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The Distributed SQL Database for
Real-Time Analytics, Search, and AI

From Ingest to Insights, in Milliseconds.
Any Data, Any Scale, with SQL.

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"We needed a solution that could watch, record and analyze production in real time. CrateDB gives us the freedom to be cumulative and scale limitless - we found no alternative solution with such simplicity and efficiency.”
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"Working with CrateDB brings positive outcomes. The ingestion and throughput have very good performance, with 1 million values/sec, the horizontal scalability where we can add as many nodes as we need and the automatic query distribution across the whole cluster."
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"CrateDB allows us to do real-time dashboards on very big streaming and historic datasets in a simple way. We can scale the system easily as we grow the load and customers and have it all done with SQL."
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"Having a standardized SQL language is a big advantage with CrateDB. That makes it very easy for people to access this data and work with it in different tools like Grafana or Tableau."
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CrateDB is a lead in the time series database category on G2 winter 2026
G2 badge for Best Relationship for CrateDB Winter 2026 in the time series database category

CrateDB is a distributed, real-time analytics database with exceptional support for high-cardinality, multi-dimensional data using SQL, without pre-aggregation or schema rewrites.

Execute complex aggregations, ad-hoc joins, hybrid search, and AI feature queries in seconds through a powerful distributed SQL engine.

Real-Time Analytics at Scale

CrateDB is built from the ground up for real-time analytics on fast-moving data. It ingests high-velocity streams and makes them queryable in milliseconds, keeping dashboards and applications responsive even as data grows to billions of records.

High-Cardinality and Dimensionality

CrateDB handles high-cardinality dimensions without performance penalties. You can freely analyze data by device, customer, tenant, region, or any other dimension to uncover precise insights, isolate anomalies, and understand long-tail behavior in real time.

Analytics, Search, and Vectors

CrateDB unifies analytics, full-text search, and semantic search in a single distributed SQL engine. Teams can aggregate, explore, and feed data to AI models without moving data between systems or maintaining complex pipelines.

Simple to Operate, Built to Be Resilient

CrateDB removes the operational burden typically associated with large-scale analytics. With automatic indexing, built-in replication, and shared read/write nodes, it delivers predictable performance and high availability without constant tuning or re-architecture.
 

SQL for All Data Types

CrateDB keeps SQL at the center, even for modern data. Teams can query time series, JSON, geospatial, text, relational and vector data using familiar SQL, reducing learning curves and enabling faster adoption across engineering and data teams.

Designed for Change without Downtime

CrateDB is built for evolving requirements. New data fields, dimensions, and use cases can be introduced without redesigning schemas or pipelines and without downtime, allowing teams to adapt analytics instantly as products, customers, and business questions change.

Real-Time Unified Data Layers

A New Era for Scalable Analytics, Search, and AI

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Successful Companies Using CrateDB

Enhanced Developer Productivity

Boost your developer productivity with native SQL for simple queries and quick onboarding. Analyze relational, JSON, time-series, geospatial, full-text, and vector data within a single system. Query across many dimensions with confidence: no need for upfront flattening, manual feature pipelines, or dimensional compromises.

PostgreSQL compatibility ensures easy integration with third-party tools, enhancing compatibility and migration. Utilize the vector store to seamlessly integrate with AI/ML tools and LangChain, allowing you the freedom to choose your LLM and embedding algorithms.

The power and flexibility of the open-source licensing model liberates you from vendor lock-in, and provides support from the growing developer community.

 
        

/* Based on device data, this query returns the average
 * of the battery level for every hour for each device_id
 */
WITH avg_metrics AS (
    SELECT device_id,
       DATE_BIN('1 hour'::INTERVAL, time, 0) AS period,
       AVG(battery_level) AS avg_battery_level
    FROM devices.readings
    GROUP BY 1, 2 
    ORDER BY 1, 2
)
SELECT period,
       t.device_id,
       manufacturer,
       avg_battery_level  
FROM avg_metrics t, devices.info i
WHERE t.device_id = i.device_id 
      AND model = 'mustang'
LIMIT 10;


        
        
        

+---------------+------------+--------------+-------------------+
|    period     |  device_id | manufacturer | avg_battery_level |
+---------------+------------+--------------+-------------------+
| 1480802400000 | demo000001 |    iobeam    | 49.25757575757576 |
| 1480806000000 | demo000001 |    iobeam    | 47.375            |
| 1480802400000 | demo000007 |    iobeam    | 25.53030303030303 |
| 1480806000000 | demo000007 |    iobeam    | 58.5              |
| 1480802400000 | demo000010 |    iobeam    | 34.90909090909091 |
| 1480806000000 | demo000010 |    iobeam    | 32.4              |
| 1480802400000 | demo000016 |    iobeam    | 36.06060606060606 |
| 1480806000000 | demo000016 |    iobeam    | 35.45             |
| 1480802400000 | demo000025 |    iobeam    | 12                |
| 1480806000000 | demo000025 |    iobeam    | 16.475            |
+---------------+------------+--------------+-------------------+
Learn more about time-series data 
        
