Python stream processing made simple for InfluxDB

Use Quix to downsample time-series data before it is written to InfluxDB V2 or V3. Quickly build reliable alerting pipelines using declarative window operators and DataFrames.

Inside the product

The data processing engine for InfluxDB

Process Quix line.

Built for processing data by time-series experts

Quix was founded by ex-McLaren Formula 1 engineers who built and managed their Telemetry Analytics Platform with InfluxDB.

Having an easy to use yet powerful data processing solution to pair with InfluxDB was the missing piece.

Quix has been tested to efficiently and reliably stream and process over 60,000 fields per measurement at kilohertz frequency per Field.


Work with a lightweight python library optimised for InfluxDB

Timestamps, Fields (we call them Values) and Tags. Quix Streams provides a high performance stream processing library with a time-series data model familiar to InfluxDB developers. Combined with InfluxDB 3.0, you can leverage Quix as a task-based engine to process your data in an automated workflow.


InfluxDB V2 to V3 migration made easy

Quix provides fully managed source and sink connectors for InfluxDB V2 and V3 to reduce the complexity of migrating between versions.

Quix also integrates natively with Telegraf, MQTT and OpenTelemetry so you can quickly build ingestion, ETL and alerting pipelines.

Get started in 3 steps:

1. Create a free Quix account.

2. Configure and deploy the InfluxDB Source connector to ingest data.

3. Add a Transformation from our samples library to start processing your data with your code.

Architecture diagram for InfluxDB and Quix

At InfluxData, we prioritize ‘time to awesome’ when developing InfluxDB, aiming to empower developers to quickly transition from beginners to experts and create impactful solutions. Quix perfectly aligns with this core value, offering an exceptional user experience and a seamlessly scalable platform right from the start.

One of the features I appreciate the most is the built-in pandas DataFrame support. This functionality is invaluable for efficiently handling bulk transformations and enrichments of time series data within a multistage data pipeline, providing both power and simplicity.

For those venturing into the realm of event streaming applications, or for anyone looking to construct a scalable task engine armed with Python’s power and flexibility for manipulating time series data, I wholeheartedly recommend Quix to the community.

Jay Clifford
Developer Advocate at InfluxData

Related content

Webinar Recording: Simplify Stream Processing with Python, Quix, and InfluxDB
Watch our webinar with InfluxData on-demand. Quix CTO & Co-Founder Tomáš Neubauer demonstrates building a crash detection application using Quix and InfluxDB.
Quix Community Plugins for InfluxDB: Build Your Own Streaming Task Engine
Read InfluxData's post about the release of two InfluxDB Community plugins for Quix. They run through what each plugin does and demonstrate an Industrial IoT use case that you can try out yourself.
Read the Documentation for our InfluxDB Source Connector
Jump into our docs and learn how you can get started by connecting InfluxDB to Quix.

It’s free to get started

Sign up now and start building your first event streaming app with free credits to use in compute and streaming.