Can you vibe code your own Sigma alternative? Why AI plus a data warehouse is not enough

Mike Palmer argues that AI models connected directly to cloud data warehouses can quickly create useful dashboards and applications for small teams, but that scaling such tools across an enterprise introduces operational challenges involving security, governance, performance, cost control, ownership, and auditability. Using the failed Fyre Festival as an analogy, he contends that an attractive initial prototype can conceal the infrastructure needed when many users rely on it, including row-level permissions, single sign-on, monitoring, caching, writeback records, and change histories required for financial controls such as Sarbanes-Oxley compliance. He also notes that unmanaged agents may generate inefficient and duplicative warehouse queries, while their accuracy can decline as data models evolve unless their contextual instructions are continuously maintained. The post presents Sigma as a model- and warehouse-agnostic governed layer that can incorporate externally built AI applications and agents while providing live warehouse access, permission controls, audit trails, ownership, versioning, and shared query results. It concludes that direct AI-assisted development remains appropriate for simple questions and small-team prototypes, whereas broader business use requires a managed platform to maintain reliability and compliance over time.

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