Stream high-frequency test data to the cloud

Send measurement data from your DAQ systems directly to Quix Cloud for centralized storage and analysis across all test campaigns.

Automatically enrich data with test configurations

Join sensor measurements with test metadata in real-time, so every data point has full context about which setup, asset, or parameters were used.

Process with Python, not proprietary tools

Use familiar Python-based transformations and analytics instead of vendor-specific software, enabling custom workflows and ML integration.

How to consume Measurement Computing data with Quix

Measurement Computing provides industrial-grade data acquisition systems for test and measurement applications. From high-speed sampling to multi-channel synchronous data collection, Measurement Computing delivers reliable instrumentation solutions for demanding industrial environments.

Quix is a Python-first platform for industrial data management that enables test engineers to:

Ingest measurement data from Measurement Computing data acquisition systems
Normalize and enrich sensor data with configuration metadata
Process time-series data in real-time with custom Python transformations
Store test data centrally in a queryable data lakehouse
Monitor production processes and equipment across multiple facilities

Quix integrates with Measurement Computing systems via standard protocols including MQTT, OPC UA, and REST APIs. Data is automatically associated with device configurations and stored centrally—eliminating manual data collection and enabling real-time insights across distributed industrial systems.

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.
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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.
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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

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Process data from Measurement Computing DAQs with Quix