Query structured and semi-structured data. Benefit from full-text and geo-spatial indexing
Real-time
Everything is indexed, run ad hoc queries in real-time. Explore your data in the moment
Run anywhere & everywhere
Run on-premise from a single to hundreds of nodes and deploy for production to our managed cloud
Fast Aggregation
Instantly aggregate across billions of records, adjust and iterate to get the results you need
Simple Scalability
Shared-nothing architecture that’s simple to scale. Run worry free with built-in high availability
SQL & NoSQL
Index high data volumes with a distributed SQL DBMS built atop Apache Lucene
Variety
Query structured and semi-structured data. Benefit from full-text and geo-spatial indexing
Real-time
Everything is indexed, run ad hoc queries in real-time. Explore your data in the moment
Run anywhere & everywhere
Run on-premise from a single to hundreds of nodes and deploy for production to our managed cloud
Fast Aggregation
Instantly aggregate across billions of records, adjust and iterate to get the results you need
Simple Scalability
Shared-nothing architecture that’s simple to scale. Run worry free with built-in high availability
SQL & NoSQL
Index high data volumes with a distributed SQL DBMS built atop Apache Lucene
PostgreSQL
Use standard SQL without the need to learn new skills and use your favorite tools out of the box
DATA INNOVATION SUMMIT 2023
Why ABB selected CrateDB for their Industrial analytics platform
CrateDB is data-shape agnostic
Ready to handle structured and unstructured data
/* 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;
/* Based on reports from IoT devices, this query returns
* the voltage corresponding to the maximum
* global active power for each meter_id
*/
SELECT meter_id,
MAX_BY("Voltage", "Global_active_power") AS voltage_max_global_power
FROM iot.power_consumption
GROUP BY 1
LIMIT 10;
/* Based on the location of the International Space Station,
* this query returns the 10 closest capital cities from
* the last known position
*/
SELECT city as "City Name",
country as "Country",
DISTANCE(i.position, c.location)::LONG / 1000 AS "Distance [km]"
FROM demo.iss i
CROSS JOIN demo.world_cities c
WHERE capital = 'primary'
AND ts = (SELECT MAX(ts) FROM demo.iss)
ORDER BY 3 ASC
LIMIT 10;
+--------------+-----------------------------------+---------------+
| City Name | Country | Distance [km] |
+--------------+-----------------------------------+---------------+
| Papeete | French Polynesia | 3386 |
| Avarua | Cook Islands | 3708 |
| Wellington | New Zealand | 4565 |
| Alofi | Niue | 4628 |
| Nuku‘alofa | Tonga | 4887 |
| Pago Pago | American Samoa | 5063 |
| Santiago | Chile | 5112 |
| Apia | Samoa | 5182 |
| Stanley | Falkland Islands (Islas Malvinas) | 5266 |
| Suva | Fiji | 5611 |
+--------------+-----------------------------------+---------------+
/*
* Based on system event logs, this query calculates:
* - a filter for specific messages using a full-text index
* - the number of entries per minute
* - the average scoring ratio for each matched row
*/
SELECT DATE_TRUNC('minute', receivedat) AS event_time,
COUNT(*) AS entries,
AVG(_score) AS avg_score
FROM "syslog"."systemevents"
WHERE MATCH(message, 'authentication failure')
USING most_fields WITH (analyzer = 'whitespace')
AND MATCH(syslogtag, 'sshd')
GROUP BY 1
ORDER BY 1 DESC
LIMIT 10;
From architects and engineers to business leaders, we work to solve data management challenges companies face today
Developers
Develop modern applications and services
Work across any data shape
Fully managed database
Familiar SQL
"The technical discussion with Crate.io engineers paid off, as it helped us to verify the technical and business requirements. CrateDB is an integral part of our big data streaming architecture, and it is delivering as promised."
Kristoffer Axelsson Principal Solution Architect, TCG
Senior Data Scientists
Selecting, configuring and implementing analytics solutions
Cost effective scaling
No upskilling required
Make real-time decisions
"CrateDB is a highly scalable database for time series and event data with a very fast query engine using standard SQL."
Alexander Mann TGW Logistics Group
Business Leaders
Optimize the performance of digital assets
Prevent operational disruptions
Increase equipment efficiency
Decrease total cost of DB ownership
"Tens of thousands of sensors generate data along our production lines, and CrateDB for Azure IoT allows us to analyze it to make real-time changes to factory efficiency."
Philipp Lehner CEO ALPLA Group
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Get started with CrateDB Cloud and launch a 30-day trial cluster. Designed to handle the complexity of high-end time series workloads in real-time, CrateDB Cloud is a fully managed database-as-a-service. Secured, scaled and operated by the engineers that built CrateDB.
Why CrateDB Cloud?
The power of CrateDB
Fully managed service
Secure in the cloud
No lock-in
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CrateDB is the leading open source, distributed SQL database for relational and time‑series data. It combines the familiarity of SQL with the scalability and data flexibility of NoSQL.
