CrateDB makes it simple to ingest and analyze massive amounts of data. Companies can extract the full value of the data – whether for new innovative digital solutions or operational improvements across the business functions.
Variety
Readily handle structured and unstructured data, regardless of data shape
Real-time
Run ad hoc queries in real-time - no waiting necessary
Run anywhere & everywhere
Run on-premise or in the cloud, any cloud
Aggregation
Condense and distill to get what you need and when you need it
Simple Scalability
Unique architecture that’s simple to scale and keeps running 24/7 with no bottlenecks
SQL & NoSQL
Index high data volumes with a distributed SQL DBMS built atop NoSQL storage
Variety
Readily handle structured and unstructured data, regardless of data shape
Real-time
Run ad hoc queries in real-time - no waiting necessary
Run anywhere & everywhere
Run on-premise or in the cloud, any cloud
Aggregation
Condense and distill to get what you need and when you need it
Simple Scalability
Unique architecture that’s simple to scale and keeps running 24/7 with no bottlenecks
SQL & NoSQL
Index high data volumes with a distributed SQL DBMS built atop NoSQL storage
PostgreSQL
Use standard SQL without the need to learn new skills
/*
* Based on sensor data, this query calculates:
* - time-buckets of 10 seconds
* - different aggregations per time-bucket and host group
*/
SELECT FLOOR(EXTRACT(epoch FROM m.timestamp) / 10) * 10 AS period,
h.host_group,
MIN(m.fields['usage_user']) AS "min",
AVG(m.fields['usage_user']) AS "avg",
MAX(m.fields['usage_user']) AS "max"
FROM telegraf.metrics m
LEFT JOIN telegraf.hosts h ON h.host_name = m.tags['host']
WHERE tags['cpu'] = 'cpu-total'
AND m.timestamp > NOW() - '150 seconds'::INTERVAL
GROUP BY 1, 2
ORDER BY 1 DESC;
/*
* Based on a given observation timeframe, this query calculates:
* - the observed value's ratio compared to the previous value
* - the average over a moving window of the last 100 rows
* - the maximum value during the same hour of observation
*/
SELECT s.time,
distance,
1 - (
LAG(distance) OVER (ORDER BY s.time ASC) / distance
) AS ratio,
AVG(distance) OVER (
ORDER BY s.time ASC ROWS BETWEEN 100 PRECEDING AND CURRENT ROW
) AS moving_avg,
MAX(distance) OVER (
PARTITION BY DATE_TRUNC('hour', s.time)
) AS hourly_max
FROM GENERATE_SERIE(
DATE_TRUNC('minute', NOW() - '10 minutes'::INTERVAL),
DATE_TRUNC('minute', NOW()), '1 minute'::INTERVAL
) s(time)
LEFT JOIN doc.raw r USING (time)
ORDER BY 1 ASC;
/*
* Based on ride-hailing data, this query calculates:
* - the distance of a given dropoff location to a fixed point of interest,
* partitioned in buckets of 50 meters each
* - the average trip distance in kilometers within that partition
* - the number of trips within that partition
*/
SELECT FLOOR(
DISTANCE(t.dropoff_location, 'POINT(-73.984 40.758)') / 50
) * 50 AS point_distance,
AVG(
DISTANCE(t.pickup_location, t.dropoff_location) / 1000.0
) AS trip_distance,
COUNT(*) AS trips
FROM nyc_taxi.trips t
WHERE t.pickup_month = DATE_TRUNC('month', CURDATE())
GROUP BY 1
ORDER BY 1 ASC;
/*
* 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
"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."
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.
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.
