Imagine dedicating weeks or even months to a complex data project, pouring your time and effort into crafting a robust solution, only to witness it crumble shortly after being deployed to production. The frustration mounts as you discover that the data you were so confident about is now riddled with errors and discrepancies. End users, relying on this flawed data, begin reporting issues, shaking the very foundation of trust you had painstakingly established. It’s a nightmare scenario that can shatter the relationship between Data Engineers and their consumers.