How did you test your script before running it on real data? What steps did you take to ensure it was safe to run again on the data?

Before running the script on production data, I first created a suite of unit tests that exercised each function with controlled inputs. I then set up an integration test environment that mimicked the production data pipeline, using a subset of real data stored in a separate S3 bucket. The script was executed against this test data, and I used data validation libraries (e.g., Great Expectations) to assert that the output matched expected schemas and value ranges. I also ran the script with mocked external services (e.g., API endpoints) to ensure it handled network failures gracefully. Finally, I performed a dry‑run in a staging environment, comparing the intermediate files and logs to the production run. By combining unit tests, integration tests, data quality checks, and a staged deployment, I was confident that the script would run safely on the full dataset. Additionally, I used a version control system to track changes and a CI pipeline to automatically run the tests on every commit. I also set up a rollback plan in case the script produced unexpected results, which included restoring the previous dataset and notifying the data engineering team. This comprehensive testing strategy ensured that the script was safe to run again on the real data.