Scott Brinker's new report with Databricks articulates something I've been watching take shape for years: the martech "stack", the familiar Tetris arrangement of boxes, is beginning to dissolve. What's emerging in its place is what Scott calls a composable canvas: a fluid, data-centric architecture where AI agents and custom software operate on shared data rather than fighting through integration pipelines.

Reading through it, I found myself nodding along more than once. Not because it's an easy thesis to make (it's actually a fairly radical reframing of how enterprises think about marketing technology), but because it describes an architectural direction we at Snowplow committed to a long time ago, often before there was shared vocabulary for it.

I wanted to share a few reactions: where the report resonates strongly, how we think Snowplow fits into the architecture it describes, and one dimension I'd add to the model that I think becomes more important as AI agents take on a larger role in customer interactions.

The report's core structural argument is that the data platform (Databricks, Snowflake, BigQuery, etc.) has become the gravitational center of the entire martech stack. Applications, agents, and analytics no longer sit on top of the data; they operate within it. The data platform isn't a repository at the bottom of the stack anymore. It is the stack.

This is a view we've held at Snowplow for a long time, and it's one that shaped a lot of early decisions about how we built our product. When we were first putting Snowplow together in 2012, the prevailing model was to accumulate customer data inside vendor systems and provide managed access to it. We took the opposite position: your data belongs in your infrastructure, governed by your rules, queryable by any tool you choose. At the time, that felt like a principled architectural stance, maybe even a slightly contrarian one. As this report makes clear, it's now the only architecture that makes sense at scale.

What is the customer context layer? The customer context layer is the real-time behavioral infrastructure that sits across your data foundation and your customer-facing systems. It’s wired directly into digital experiences so that AI agents can understand what a customer is doing right now, in addition to their entire historical journey.