Introduction

When deploying Python services that must handle large sums of IoT data, standard single-server setups quickly buckle under the load. In my time building backend architecture for fish farms, we needed a system that could seamlessly ingest, process, and return analytical telemetry without dropping packets.

The Architecture

We opted for a combination of:

• API Gateway: For rate limiting and secure entry points.
• EC2 instances: Auto-scaling groups to manage the heavy lifting of the Flask application.
• S3: For durable, long-term raw data storage.

Why Flask?

While FastAPI is my go-to for modern asynchronous architectures, Flask provides an incredibly mature ecosystem for modular applications that require a lot of synchronous, CPU-bound processing, especially when tied closely with extensive ORM operations.

The Results

By shifting from a monolithic server to a distributed AWS architecture, we reduced our latency spikes by 80% during peak upload periods, and we achieved a solid 99.99% uptime.

Takeaway: Cloud integration isn’t just about moving code to another computer. It’s about designing your code to be native to the infrastructure it runs on.