Implementing a Kalman Filter directly within Postgres offers a novel approach to smoothing noisy GPS data, traditionally handled outside the database in environments like Python or MATLAB. This method leverages SQL functions, recursive queries, and aggregates to refine GPS data by addressing the inherent noise from satellites, buildings, and devices, making large-scale analysis more reliable. The Kalman Filter is a recursive algorithm that estimates the true state of a dynamic system by combining predictions with observed measurements while explicitly modeling uncertainty. Challenges in implementing it within SQL include maintaining state, updating it with each new measurement, and ensuring data is processed in sequence. The project by traconiq includes an open-source implementation that supports both online and offline filtering, with benchmarks indicating that while online filtering ensures immediate availability of smoothed data, offline filtering, especially using custom aggregates, offers higher efficiency for large datasets. This approach significantly enhances the ability to perform scalable analytics directly within Postgres, offering immediate, realistic paths from jittery GPS samples useful for analytics or visualization.