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Test facilities are stuck in 2010:.• Laptop connected to test rig.• Proprietary desktop app.• Data dumped locally .• Flash drive transfer to "wherever seems logical". #F1 teams are streaming data at scale with fault-tolerant pipelines. Time to modernize your R&D infra.

MQTT gives:.3d_printer_1/bed_temperature: 65.2.3d_printer_1/nozzle_temperature: 210.4. You need:.timestamp | machine | bed_temp | nozzle_temp. Row-to-column is step one. Then comes gaps, timing issues, downsampling, interpolation. Test data pipelines are harder than they look.

Your test data has a configuration problem:. ❌ Send metadata with every sensor message .❌ Send metadata once at startup.✅ Separate time-series from configuration data entirely. Different channels. Different databases. Join only what you need. Architecture decisions compound.

Still transferring test data via flash drives? . F1 teams stream 1,000+ sensor points/second to the cloud. Local ingestion mistakes = data lost forever.Cloud processing mistakes = rerun the pipeline. Your prototype validation work deserves better than the USB graveyard.

🚀 QuixStreams 3.15.0: Time-aware joins for industrial data. Perfect for predictive maintenance, smart grids, and asset tracking. Now with enhanced reliability. pip install --upgrade quixstreams. #IndustrialData.

🚀 Webinar: Build industrial data pipelines with AI & MCP servers. See how to deploy apps and manage topics using natural language. Bonus: Learn to build your own MCP server from API docs. 🔗Register: #DataEngineering #AIDevelopment #IndustrialData #MCP.

Join our webinar to discover how we architected and built a system to ingest and process vast amounts of data for one of our energy sector customers. More and Register: #iot #industry4 #customer #python.

Private Container Registries, Bug Fixes and UX Improvements. The latest Quix Cloud release is packed with stuff our users have asked for. Full release note here: #productrelease #iot #industry4

Flexible Windowed [data] Aggregations.Quix Streams 3.13.0 has a bunch of awesome new features including more flexibility for windowed aggregations and a new data sink. Check out the full list here:

Big news! Quix has achieved Google Cloud Ready - BigQuery designation!.Empowering manufacturers & energy companies to process high-frequency sensor data: #BigQuery #GoogleCloud #RealTimeData #IndustrialIoT.

✔ Stop conditions for ) – Finally, a way to stop infinite loops. ✔ Breaking change in Sink.flush() – We tweaked it. Your pipeline might have opinions!.🔗 Details: #QuixStreams #Kafka #Python #DataStreaming

🔥Quix Cloud is now Multi-Cluster capable!.☑️Deploy different environments to different clusters.☑️Improved control, isolation, flexibility and compliance.☑️Reduce risk by utilizing different clusters.Check it out: #QuixCloud #Kubernetes #StreamingData

Model based development can save costs, but managing data manually adds ‌them back. Discover best practices for:.-Managing test data.-Versioning configs.-Using modern databases & streaming tools. Read this to get your MBD data strategy right from day 1 .

🚀 Quix Streams 3.10.0 is out! 🚀. - Window closures that actually obey you. - Connector callbacks so you’re not debugging blind. - Fewer bugs, more stability. - Docs that don’t make you rage-quit.Get the details: #QuixStreams #Python #DataNerdsUnite.

Move beyond legacy systems, unlock your data’s full potential! Data historians can’t keep up with Industry 4.0. Quix & InfluxDB enable real-time processing, scale & cost efficiency. Learn more #IIoT #DataAnalytics #Quix #InfluxDB @InfluxDB @IIoT_World.

Are IIoT data volumes outpacing your systems? More sensors & real-time streams mean scaling issues, latency & bandwidth bottlenecks—and legacy tools can’t keep up. We break down why & what actually works for real-time IIoT data. 🔗 Read more:

Moving towards Industry 4.0 has major advantages but legacy historians make the move harder. InfluxDB join us on a webinar to discuss the issues our customers face and how we solve them. Register your interest here: @InfluxDB #IIoT #IoT #Manufacturing.

"Just connect our factory to the cloud". It'll be easy, they said. Factory (OT) systems are brilliant at controlling machinery, but getting their data into business (IT) systems? That's where things get spicy. Here's why: #OT #IT #Industry4 #IIoT

Industry 4.0 needs real-time data, but most companies find their OT data trapped behind legacy historians. Some teams are now pulling data directly from OPC servers instead enabling analysis at lower cost. Tech deep-dive:#opcua #industry40 #manufacturing