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Database of Databases

Database Entry

Warp 10


Warp 10 is an open-source time series DBMS to collect, store and analyze sensors and time series data.

Source Code
https://github.com/senx/warp10-platform[02]
Twitter
@warp10io
Developer
Country of Origin
FR
Start Year
2013 [06]
Project Types
Commercial, Open Source
Written in
Java
Operating System
All OS with Java VM
License
Apache v2

Shaped for the Internet of Things (IoT), it supports geolocated data in its core model (called Geo Time Series).

Warp 10 offers both a time series database (TSDB) and an analysis environment. The two components can be used together or independently.

The Warp 10 Analytics Engine is based on a library of more than 1200 functions adapted to time series and on two analysis languages, WarpScript and FLoWS. This environment makes it possible in particular to perform statistics, extraction of characteristics for training models, filtering and cleaning of data, detection of patterns and anomalies, synchronization or even forecasts.

The analysis environment can be implemented within a large ecosystem of software components such as Spark, Kafka Streams, Hadoop, Jupyter or Zeppelin. It can also access data stored in many existing solutions, relational or NoSQL databases, search engines and S3 type object storage system.

SenX, the creator of Warp 10, offers support and assistance to secure the transition to production.

Database Entry

Warp 10


Warp 10 is an open-source time series DBMS to collect, store and analyze sensors and time series data.

Shaped for the Internet of Things (IoT), it supports geolocated data in its core model (called Geo Time Series).

Warp 10 offers both a time series database (TSDB) and an analysis environment. The two components can be used together or independently.

The Warp 10 Analytics Engine is based on a library of more than 1200 functions adapted to time series and on two analysis languages, WarpScript and FLoWS. This environment makes it possible in particular to perform statistics, extraction of characteristics for training models, filtering and cleaning of data, detection of patterns and anomalies, synchronization or even forecasts.

The analysis environment can be implemented within a large ecosystem of software components such as Spark, Kafka Streams, Hadoop, Jupyter or Zeppelin. It can also access data stored in many existing solutions, relational or NoSQL databases, search engines and S3 type object storage system.

SenX, the creator of Warp 10, offers support and assistance to secure the transition to production.

Query Interface[05]


Warp 10 uses a proprietary query language called WarpScript.

Citations

6 sources
  1. Warp 10 - The Most Advanced Time Series Platform warp10.io
  2. GitHub - senx/warp10-platform: The Most Advanced Time Series Platform · GitHub github.com
  3. Warp 10 - Warp 10 Documentation warp10.io
  4. https://fr.wikipedia.org/wiki/Warp_10 wikipedia.org Dead — Check Archive
  5. Warp 10 - WarpScript warp10.io
  6. Initial GitHub commit. · senx/warp10-platform@3b67c57 · GitHub github.com
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