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 Data Insights at Scale

CrateDB is the leading SQL database for real-time analytics.
Search and analyze across billions of records every second.
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See all our customer success stories
Variety

Variety

Query structured and semi-structured data. Benefit from full-text and geo-spatial indexing

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Real-time

Everything is indexed, run ad hoc queries in real-time. Explore your data in the moment

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Run anywhere & everywhere

Run on-premise from a single to hundreds of nodes and deploy for production to our managed cloud

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Fast Aggregation

Instantly aggregate across billions of records, adjust and iterate to get the results you need

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Simple Scalability

Shared-nothing architecture that’s simple to scale. Run worry free with built-in high availability

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SQL & NoSQL

Index high data volumes with a distributed SQL DBMS built atop Apache Lucene

Variety

Variety

Query structured and semi-structured data. Benefit from full-text and geo-spatial indexing

icon-home-clock

Real-time

Everything is indexed, run ad hoc queries in real-time. Explore your data in the moment

icon-home-anywhere

Run anywhere & everywhere

Run on-premise from a single to hundreds of nodes and deploy for production to our managed cloud

icon-home-folders

Fast Aggregation

Instantly aggregate across billions of records, adjust and iterate to get the results you need

icon-home-scalable

Simple Scalability

Shared-nothing architecture that’s simple to scale. Run worry free with built-in high availability

icon-home-nosql

SQL & NoSQL

Index high data volumes with a distributed SQL DBMS built atop Apache Lucene

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PostgreSQL

Use standard SQL without the need to learn new skills and use your favorite tools out of the box

See all the feature and benefits of CrateDB

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;
        
        

+---------------+------------+--------------+-------------------+
|    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            |
+---------------+------------+--------------+-------------------+
        
        

/* 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;
        
        

+------------+--------------------------+
|   meter_id | voltage_max_global_power |
+------------+--------------------------+
| 840073190N |          233.57          |
| 840072401F |          233.53          |
| 840072655G |          234.1           |
| 840071893D |          234.47          |
| 840073950P |          231.73          |
| 840075260N |          235.51          | 
| 840076398A |          234.56          |
| 84007B071E |          231.94          |
| 840075959Y |          237.21          |
| 840072534A |          231.96          |
+------------+--------------------------+
        
        

/* 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;





        
        

+---------------+---------+--------------------+
|    event_time | entries |          avg_score |
+---------------+---------+--------------------+
| 1620220260000 |       4 | 1.5798743814229965 |
| 1620220200000 |       8 | 1.7750384211540222 |
| 1620220140000 |      10 | 1.6113891124725341 |
| 1620220080000 |       9 | 1.676726798216502  |
| 1620220020000 |       8 | 1.6908064410090446 |
| 1620219960000 |       8 | 1.690401442348957  |
| 1620219900000 |       7 | 1.7646006005150932 |
| 1620219840000 |       7 | 1.7795820917401994 |
| 1620219780000 |      10 | 1.5844267368316651 |
| 1620219720000 |      13 | 1.5637413492569556 |
+---------------+---------+--------------------+








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across an organization

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
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"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."


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Kristoffer Axelsson
Principal Solution Architect, TCG

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Senior Data Scientists

Selecting, configuring and implementing analytics solutions

  • Cost effective scaling
  • No upskilling required
  • Make real-time decisions
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"CrateDB is a highly scalable database for time series and event data with a very fast query engine using standard SQL."

 

Alexander Mann

Alexander Mann
TGW Logistics Group

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Business Leaders

Optimize the performance of digital assets

  • Prevent operational disruptions
  • Increase equipment efficiency
  • Decrease total cost of DB ownership
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"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."

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Philipp Lehner
CEO
ALPLA Group

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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?
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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.

Why CrateDB?
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Runs on-premise

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Simple scalability

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SQL for machine data

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Industrial time series database

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Easy IoT tool integrations

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