Rearchitecting Approximation-First Cloud Telemetry

Cloud-native telemetry systems like Prometheus enable observability through rule queries-window- and dimension-based aggregations over time series data. However, these queries incur high operational costs and latency due to raw data ingestion, repeated computations, and expensive storage. In this demonstration, we revisit the potential of approximate analytics to improve efficiency across the telemetry pipeline. We propose a hybrid, sketch-based caching architecture that unifies optimizations across ingestion, query processing, and storage. By supporting approximate representations at each stage, our design achieves significant cost savings while maintaining high accuracy and responsiveness.