The architecture of a data product is often misunderstood because it is composed of two distinct yet interdependent layers. The outer architecture defines how the data product behaves within the enterprise ecosystem — its contracts, SLAs, metadata, governance boundaries, interoperability rules, lineage, and the mechanisms that ensure trust, compliance, and discoverability.

The inner architecture focuses on how the data product is built — including its pipelines, workloads, storage patterns, serving protocols, orchestration logic, and the technologies used to implement its internal logic.

This session examines both layers in detail, explaining why scaling data products requires a clear separation between external governance and internal engineering autonomy. It highlights how organizations can design composable, governed, and future-proof data products by standardizing the outer architecture while preserving freedom and flexibility in the inner one. The talk clarifies the principles, patterns, and guardrails that allow data products to remain interoperable, high-quality, and AI-ready across the enterprise.