Founded by Formula 1 Engineers, Quix makes Python stream processing simple for time series data. It allows developers to ingest data from any source and write to any destination, leveraging the Python ecosystem to reduce, enrich, and transform their data.

Data processing engine for time series data

Quix and InfluxData

Quix integrates directly with Influx as either a source or sink—serving as an ETL engine for users of 3.x or 2.x of InfluxDB who want to leverage stream processing for their use cases. With Kafka as the data backbone of the platform, Quix enables users to stream data directly to InfluxDB as their time series data store, taking advantage of its scale and performance by powering real-time applications or pre-processing their data before storing it.

Source, Transformation, and Sink. The Quix Streams DataFrame interface enables users to process their data in Python. Data teams can build task-based engines, downsampling capabilities, scalable time series data alerting pipelines, and ML deployments for time series forecasting by combining the two technologies.

In Quix, you can migrate between versions of InfluxDB (2.x -> 3.x). Quix resembles Flux tasks in 2.x, making it easier for InfluxDB customers to migrate to 3.x and convert their Flux tasks to Python services in Quix.


How to get started with Quix and InfluxDB

Quix is packaged to run in any cloud or on-prem environment, with serverless cloud available for free trials. Simply create an account and set up the source or sink connectors needed for your version of InfluxDB. From there, you can create a real-time data processing engine.


  1. Create a free Quix account.
  2. Configure and deploy the InfluxDB source connector to ingest data or use Telegraf to get data into Quix.
  3. Add a transformation from the Quix samples library to start processing your data with your code. Check out this predictive maintenance project for inspiration.


Key resources


Diagram of sample Quix and Influx Architecture

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