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80 changes: 9 additions & 71 deletions docs/index.md
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# Welcome to CrateDB

CrateDB is a **distributed SQL database** designed for **real-time analytics
and search** at scale. Whether you are working with time series data, full-text
CrateDB is a **distributed SQL database** designed for **real-time analytics,
search and AI** at scale. Whether you are working with time series data, full-text
search, or large volumes of structured and semi-structured data, CrateDB gives
you the **power of SQL**, the **scalability of NoSQL**, and the **flexibility
of a modern data platform**.

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See our [Product overview] to learn more about high-level features and use cases.

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:::{rubric} Why CrateDB?
:::
CrateDB was built for speed, scale, and simplicity:

* **Real-time performance:** Query millions of records per second with sub-second response times.
* **AI/ML-ready:** Store and serve data for modern AI pipelines.
* **Search + SQL**: Combine full-text search with rich SQL queries.
* **Geospatial & time series**: Native support for IoT, sensor data, and location-based use cases.
* **Horizontal scalability**: Add nodes effortlessly to handle more data and users.
* **Resilient and fault-tolerant**: Built-in replication and recovery.
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:::{rubric} What Can You Build?
:::
CrateDB is used across industries to power:

* Real-time **dashboards and analytics**
* Hybrid **search and retrieval experiences**
* Large-scale **IoT telemetry and analytics**
* Complex **geospatial applications**
* **AI-powered features** embedded in your apps
* **Industrial IoT** data backends
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:::{rubric} Benefits and Features
:::

Whether you are a developer,
database administrator, or just starting your journey with CrateDB, our
documentation provides the information and knowledge needed to build
real-time analytics and hybrid search applications that leverage CrateDB's
unique features.

* In a unified data platform, CrateDB lets you analyze relational, JSON,
time series, geospatial, full-text, and vector data in a single system,
eliminating the need for multiple databases.
* The fully distributed SQL query engine, built on top of Apache Lucene,
and inheriting technologies from Elasticsearch/OpenSearch, provides performant
aggregations and advanced SQL features like JOINs and CTEs on large datasets
of semi-structured data.
* Real-time indexing automatically indexes all columns, including nested
structures, as data is ingested, eliminating the need to worry about
indexing strategy.
* The flexible data schema dynamically adapts based on the data you ingest,
offering seamless integration and instant readiness for analysis.
* Columnar storage enables fast query and aggregation performance.
* PostgreSQL wire protocol compatibility and an HTTP interface provide versatile
integration capabilities.
* AI-ready: The vector store subsystem integrates well
with an extensive third-party ecosystem of AI/ML frameworks for advanced data
analysis and data-driven decisions.
This documentation is about helping you get started, explore in practice, and
operate in details.


## Get Started
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:::

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:link: index
:link: handbook
:link-type: ref
:link-alt: The CrateDB Guide
:link-alt: The CrateDB Handbook
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The CrateDB Guide
The CrateDB Handbook
^^^
{material-outlined}`hiking;3.5em`
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Don't miss the relevant [CrateDB customer stories].
::::


[Product overview]: https://cratedb.com/database
[CrateDB customer stories]: https://www.youtube.com/playlist?list=PLDZqzXOGoWUJrAF_lVx9U6BzAGG9xYz_v
[HTTP protocol]: https://en.wikipedia.org/wiki/HTTP
[JDBC]: https://en.wikipedia.org/wiki/Java_Database_Connectivity
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