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
Modernize your data architecture
Simplify your data architecture with CrateDB. The database for data-intense analytics and AI applications that require fast data ingest, high-performance queries, and elastic scaling with a familiar SQL interface.
CrateDB is data-shape agnostic
Ready to handle structured and unstructured data
/*
* Based on sensor data, this query calculates:
* - time-buckets of 10 seconds
* - different aggregations per time-bucket and host group
*/
SELECT DATE_BIN('10 seconds'::INTERVAL, m.timestamp, 0) 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() - '5 minutes'::INTERVAL
GROUP BY 1, 2
ORDER BY 1 DESC;
/*
* Based on a date dimension, this query calculates:
* - the observed value, carrying forward the last known value
* - 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,
COALESCE(
distance,
LAG(distance) IGNORE NULLS OVER (ORDER BY s.time ASC)
) AS 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_SERIES(
DATE_TRUNC('minute', NOW() - '10 minutes'::INTERVAL),
DATE_TRUNC('minute', NOW()),
'1 minute'::INTERVAL
) s(time)
LEFT JOIN doc.raw r ON r.time >= s.time
AND r.time < s.time + '1 minute'::INTERVAL
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
Senior Data Scientists
Selecting, configuring and implementing analytics solutions
Cost effective scaling
No upskilling required
Make real-time decisions
"CrateDB is an important part of our data stack giving us the performance and horizontal scalability to meet our rapidly growing business needs."
Sekhar Sarukkai Chief Scientist, McAfee
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
Gartner® Honorable Mention 2021
Crate.io named as an Honorable Mention in 2021 Gartner® Magic Quadrant™ for Cloud Database Management Systems
Industries
CrateDB's value spans across several industries and segments
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
Download 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.
