Warehouse as Shared Data Layer

The warehouse shifted power from app vendors to a shared data layer. Once product events, CRM records, billing data, and support data landed in one warehouse, teams could join them with SQL instead of waiting for a CDP or SaaS app to support a specific schema or connector. That let Fivetran handle ingestion, dbt define business logic, and Census or Hightouch sync the results back into tools like Salesforce or Braze, each specializing in one layer instead of one vendor owning the full loop.

• A classic CDP worked best when a company fit the vendor's built in data model. The warehouse model was looser. Teams could model customers, accounts, workspaces, subscriptions, or any custom object in SQL, then send those definitions anywhere, which made the system far more extensible for PLG and multi product companies.
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• SQL was the key adapter. Cloud warehouses gave startups big data scale without learning Hadoop era systems, and tools across the stack could all read and write through the same interface. That is why the modern stack could be mixed and matched so easily, and why one bundled product split into Fivetran, Snowflake, dbt, and BI tools.
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• This also changed who controlled business logic. Before dbt, analysts often depended on data engineers or app specific rules inside tools. With dbt, teams could define metrics and transformations once in the warehouse, test them, version them, and reuse the same definitions for dashboards, sales workflows, and customer messaging.
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The next step is not moving away from the warehouse, but making it fast enough to power more operational workflows directly. As warehouses, transformation, and sync tools keep tightening latency, more SaaS products will become interfaces on top of the customer's warehouse, and vendors that cannot plug cleanly into that shared layer will lose strategic control.