The first three Orca Toolboxes are live - a production grade set of algorithms for sensor data, shipped as minimal binaries and designed to run inside your own cloud or on premise.
Orca was built for a specific shape of problem - continuous, window-oriented, algorithm-on-algorithm analytics over sensor data. Historically that shape has shown up most clearly in vibration, pressure and temperature analytics. Increasingly - and more interestingly - it is showing up in robotics telemetry, where Orca's design solves a set of problems that the rest of the robotics observability stack only addresses in parts.
Telemetry analytics has a shape problem. The data arrives continuously, the logic is event-window based, and the algorithms that produce insight depend on the results of earlier algorithms. Most orchestration frameworks were not designed around that shape - they were designed around periodic batch jobs, or around pure stream processing. Orca takes a different position: a hub and spoke orchestrator that keeps the orchestration logic in one place and pushes the analytics work out to independent, language-agnostic processors.
Orc-a has become Orca Telemetry 馃帄.
Welcome to Orca Telmetry. A platform aspiring to become the standard for processing analytics on real-time data.
Here is the origin story of why we built it, the problem it solves, and where we are going next.