Device Telemetry Streaming Pipeline
An event-driven pipeline that ingests high-volume device telemetry and fans it out to downstream consumers without coupling them to the producer.
- Python
- Apache Kafka
- Redis
- Kubernetes
title: Device Telemetry Streaming Pipeline summary: An event-driven pipeline that ingests high-volume device telemetry and fans it out to downstream consumers without coupling them to the producer. stack: ["Python", "Apache Kafka", "Redis", "Kubernetes"] links: repo: https://github.com/your-handle/telemetry-pipeline featured: true order: 1
The problem
Downstream services shared state through a single bottleneck. One slow consumer stalled the rest, and backpressure was impossible to reason about — a spike in one place degraded everything.
The approach
I moved the system to a Kafka-based event log with independent consumer groups, so each consumer reads at its own pace and failures stay isolated. A Redis caching layer absorbed hot reads, and tuning async I/O kept tail latency flat under burst load.
The key decision was trading immediate consistency for decoupling: consumers now work from the log rather than a shared store, which means a new consumer can be added without touching the producer or the others.
The outcome
- Sustained 120K+ messages/sec at sub-100ms p99
- New consumers added with zero producer changes
- Backpressure became observable and bounded instead of cascading