Mutual Dependency Between Sigma and Warehouses

This setup makes Sigma stronger when warehouses win, but it also means Sigma can never fully control its own cost base or product surface. Sigma works by querying customer data where it already lives, inside Snowflake, BigQuery, Redshift, and Databricks, so every new Sigma dashboard, app, or embedded view drives warehouse usage too. That alignment helps Sigma ride partner distribution, but it ties Sigma's user experience and margins to warehouse pricing, feature support, and native app roadmaps.

• The dependency is operational, not just commercial. Sigma documents that a data source connection is what lets Sigma talk to the warehouse, and feature support varies by platform and region. In practice, the warehouse decides what data, AI functions, and performance Sigma can expose to end users.
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• The dependency is mutual because Sigma expands warehouse reach beyond data teams. Snowflake and Sigma both position the product as a way for business users to analyze warehouse data with just a few clicks, and Sigma's embedded product turns a customer's application usage into both Sigma revenue and more warehouse compute consumption.
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• This is the same unbundled modern data stack pattern that lifted Looker and other BI tools. Prior research on Looker and Dataiku shows the interface layer often becomes the channel through which partners sell more compute and storage, but that also leaves the interface layer exposed if the underlying platform builds enough native analytics itself.
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The next phase is deeper co packaging with warehouses, especially through native apps, partner connect flows, and AI features that run inside the warehouse. That will keep Sigma's growth tightly linked to cloud data platform adoption, while raising the stakes for Sigma to stay the best business user interface before warehouse vendors close more of the gap themselves.