Should you write your own Data Engineering Framework?

Data engineering involves repeating patterns: ingesting, transforming, storing, and serving data all follow the same recipe. Many teams have opted to create an in-house framework, with mixed results. Now, with AI coding assistants, some may see these as obsolete, since ETLs can now be built in minutes. Or should we instead write better, more robust, and agile frameworks? This talk will explore the "why" and the "how" of building your own.