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+---
+navigation_title: Elasticsearch
+description: An introduction to Elasticsearch. 
+applies_to:
+  serverless: all
+  stack: all
+products:
+  - id: elasticsearch
+---
+
+# {{es}} overview [elasticsearch-overview]
+
+{{es}} is a distributed data store that ingests, indexes, and manages diverse data types in near real time, making your data searchable and analyzable. Built on Apache Lucene, {{es}} scales horizontally across multiple nodes to handle large data volumes while maintaining fast query performance.
+
+{{es}} enables you to make large amounts of data quickly searchable. Whether you’re building an e-commerce product search, implementing semantic search with AI, or analyzing log data, {{es}} provides a powerful foundation with efficient indexing and query capabilities.
+
+## Key concepts [elasticsearch-key-concepts]
+
+### Distributed architecture [elasticsearch-distributed-architecture]
+
+{{es}} distributes data across multiple nodes within a cluster. Each node stores a portion of the data in shards, which are self-contained indexes that can reside on any node in the cluster. 
+
+The distributed {{es}} architecture enables the following:
+
+* **Horizontal scaling** — Add more nodes to increase capacity
+* **High availability** — Maintained through data replication across nodes to prevent data loss
+* **Parallel processing** — Queries execute across shards simultaneously to deliver fast performance
+
+### Near real-time indexing [elasticsearch-near-real-time-indexing]
+
+When you send documents to {{es}}, they become searchable within about one second. The near real-time capability makes {{es}} ideal for applications that require immediate data availability. 
+
+For example:
+
+* Live dashboards display currently collected system metrics 
+* Product catalogs that instantly update as inventory changes
+* User-generated content that appears in search results the moment the content is created
+
+### Schema-on-write with dynamic mapping [elasticsearch-schema-on-write-with-dynamic-mapping]
+
+When you index documents, {{es}} automatically detects the field types. For example, when a document includes a `price` field with a `29.99` value, {{es}} infers that the value is a floating-point number. You can also define explicit mappings to control exactly how data is stored and indexed. 
+
+Mappings play a key role in the following:
+
+* Storage and query performance optimization
+* Specific search feature enablement, such as autocomplete or geospatial search
+* Data consistency across documents
+
+### Vector capabilities [elasticsearch-vector-capabilities]
+
+{{es}} functions as a vector database for AI and {{ml}} applications, storing dense vector embeddings alongside traditional text and numeric data. 
+
+Vector capabilities enable the following:
+
+* **Semantic search** —— Find content based on meaning rather than exact keywords
+* **Hybrid search** —— Combine keyword and vector-based search results for greater accuracy
+* **Retrieval-augmented generation (RAG) systems** —— Provide relevant context to large language models
+
+## How {{es}} works [how-elasticsearch-works]
+
+To enable fast and scalable search, {{es}} ingests, analyzes, and indexes data so queries execute across shards and return results in milliseconds. 
+
+![How Elasticsearch works](/get-started/images/how-elasticsearch-works.png)
+
+
+### Storage model [elasticsearch-storage-model]
+
+{{es}} stores data in indices, which are collections of documents with similar characteristics. Each document is a JSON object with fields. 
+
+For example:
+
+```console
+{
+  "product_id": "abc123",
+  "name": "Wireless Headphones",
+  "price": 79.99,
+  "category": "Electronics",
+  "in_stock": true,
+  "description": "High-quality wireless headphones with noise cancellation"
+}
+```
+
+To enable fast full-text search, {{es}} creates inverted indexes that map each unique term to the documents that containin the term.
+
+### Query execution [elasticsearch-query-execution]
+
+To search your data, {{es}} uses distributed query execution.
+
+When you search, {{es}}:
+
+1. Parses your query (e.g., "wireless headphones under $100")
+2. Determines the shards that contain the matching documents
+3. Executes the query on each relevant shard in parallel
+4. Scores results by relevance
+5. Merges and sorts results from all shards
+6. Returns the top results
+
+## Use cases [elasticsearch-use-cases]
+
+{{es}} is ideal for uses cases that require fast search and analysis across large datasets.
