitor, quantify, and analyze user interactions with web applications.
• OpenTelemetry: Reuse your existing instrumentation to send telemetry data to the Elastic Stack using the OpenTelemetry standard.

Security

• Security information and event management (SIEM): Collect, store, and analyze security data from applications, systems, and services.
• Endpoint security: Monitor and analyze endpoint security data.
• Threat hunting: Search and analyze data to detect and respond to security threats.

Search

• Full-text search: Build a fast, relevant full-text search solution using inverted indexes, tokenization, and text analysis.
• Vector database: Store and search vectorized data, and create vector embeddings with built-in and third-party natural language processing (NLP) models.
• Semantic search: Understand the intent and contextual meaning behind search queries using tools like synonyms, dense vector embeddings, and learned sparse query-document expansion.
• Hybrid search: Combine full-text search with vector search using state-of-the-art ranking algorithms.
• Build search experiences: Add hybrid search capabilities to apps or websites, or build enterprise search engines over your organization’s internal data sources.
• Retrieval augmented generation (RAG): Use Elastic Cloud as a retrieval engine to supplement generative AI models with more relevant, up-to-date, or proprietary data for a range of use cases.
• Geospatial search: Search for locations and calculate spatial relationships using geospatial queries.

This is just a sample of search, observability, and security use cases enabled by Elastic Cloud. Refer to Elastic customer success stories for concrete examples across a range of industries.