KX is a leader in what it does but it doesn't really fit the canonical IoT use case, either from a workload or analytics perspective. The challenge is that IoT isn't just temporal, most of the interesting relationships are spatial as well since you are analyzing relationships across sensor and telemetry streams, and the analytics are real-time mixed workloads with ridiculous working set sizes. It isn't their market. They rock the time-series market though.
A good example of an IoT data analytics problem is analyzing a petabyte of drone sensor data, which on a large drone amounts to a few flights worth. Typical raw sources tend to be some combination of hyper-spectral imaging/video and LIDAR. Or RF probability functions e.g. mobile. Or a combination of all of the above because you are fusing multiple sources to reduce the uncertainty for your analysis.
FWIW, the "tens of trillions" of IoT records I mentioned was a real-world example from one of the most famous financial companies. It was a spatial analytic on a polygon model, and a classic IoT data model. If KX solved that particular analysis problem, they would have used it.
A good example of an IoT data analytics problem is analyzing a petabyte of drone sensor data, which on a large drone amounts to a few flights worth. Typical raw sources tend to be some combination of hyper-spectral imaging/video and LIDAR. Or RF probability functions e.g. mobile. Or a combination of all of the above because you are fusing multiple sources to reduce the uncertainty for your analysis.
FWIW, the "tens of trillions" of IoT records I mentioned was a real-world example from one of the most famous financial companies. It was a spatial analytic on a polygon model, and a classic IoT data model. If KX solved that particular analysis problem, they would have used it.