Real-Time Telemetry & Observability

Energy trading desks rely on sub-second physical asset telemetry to make accurate intraday market commitments and balance grid position. Across large-scale wind parks, solar installations, and industrial battery storage portfolios, telemetry dropouts create critical blind spots. Unreliable data feeds force traders to operate with higher risk margins or face financial penalties due to imbalance charges from grid operators. At Eneco, raw telemetry streams were subject to intermittent connection losses, inconsistent partner API responses, and unmonitored pipeline failures. Without an automated observability layer, engineering teams were forced into reactive troubleshooting long after data gaps had already skewed intraday forecasting models and trading algorithms.

To establish reliable end-to-end telemetry observability, Datapand architected an ingestion pipeline leveraging Azure Data Explorer (Kusto / KQL) and Databricks SQL. Raw asset streams from physical sensors and third-party partner portals—including Greenbyte, Synaptiq, and North-tec—were normalized into high-throughput time-series tables. Automated data quality frameworks were deployed directly at the ingestion boundary. KQL queries running on sliding time windows continually check stream completeness, flagging zero-value anomalies, connection drops, and out-of-order timestamps in real time. The observability layer was operationalized through unified Grafana dashboards linked with automated alerting mechanisms via Azure DevOps Pipelines. When a telemetry dropout occurs, alerts are dynamically routed to the responsible engineering or trading desk based on severity and asset domain.