Welcome
Quix is a data platform for consolidating measurement data, configuration metadata, and test parameters from test rigs, simulations, and sensors. The platform handles high-frequency telemetry data in real-time and provides the ability to replay historical runs for analysis and debugging.
This documentation includes guides, tutorials, and API references for Quix Cloud, Quix Streams (the open-source Python library), and Bring Your Own Cluster (BYOC) deployment options.
Quix Features
Data consolidation: Connect sensors, data acquisition systems, MATLAB/Simulink outputs, and legacy instruments through pre-built connectors or custom Python code. All data streams to a centralized data store.
Configuration and measurement linkage: Store test configurations alongside sensor measurements. Query by test parameters to find specific runs, or trace measurement anomalies back to their exact test setup.
Real-time processing and historical replay: Process live telemetry as it arrives, then replay historical data later to investigate issues or validate changes without re-running physical tests.
Python-based pipeline development: Use Quix Streams to build data processing pipelines in Python. Develop and test locally, then deploy to managed infrastructure without requiring Kafka or DevOps expertise.
Typical Workflow
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Develop locally: Build Python data processing pipelines with Quix Streams, your IDE, and the Quix CLI. Test with Docker before deployment.
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Debug with replay: Replay historical test runs locally to reproduce issues or validate fixes.
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Deploy to production: Push pipelines to Quix Cloud for managed infrastructure, monitoring, and data persistence. Alternatively, use BYOC to run on your own Kafka cluster.
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Get started
Install Quix Streams and build your first data processing pipeline.
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