Why Your Code Breaks in Production (and How Docker Fixes It)

1. Why This Matters You write your code. You test it locally. Everything works perfectly. Then it goes to production… and breaks. You spend hours debugging, only to realize: nothing is wrong with your code — the environment is the problem. In data engineering, this happens all the time: A Spark job runs locally but fails in production Airflow works on Ubuntu but breaks on macOS Kafka pipelines behave differently across environments At its core, the issue is simple: Your environment is not consis