dbt Python models support a limited set of materializations because they rely on execution within the data platform’s Python compute engine (such as Snowpark for Snowflake, Dataproc for BigQuery, or Spark). These engines require models to materialize into actual relations—tables or views—in order to persist the results of Python-based transformations.
The ephemeral materialization, however, is fundamentally incompatible with this behavior. Ephemeral models do not create relations in the warehouse; instead, dbt inlines their SQL logic directly into downstream models. Since Python models cannot be inlined (they execute Python code, not SQL), dbt does not allow ephemeral Python models. dbt requires Python model outputs to be materialized as either:
Therefore, ephemeral is not supported for Python models, and attempting to configure a Python model as ephemeral will result in a compilation error.
The reason is straightforward: ephemeral logic depends on SQL compilation, while Python models depend on executing Python code in the data platform. Because these mechanisms are incompatible, dbt restricts Python models to relational materializations only.
Thus, the correct answer is No — ephemeral is not supported for Python models.