 
/* Return the name and truncated description for the 5 Chicago community
   areas with populations over 50,000 people. */
SELECT name, 
       details['population'] AS population, 
       concat(left(details['description'], 25), '...') AS description 
FROM community_areas 
WHERE details['population'] > 50000 
ORDER BY details['population'] DESC
LIMIT 5;


        
        
        

+-----------------+------------+------------------------------+
| name            | population | description                  |
+-----------------+------------+------------------------------+
| NEAR NORTH SIDE |     105481 | The Near North Side is th... |
| LAKE VIEW       |     103050 | Lakeview, also spelled La... |
| AUSTIN          |      96557 | Austin is one of 77 commu... |
| WEST TOWN       |      87781 | West Town, northwest of t... |
| BELMONT CRAGIN  |      78116 | Belmont Cragin is one of ... |
+-----------------+------------+------------------------------+
Learn more about JSON data 
        

SELECT text, _score
FROM word_embeddings
WHERE knn_match(embedding,[0.3, 0.6, 0.0, 0.9], 2)
ORDER BY _score DESC; 


        
        
        

|------------------------|--------|
|         text           | _score |
|------------------------|--------|
|Discovering galaxies    |0.917431|
|Discovering moon        |0.909090|
|Exploring the cosmos    |0.909090|
|Sending the mission     |0.270270|
|------------------------|--------|
Learn more about vector data 
        

SELECT show_id, title, director, country, release_year, rating, _score
FROM "netflix_catalog"
WHERE MATCH(title_director_description_ft, 'title^2 Friday') USING best_fields 
AND type='Movie' 
ORDER BY _score DESC;


        
        
        

+---------+------------------------------------+-------------------+----------------------+--------------+--------+-----------+
| show_id | title                              | director          | country              | release_year | rating | _score    |
+---------+------------------------------------+-------------------+----------------------+--------------+--------+-----------+
|  s1674  | Black Friday                       | Anurag Kashyap    | India                | 2004         | TV-MA  | 5.6455536 |
|  s6805  | Friday the 13th                    | Marcus Nispel     | United States        | 2009         | R      | 3.226806  |
|  s1038  | Tuesdays & Fridays                 | Taranveer Singh   | India                | 2021         | TV-14  | 3.1089375 |
|  s7494  | Monster High: Friday Night Frights | Dustin McKenzie   | United States        | 2013         | TV-Y7  | 3.0620003 |
|  s3226  | Little Singham: Mahabali           | Prakash Satam     | NULL                 | 2019         | TV-Y7  | 3.002901  |
|  s8233  | The Bye Bye Man                    | Stacy Title       | United States, China | 2017         | PG-13  | 2.9638999 |
|  s8225  | The Brawler                        | Ken Kushner       | United States        | 2019         | TV-MA  | 2.8108454 |
+---------+------------------------------------+-------------------+----------------------+--------------+--------+-----------+
Learn more about text data 
        

/* Using 311 data from the City of Chicago, this query returns 5 open
   work orders for locations closest to the Willis Tower. */
SELECT srnumber, 
       srtype, 
       locationdetails['streetaddress'] AS address, 
       distance(
           'POINT(-87.636256 41.8786492)'::GEO_POINT,
           locationdetails['location']
       ) / 1000 AS distance_km
FROM three_eleven_calls 
WHERE status != 'Completed'
ORDER BY distance_km ASC
LIMIT 5;


        
        
        

+---------------+-----------------------------------------------+--------------------+---------------------+
| srnumber      | srtype                                        | address            |         distance_km |
+---------------+-----------------------------------------------+--------------------+---------------------+
| SR24-00711535 | Cab Feedback                                  | 200 S WACKER DR    | 0.09800707616741176 |
| SR24-00694851 | No Building Permit and Construction Violation | 300 W ADAMS ST     | 0.1346164665090538  |
| SR24-00651822 | Sign Repair Request - All Other Signs         | 111 SW WACKER DR   | 0.20355339153863516 |
| SR24-00608464 | Building Violation                            | 235 W VAN BUREN ST | 0.26374860571526554 |
| SR24-00608655 | Building Violation                            | 235 W VAN BUREN ST | 0.26374860571526554 |
+---------------+-----------------------------------------------+--------------------+---------------------+
Learn more about geospatial data

Streamlined Operations

Experience a cost-efficient, robust, and scalable architecture that delivers high performance at any scale. Eliminate the hassle of combining and synchronizing different databases, reducing overhead, and minimizing your carbon footprint.

Ensure high availability with automatic failover, recovery, and replication, keeping your data safe and accessible. The resilient architecture detects failures and maintains cluster health, offering peace of mind even in distributed environments.

Choose from multiple deployment models: DBaaS, hybrid cloud, of self-managed, providing flexibility to meet your operational needs, even for Edge deployment with limited connectivity. Whether you're running on a single laptop or dozens of servers with terabytes of data, seamlessly scale from prototype to production.

Upcoming Events

January 29, 2026 14:00 - 14:30 CET
Webinar

From Sensors to Dashboards: Building Real-Time Analytics Pipelines That Actually Work

In this session, Gregor Bauer (VP Customer Engineering) and Stephane Castellani (SVP Marketing) will break down why a modern real-time pipeline...

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March 6, 2026 09:00 - 12:30 CET
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