Production Error Handling & Pipelines
Batch processing, database integration, API validation, and multi-stage pipelines.
It’s 3 AM. Your data import job is screaming at you.
Ten thousand customer records came in from a legacy system—a migration you promised would be seamless. Your Pydantic models look bulletproof. Nested structures, strict validation, type hints on everything. You validated a sample batch locally and it was fine.
Then production happened.
Record 17 has a missing required field. Your code crashes. The import stops. Nine thousand, nine hundred eighty-three records sit in limbo. You can’t rollback—the client expects this data. You can’t move forward—your validation is a brick wall.
Here’s the reality: you don’t control the data. Systems are messy. Migrations are ugly. Timestamps come in seventeen different formats. Email validation passes some garbage. And your users will find edge cases you never imagined.
This week, you’ll stop building validation that fails on the first error—and start building systems that handle failure gracefully. You’ll learn to batch, to collect errors, to validate pipelines not just fields. You’ll see how real backends distinguish between "reject and crash" and "log and recover."
Production doesn’t care how perfect your models are. It only cares if your system survives impact.
Practice your skills
Sign up to write and run code in this lesson.
Production Error Handling & Pipelines
Batch processing, database integration, API validation, and multi-stage pipelines.
It’s 3 AM. Your data import job is screaming at you.
Ten thousand customer records came in from a legacy system—a migration you promised would be seamless. Your Pydantic models look bulletproof. Nested structures, strict validation, type hints on everything. You validated a sample batch locally and it was fine.
Then production happened.
Record 17 has a missing required field. Your code crashes. The import stops. Nine thousand, nine hundred eighty-three records sit in limbo. You can’t rollback—the client expects this data. You can’t move forward—your validation is a brick wall.
Here’s the reality: you don’t control the data. Systems are messy. Migrations are ugly. Timestamps come in seventeen different formats. Email validation passes some garbage. And your users will find edge cases you never imagined.
This week, you’ll stop building validation that fails on the first error—and start building systems that handle failure gracefully. You’ll learn to batch, to collect errors, to validate pipelines not just fields. You’ll see how real backends distinguish between "reject and crash" and "log and recover."
Production doesn’t care how perfect your models are. It only cares if your system survives impact.
Practice your skills
Sign up to write and run code in this lesson.