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{{es}} and {{kib}} [introduction]

What is {{es}}?

{{es}} 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 {{es}} to search, index, store, and analyze data of all shapes and sizes in near real time.

You can deploy {{es}} 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 {{es}} features on the product webpage.

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

::::{tip} Want to get started quickly with the {{es}} API? Check out our hands-on quick start tutorials and Python notebooks. ::::

What is {{kib}}?

{{kib}} is the graphical user interface for {{es}}. It’s a powerful tool for visualizing and analyzing your data, and for managing and monitoring the Elastic Stack.

Together, {{es}} and {{kib}} form the core of the Elastic Stack.

They power all Elastic solutions and use cases:

Use cases

The {{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 {{ecloud}} 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 {{ecloud}}. Refer to Elastic customer success stories for concrete examples across a range of industries.

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