Handling HTTP 429 Too Many Requests responses correctly in Python data pipelines requires more than a time.sleep(1) in an except block. The following tools and libraries are the practical toolkit for building rate limit resilience into production data automation. They cover everything from simple retry decorators to distributed rate limiting for multi-worker pipelines.
1. Tenacity
What it does: A Python retry library that provides a decorator-based interface for configuring retry behavior, backoff strategies, and logging.
Why it matters: Rolling your own exponential backoff is straightforward for simple cases, but production pipelines need configurable stop conditions, structured logging of retry attempts, and clean separation between business logic and retry behavior. Tenacity handles all of these.
Installation:
