Georgia-Pacific needed to move beyond traditional historians and databases that could not keep up with its asset-health data. With Quasar, GP now processes over 73 billion rows per day from approximately 80,000 sensors across 140+ facilities, supporting predictive maintenance, faster model development, and measurable reductions in unplanned downtime.

The National Laboratory of the Rockies needed to capture voltage and current waveforms from three-phase circuits powering compute racks, at frequencies beyond what previously evaluated platforms could handle reliably. Quasar now supports continuous high-frequency ingestion under real research conditions, with 240 signals captured at roughly 10 kHz and testing planned for higher-frequency configurations.

This case study shows how Quasar was used alongside an existing process historian to support deeper analysis of manufacturing data. Instead of reducing history depth or lowering sampling accuracy, the facility kept full-resolution data available for analytics, with up to 20x lower storage footprint and faster feature extraction for quality and energy-consumption models.

An energy trading team needed to recompute Price Forward Curves more frequently as renewable generation made supply and demand harder to predict. Quasar captured market and weather data directly, reduced the storage footprint, and accelerated PFC computation by 100x without adding another fragile layer to the data stack.

This case study shows how Quasar was used to capture Nasdaq ITCH market data, store orders and trades at scale, and rebuild order books at any point in the trading day. The system handled data volumes around 2 TB per day, reduced storage requirements by 10x, and enabled sub-second order book reconstruction for market analysis and best-execution workflows.