Leveraging Redis and Kubernetes to Build an Air-Quality Geospatial Visualization

The author used Redis as a database deployed on Kubernetes to demonstrate its power during the California wildfires in 2020. They re-interpreted their previous work on exposing geospatial data with two new guiding principles: using Redis' geospatial features for partitioning and deploying the whole application on Kubernetes. The author applied a methodology called PAN (Partition, Annotate, and Name) to organize air-quality sensor data collected by PurpleAir. They used Python, Redis, and a Kubernetes deployment to collect, store, and use air quality data.