+
+### Full-text and hybrid search [elasticsearch-full-text-hybrid-search]
+
+* **E-commerce product catalogs** — Fast product discovery with filters, facets, and autocomplete
+* **Enterprise knowledge bases** — Search across documents, wikis, and databases with permission controls
+* **Content platforms** — Search articles, videos, and user-generated content by relevance
+
+### AI-powered applications [elasticsearch-ai-powered-applications]
+
+* **Semantic search** — Find documents by meaning using vector embeddings from models like BERT or OpenAI
+* **Chatbots and RAG systems** — Retrieve relevant context from knowledge bases to enhance LLM responses
+* **Recommendation engines** — Surface similar items based on vector similarity
+
+### Geospatial search [elasticsearch-geospatial-search]
+
+* **Location-based services** — Find nearby restaurants, stores, or services
+* **Delivery routing** — Optimize routes based on geographic data
+* **Geofencing** — Trigger actions when users enter specific areas
+
+### Analytics and monitoring [elasticsearch-analytics-monitoring]
+
+* **Log analytics** — Centralize and analyze application and system logs
+* **Security analytics** — Detect threats and anomalies in security events
+* **Business metrics** — Analyze user behavior, sales trends, and KPIs
+
+## When to use {{es}} [when-to-use-elasticsearch]
+
+Use {{es}} when you need:
+
+* Fast search across large volumes of text, numeric, or vector data
+* Complex queries with filters, aggregations, and relevance scoring
+* Near real-time access to data
+* Scalability to handle growing datasets
+* Flexibility to manage diverse data types and evolving schemas
+
+Consider alternatives to {{es}} when:
+
+* You require transactional guarantees and complex joins across multiple entities, which are better handled by relational databases
+* Strong consistency is more important than eventual consistency
+* Your datasets are small, such as under 1GB, where simpler solutions suffice
+
+## Architecture considerations [elasticsearch-architecture-considerations]
+
+### Deployment options [elasticsearch-deployment-options]
+
+{{es}} offers flexible deployment options to match your organization requirements and level of operational control. 
+
+| Option | Description |
+| ----- | ----- |
+| **[{{serverless-full}}](/deploy-manage/deploy/elastic-cloud/serverless.md)** | Fully managed, auto-scaling deployment. Recommended for new projects. |
+| **[{{ech}}](/deploy-manage/deploy/elastic-cloud/cloud-hosted.md)** | Managed {{es}} with more configuration control. |
+| **[Self-managed](/deploy-manage/deploy/self-managed.md)** | Install and operate {{es}} yourself. Requires expertise. |
+
+### Cluster sizing [elasticsearch-cluster-sizing]
+
+To make sure your {{es}} deployment performs efficiently and scales with your data and query demands, use the right cluster size. 
+
+| Deployment size | Cluster size |
+| ----- | ----- |
+| **Small** | 3-5 nodes for development and small production use cases. |
+| **Medium** | 10-20 nodes for moderate data volumes and query loads. |
+| **Large** | 50 or more nodes for high-volume production systems. |
+
+### Data modeling best practices [elasticsearch-data-modeling-best-practices]
+
+* **One document type per index** — Keep related data together
+* **Denormalize data** — Include related information in documents to avoid joins
+* **Use appropriate field types** — Match data types to query patterns
+* **Plan for growth** — Consider time-based indices for logs and events
+
+## Next steps [elasticsearch-next-steps]
+
+Ready to try {{es}}? 
+
+Here's how to get started:
+
+* [Get started](/solutions/search/get-started.md) — Run your first queries in 5 minutes
+% how* Tutorial: Build a search application — Create a full-featured search experience
+* [Understanding {{es}} architecture](/deploy-manage/distributed-architecture.md) — Deep dive into distributed systems concepts
+
+For specific use cases:
+
+* [Implementing semantic search](/solutions/search/get-started/semantic-search.md) — Add AI-powered search
+* [Building geospatial applications](/explore-analyze/geospatial-analysis.md) — Work with location data
+* [Analyzing logs and metrics](/solutions/observability/get-started.md) — Set up observability
\ No newline at end of file
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diff --git a/get-started/toc.yml b/get-started/toc.yml
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--- a/get-started/toc.yml
+++ b/get-started/toc.yml
@@ -1,6 +1,7 @@
 project: 'Get started'
 toc:
   - file: index.md
+  - file: elasticsearch.md
   - file: introduction.md
   - file: the-stack.md
   - file: deployment-options.md