There’s a specific moment in an ecommerce business’s growth where integrations start breaking. Not because they were built poorly—because they were built for a different scale.
An integration that polls for new orders every minute works fine at 50 orders per day. At 500 orders per day, you’re making 1,440 API calls to check for maybe 20 new orders per hour. At 5,000 orders per day, you’re probably hitting rate limits.
The Scale Mindset Shift
Building integrations that scale requires thinking differently from the start. Instead of asking ‘how do I get this data?’ ask ‘how do I get this data efficiently at 10x volume?’
This doesn’t mean over-engineering. It means making architectural choices that don’t box you in. Webhooks instead of polling. Queues instead of synchronous processing. Idempotent operations instead of hoping nothing fails.
Webhook-First Architecture
Polling is the default because it’s simple. You ask for data, you get data. But polling doesn’t scale. It wastes resources when nothing has changed and can miss events when changes happen faster than you poll.
BigCommerce’s API rate limits make this concrete: you get roughly 150 requests per 30 seconds with OAuth apps. At high volume, a polling-based integration can burn through that allowance just checking for new orders, leaving nothing for actually processing them.
Webhooks flip the model. Instead of asking for changes, you get notified about changes. BigCommerce supports webhooks for orders, products, customers, and inventory changes. This is more efficient, more timely, and more scalable. It’s also more complex to implement correctly—you need to handle retries, verify the webhook signature, and manage the state of what you’ve processed.
Queue-Based Processing
When an order comes in, the temptation is to process it immediately—create the record in your ERP, update inventory, send notifications. But synchronous processing creates fragile chains of dependencies.
Queues decouple receiving events from processing them. An order comes in, it goes in a queue, and separate workers process it when they can. If the ERP is slow, orders queue up instead of timing out. If processing fails, the message stays in the queue for retry.
Idempotency: The Safety Net
At scale, failures happen. Network timeouts, temporary outages, race conditions—they’re not edge cases, they’re normal operation. The question isn’t whether a message will be processed twice, but what happens when it is.
Idempotent operations give the same result whether you run them once or ten times. Creating an order with ID 12345 either creates it (if it doesn’t exist) or recognizes it already exists (if it does). This makes retries safe and recovery automatic.
Monitoring and Observability
You can’t fix problems you don’t know about. At scale, problems happen constantly—the question is whether they’re self-correcting or accumulating.
Good integrations have clear metrics: messages processed, messages failed, processing time, queue depth. They have alerts for anomalies. And they have enough context in logs to diagnose problems without guessing.
BigCommerce provides some visibility through the control panel’s webhook logs and API analytics, but for production integrations, you need your own monitoring. When an order fails to sync, you need to know within minutes, not when a customer complains days later.
Start Now, Not Later
The time to think about scale is before you need it. Retrofitting these patterns into an existing integration is possible but painful. Building them in from the start is just good engineering.