From f3126fed7f464c64c5ac26f2e86d1b7425ec1c0a Mon Sep 17 00:00:00 2001 From: Colleen McGinnis Date: Wed, 12 Feb 2025 12:35:26 -0600 Subject: [PATCH] write intro page for the es data store section --- manage-data/data-store.md | 54 ++++----------------------------------- 1 file changed, 5 insertions(+), 49 deletions(-) diff --git a/manage-data/data-store.md b/manage-data/data-store.md index f5e1612a5..a50cc4bc3 100644 --- a/manage-data/data-store.md +++ b/manage-data/data-store.md @@ -3,56 +3,12 @@ mapped_pages: - https://www.elastic.co/guide/en/elasticsearch/reference/current/elasticsearch-intro-what-is-es.html --- -# The Elasticsearch data store [elasticsearch-intro-what-is-es] +# The Elasticsearch data store -[{{es}}](https://github.com/elastic/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 {{es}} to search, index, store, and analyze data of all shapes and sizes in near real time. +[{{es}}](https://github.com/elastic/elasticsearch/) is a distributed search and analytics engine, scalable data store, and vector database built on Apache Lucene. -{{es}} is the heart of the [Elastic Stack](../get-started/the-stack.md). Combined with [{{kib}}](https://www.elastic.co/kibana), it powers the following Elastic solutions: +The documentation in this section details how {{es}} works as a _data store_ starting with the fundamental unit of storage in Elasticsearch: the index. An index is a collection of documents uniquely identified by a name or an alias. Read more in [Index basics](/manage-data/data-store/index-basics.md) and [Index types](/manage-data/data-store/index-types.md). -* [Observability](https://www.elastic.co/observability) -* [Search](https://www.elastic.co/enterprise-search) -* [Security](https://www.elastic.co/security) +Then, learn how these documents and the fields they contain are stored and indexed in [Mapping](/manage-data/data-store/mapping.md), and how unstructured text is converted into a structured format that’s optimized for search in [Text analysis](/manage-data/data-store/text-analysis.md). -::::{tip} -{{es}} has a lot of features. Explore the full list on the [product webpage](https://www.elastic.co/elasticsearch/features). - -:::: - - -::::{admonition} What is the Elastic Stack? -:name: elasticsearch-intro-elastic-stack - -{{es}} is the core component of the Elastic Stack, a suite of products for collecting, storing, searching, and visualizing data. [Learn more about the Elastic Stack](../get-started/the-stack.md). - -:::: - - - -## Use cases [elasticsearch-intro-use-cases] - -{{es}} 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. - -**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 {{es}} 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. - -**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. - -This is just a sample of search, observability, and security use cases enabled by {{es}}. Refer to Elastic [customer success stories](https://www.elastic.co/customers/success-stories) for concrete examples across a range of industries. +You can also read more about working with {{es}} as a data store including how to use [aliases](/manage-data/data-store/aliases.md) to point to multiple indices, how to use [index templates](/manage-data/data-store/templates.md) to tell {{es}} how to configure an index when it is created, and how to use the [command line to manage data](/manage-data/data-store/manage-data-from-the-command-line.md) stored in {{es}}. \ No newline at end of file