Event-Driven Systems for High-Volume Billing & EV Telematics

Context

At Bidgely I work on the backend behind solar billing, time-of-use optimization, and EV telematics. These workflows generate large, bursty volumes of customer and device data — exactly the kind of workload that punishes tightly-coupled synchronous services.

Why event-driven

If services call each other synchronously, a slow or failing downstream pushes back-pressure all the way to ingestion, and a traffic burst has nowhere to go but the floor. Modeling the data as events on EventBridge, SQS, and Kafka decouples producers from consumers: bursts buffer in durable storage, consumers scale on their own, and a transient downstream failure becomes a retry instead of a cascade. Because that delivery is at-least-once, the consumers are built to be idempotent — processing the same event twice has the same effect as once, which is non-negotiable for anything touching billing. The cost is eventual consistency and harder end-to-end tracing — a trade worth making for a high-volume, bursty workload where reliability is the point.

The Cassandra driver migration

The services talked to Cassandra through Astyanax — a client that's deprecated and no longer maintained. Every month on it accrued risk: no upstream fixes, harder onboarding, growing distance from current Cassandra. I led the migration to the supported DataStax Java Driver.

The subtle part of a client swap isn't the API surface; it's the defaults. Consistency levels, retry policies, and timeouts differ between clients, and a silent change in any of them changes how your queries behave under failure. The migration was careful precisely because those defaults are part of the contract — I verified read/write behavior matched the old client before cutting services over.

Outcomes

High-volume billing and telematics data flows through resilient event-driven pipelines, and the data-access layer now runs on the supported DataStax driver — better maintainability, better query reliability, and less standing platform risk.