Most data architectures make you choose between fresh data and fast queries, but there's a third option. Chipotle figured it out decades ago, and the same model applies to how you build real-time data products for the apps and agents that run your business.

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Most data architectures force a tradeoff between fresh data and fast queries. Using Chipotle's kitchen model as an analogy, the post argues for continuously pre-transforming raw operational data into reusable 'real-time data products' rather than waiting for client requests. This proactive approach enables millisecond query latency with near-real-time freshness, supporting AI agents, microservices, and interactive UIs at scale. The post recommends modeling data products around business domain nouns (customers, orders, portfolios) and composing them to expand what agents and services can do. Materialize is presented as a SQL-based platform implementing this pattern.

What Chipotle Can Teach Us About Real-time Data Products

Applying these ideas to your data infrastructure