Real-Time Unified Data Layers: A New Era for Scalable Analytics, Search, and AI

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Your Unified Data Layer
for Analytics, Search, and AI

From Ingest to Insights in Real Time

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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 leader in Time Series Databases on G2
Users love CrateDB on G2

Real-Time Data Insights

Gain immediate access to real-time data and unlock the ability to make decisions in milliseconds. Whether it's responding to emerging trends, detecting anomalies, or optimizing workflows, CrateDB ensures data-driven actions happen at the speed of your business.

Turbocharged Aggregations

Run ad-hoc queries on billions of records in milliseconds, uncovering actionable insights at unprecedented speed. Powered by columnar storage, CrateDB delivers ultra-fast aggregations, enabling instant exploration of complex data relationships.

Hybrid Search Powered by Lucene 

Perform full-text, geospatial, vector similarity, and hybrid searches, seamlessly combining results from diverse data types and the most complex datasets, thanks to a fully distributed SQL query engine built on Apache Lucene.


Seamless AI Model Integration

Effortlessly integrate AI models to store, search, and query vectors, enabling real-time training, predictions, and decision-making. With this closed-loop system of continuous optimization, the database evolves alongside your business needs, enhancing AI-driven workflows.

 

Dynamic Schema and Indexing

Adapt seamlessly to evolving data types and schema changes without interrupting operations. Real-time data is immediately available for querying upon ingestion, even as your data structure shifts. The system dynamically optimizes indexing on the fly, ensuring fast, uninterrupted performance, regardless of fluctuating workloads or schema adjustments.

Built for Scale and Resilience

Manage massive data volumes, from millions of records to petabytes, while ensuring high-speed performance. Fault-tolerant, distributed architecture guarantees uninterrupted insights, scaling effortlessly to meet growing demands—even under heavy workloads or failures.

CrateDB: Your Unified Data Layer for Analytics, Search, and AI

Real-Time Unified Data Layers

A New Era for Scalable Analytics, Search, and AI

Download now

Real-Time Unified Data Layers

A New Era for Scalable Analytics, Search, and AI

Download the white paper

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.

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.

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