Industry
Full-resolution history. Real-time ingestion. One system.
Quasar helps manufacturers keep deep operational history online, ingest live data as it arrives, and analyze low-frequency process data together with high-frequency sensor signals across plants, assets, and years.
The Challenge: Managing Industrial Data at Scale
Industrial data becomes difficult when history, speed, resolution, and computation all matter at once.
Historians work well for collection, operations, dashboards, and recent process values. They become constrained when teams need years of history, real-time freshness, high-frequency signals, and analysis across many assets or facilities.
The tradeoffs can appear quickly. Keeping more history and queries slows down. Reduce sampling and lose detail. Store waveforms separately, making root-cause analysis harder. Move the data into a lake or notebooks and add copies, latency, and custom pipelines.
The same impedance mismatch makes AI harder than it should be. The historian contains the data, but its calculation and query capabilities were not designed for large-scale feature engineering, model development, or iterative analysis. Teams must first extract, reshape, join, and duplicate the data before they can use it.
You do not need to replace the historian. You need an analytical system that works with it: one that can ingest live data, retain deep history, combine low-frequency process values with high-frequency signals, and run serious computation without sacrificing precision or performance.
Why Quasar
You've decided to move from dashboards and retrospective reporting to AI-driven decisions across the operation
That means using years of plant history, live process data, high-frequency signals, and context from thousands of assets to predict failures, optimize processes, and improve quality. The ambition is strategi and the pressure it puts on the data infrastructure is immediate.
This is where teams start fighting existing infrastructure, including historians. Not because it is failing at its original job, but because AI demands a different kind of system: deeper history, richer computation, full-resolution signals, large-scale joins, and heavy analytical queries while new data continues to arrive.
Quasar removes that impedance mismatch. It works alongside the systems already running the operation and provides the data layer required for advanced analytics and AI:
Deep, full-resolution history across plants and assets
Real-time ingestion alongside analytical workloads
Low-frequency process values and high-frequency waveforms in one system
Feature engineering, joins, interpolation, and model preparation close to the data
Lossless compression that keeps large datasets economically online
Quasar turns industrial data into a system you can act on.
Deep history, live data, high-frequency signals, and analytical compute come together in one platform built for AI-driven operations at industrial scale.
Results at Scale
100 billion rows ingested per day
Quasar sustains continuous ingestion from tens of thousands of sensors at full resolution across more than 100 facilities.
Deep history without sacrificing resolution
Quasar removes the usual tradeoff between retention depth and sampling accuracy.
Low- and high-frequency data in one system
Process values, equipment telemetry, and full waveforms remain available for the same analytical workflows.
Feature extraction close to the data
Teams can run joins, interpolation, feature engineering, and model preparation without exporting everything into a separate stack.