Every bad data architecture starts with a good intention.

A quick fix becomes permanent. A temporary integration survives for years. A "simple" reporting solution grows into a business-critical dependency nobody dares to touch. Before long, teams are drowning in complexity, performance problems, duplicate data, unclear ownership, and endless technical debt.

In this session, we'll dissect real-world architecture anti-patterns from databases that became integration hubs and data lakes that turned into swamps, to over-engineered real-time platforms and AI projects built on shaky foundations. Through stories, diagrams, and painful lessons learned, you'll discover why these architectures fail, how the warning signs can be spotted early, and what successful teams do differently.

Whether you're a DBA, data engineer, architect, or developer, you'll leave with practical principles for designing data platforms that remain scalable, maintainable, and resilient long after the first project goes live.