Engineering Note
Our data pipeline didn't become difficult because we had too much data.
It became difficult because we had too many things depending on the pipeline.
At first, a simple scheduled job was enough.
Rails app → background job → database → report.
Then the requirements grew:
* Multiple data sources
* Scheduled transformations
* Dependency between jobs
* Failed job retries
* Monitoring
* Historical backfills
* Data quality checks
We started looking at tools like Airflow, Prefect and cloud-native services.
But the decision wasn't:
“Which tool is more powerful?”
It was:
“How much workflow complexity do we actually need to operate?”
For complex, dependency-heavy pipelines, Airflow can provide strong workflow orchestration.
For Python-based workflows with a simpler developer experience, Prefect can be a good fit.
And when the workload maps well to AWS, GCP or Azure services, cloud-native solutions can reduce infrastructure we need to manage ourselves.
The trade-off is important.
More control usually means more operational responsibility.More managed services usually mean less infrastructure work, but more dependency on the cloud platform.
Important learning:
Don't introduce a data orchestration platform just because the data volume is growing.
Introduce one when the workflow itself has become difficult to manage reliably.
Data architecture should scale with the complexity of the business process, not just the number of records.
At first, a simple scheduled job was enough.
Rails app → background job → database → report.
Then the requirements grew:
* Multiple data sources
* Scheduled transformations
* Dependency between jobs
* Failed job retries
* Monitoring
* Historical backfills
* Data quality checks
We started looking at tools like Airflow, Prefect and cloud-native services.
But the decision wasn't:
“Which tool is more powerful?”
It was:
“How much workflow complexity do we actually need to operate?”
For complex, dependency-heavy pipelines, Airflow can provide strong workflow orchestration.
For Python-based workflows with a simpler developer experience, Prefect can be a good fit.
And when the workload maps well to AWS, GCP or Azure services, cloud-native solutions can reduce infrastructure we need to manage ourselves.
The trade-off is important.
More control usually means more operational responsibility.More managed services usually mean less infrastructure work, but more dependency on the cloud platform.
Important learning:
Don't introduce a data orchestration platform just because the data volume is growing.
Introduce one when the workflow itself has become difficult to manage reliably.
Data architecture should scale with the complexity of the business process, not just the number of records.