This is the part-2 of my video on auto-generating an analytical data layer means using metadata and automation to reduce manual effort in building data models. The process starts with a strong metadata catalog that defines entities and relationships. Reusable templates then create fact tables, dimensions, and KPIs from this metadata. Relationships across datasets can be detected automatically, while governance rules and documentation are generated alongside the models. This approach speeds up delivery, ensures consistency, and builds trust in analytics, allowing teams to focus more on insights than repetitive engineering.