Lessons Learned from Scaling Integrations: A Senior Engineer's Journey

When I started building integrations, I assumed the biggest challenges would be understanding APIs and transforming data between formats. But as we scaled the number of integrations, I realized that the true complexity lives in the gaps—gaps between assumptions and reality, systems and infrastructure, data types and their interpretation.

Over time, I’ve helped design and operate a system that ingests data, stores it, processes it, and sends it across systems—handling multiple use cases, APIs, protocols, and behaviors. Here are some of the most important lessons I’ve learned from that journey—lessons I wish someone had told me earlier.

1. There’s Always an Outlier

Your first integration will almost certainly give you false confidence. You’ll build something clean and well-tested, and it’ll work beautifully. You’ll feel like the problem is solved.

But the reality is: there's always an outlier.

Maybe a downstream system sends a strange character. Maybe someone uploads a CSV with empty rows. Or maybe one client has a 15-year-old legacy system that needs SOAP instead of REST. Whatever the case, there's always something that breaks your assumptions.

In our case, we once had an integration that worked fine for weeks—until we got a payload with a strange character encoding that broke the transformation logic. That was the moment I stopped assuming that just because it works now, it will work tomorrow.

2. Always Log Everything—Even the Small Stuff

As our integrations grew, we realized the importance of having solid logs. At first, we logged only the critical parts: errors, successful syncs, and retry attempts. But that wasn’t enough.

What helped us debug faster was logging even the small events—when a job was picked up, when a request was parsed, when a header was missing but not fatal, when a fallback value was used. Tiny breadcrumbs like these often led us to the root cause of much bigger problems.

Combined with structured logging (we use `zap` in Go), it became much easier to trace issues across services. For example, we can now trace a record from ingestion through transformation to delivery, complete with `entity_id`, `operation_type`, and timestamps.

Without this level of logging, you're just guessing.

3. Retry Logic Isn’t Optional

Retrying failed requests is a must. You don’t want a single timeout or network blip to result in lost data. But retries are not something you "just add." They need to be thoughtfully designed.

We implemented retries with exponential backoff and a max retry count. That protected us from accidental flooding and allowed downstream systems to recover gracefully. More importantly, we made sure our retry logic was idempotent—so even if the same record was sent multiple times, the result wouldn’t change or duplicate data.

Also, timeouts and retry intervals should be adjusted based on the endpoint. Some systems can handle rapid retries; others need a bit of breathing room.

4. Types Matter—A Lot

One of the most painful issues we ran into was related to data types. In particular, integer overflows. At some point, we started seeing silent failures in one of our integrations because a civil ID field exceeded the maximum value allowed for a 32-bit signed integer.

The fix? We changed the column type to a numeric field with up to 30 digits—ensuring it could safely store even the largest national ID or tracking code without loss or overflow. But this fix didn’t stop at the database—we also updated our JSON schemas, Go structs, and even Elasticsearch mappings to reflect this change.

It sounds minor, but it was a foundational lesson: types aren't just a technical detail—they're the contract. Break that contract, and you break everything.

5. Change Data Capture (CDC) Is Powerful—but Not Magic

We started adopting Change Data Capture to detect changes in the database and process them in near real-time. The initial appeal was strong: no need for polling, better scalability, and faster data delivery.

But CDC introduced its own challenges. Updates that didn’t actually change any values still triggered events. Sometimes a row was updated twice within milliseconds, causing ordering issues. We had to deal with deduplication, transaction boundaries, and "phantom changes."

We ended up building a more robust processing layer with filtering logic and replay support. In the end, CDC added a lot of value—but it required maturity in how we designed our integration services.

6. Version Everything

This one hit us hard. We deployed a new version of an integration that included some extra fields. Those fields broke compatibility with a downstream system that didn’t expect them, and we had no way to roll back gracefully.

That’s when we introduced versioning—on everything:

- Versioned YAML config files

- Versioned API endpoints

- Versioned transformation logic

- Even versioned SQL queries using `sqlc`

It allowed us to support multiple versions of an integration simultaneously, which turned out to be a game changer. Clients could upgrade at their own pace. We could test new logic without fear. Rollbacks became trivial.

7. Infrastructure Is Just as Important as Code

When things were small, we focused mostly on our code. But once we hit scale, infrastructure limitations became more visible than any code inefficiency.

PostgreSQL hit its connection limit. Oracle couldn’t handle too many concurrent inserts. Docker network latency caused unexpected timeouts between services. Even disk I/O became a bottleneck when our ingestion pipeline got flooded.

We had to step back and rethink our architecture. We split responsibilities into:

- An NGINX proxy for external communication

- An ingestion server for storing incoming payloads

- A process server for reading from the DB and pushing to destinations

- A metrics and alerting stack with Prometheus and Grafana

Having clear boundaries between components gave us better scalability, easier monitoring, and safer deployments.

8. Systems Differ—and Behavior Changes Across Environments

One of the most frustrating bugs we encountered came from how different systems handle large numeric values. We were passing a long number—like a civil ID or vehicle plate—as part of a payload. In some systems (especially JavaScript-heavy frontends or document stores), the number was converted into scientific notation (e.g., `2.12345678912345e+15`), which led to precision loss and downstream errors.

This happened despite our best efforts to "treat numbers as strings" in the frontend. Somewhere along the chain, the type got misinterpreted. It reminded us how fragile the assumptions are when data flows between systems with different type handling, encodings, or formats.

What made it worse was that some systems would silently accept these values—only to cause issues days later. Others would outright reject the data.

The takeaway: always test your assumptions when crossing system boundaries. What works in System A might break in System B, even if they seem “compatible.”

Final Thoughts

Scaling integrations isn't about adding more lines of code or spinning up more containers. It's about embracing complexity, designing for failure, and continuously learning from real-world behavior.

The journey taught me that building robust integrations takes more than clean code—it takes empathy, discipline, observability, and the ability to anticipate the unexpected. And most of all, it takes humility to admit that no matter how polished your system is, there’s always an outlier, always a different system, always a new challenge around the corner.

If you’re scaling integrations, don’t just build for what you know—build for what you can’t predict.