The Most Advanced Time Series Platform

Fully Open Source


Getting started

Geo Time Series

The first differentiating factor of Warp 10 is that both space (location) and time are considered first class citizens.

Working with Geo Time Series (GTS) allows you to have geo-located sensor readings without having to use four separate series and having to keep track of the reading context.

Complex searches like "find all the sensors active during last Monday in the perimeter delimited by this geo-fencing polygon" can be done without involving expensive joins between separate time series.

Ecosystem

Plug Warp 10 with your tools and ecosystem.

Docker Nifi R Python Zepplin Jupyter Spark Flink Storm Pig TensorFlow Elastic Nodered Telegraph

At any scale

Edge

Embedded

Designed for IoT, you can embed Warp 10 on your device.

  • Millions of series
  • 100s million of datapoints
  • ~10k datapoints / s

Standalone

Standalone

Meet the power of Warp 10 on your computer or on a single server.

  • 10s million of series
  • 100s of Billion of datapoints
  • ~100k datapoints / s

Distributed

Distributed

Scale the power of Warp 10 on your datacenter.

  • Billions of series
  • Trillions of datapoints
  • Millions of datapoints / s

SAAS

Cloud

No data center? No problem, we can host your data and you can access Warp 10 in the cloud.

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Our Strength

WarpScript
  • Dedicated language
  • 6 frameworks
  • 900 functions
  • Fully extensible
  • Sandboxed

Warp 10 data manipulation environment

We created WarpScript, an extensible data programming language which offers close to 900 functions and several high level frameworks to ease and speed your data analysis. Simply create scripts containing your data analysis code and submit them to the platform, they will execute close to where the data resides and you will get the result of that analysis as a JSON object that you can integrate into your application.

The WarpScript approach is another differentiating factor of Warp 10. Traditional time series platforms offer few manipulation options, usually only providing a SQL like query language which cannot express complex analysis, or providing a reduced set of aggregation functions. These approaches force you to produce more code on the client side thus increasing your development time and leading to massive transfers of unprocessed data from the platform to your applications. Our approach lets you focus on your business use cases, simplifying IoT and sensor data applications by taking care of a larger chunk of the data analysis in a very efficient way.