Which BI tools push definitions back to the warehouse?

Quoted from Databricks’s docsYou can export the context from a Genie Agent as a metric view.

Quoted from Snowflake’s docsSemantic Studio: You can refine, configure, and deploy semantic views in Semantic Studio, the authoring environment in Workspaces with conversational assistance from CoCo.

Quoted from Omni’s docsWhile not every query needs to be pushed down to dbt, when you do create a query where you want to promote the logic into dbt, you can do so within Omni.

Quoted from Sigma’s docsInstead of storing data in a database table, a warehouse view saves a SQL statement that expresses specific query logic defined by an individual data element or input table in Sigma.

Quoted from BigQuery’s docsLogical views let you save a SQL query as a virtual table, which helps you reuse query logic and simplify access to your data.

Quoted from Looker’s docsLookML-based derived analytic models: You define the analytic model by referencing an existing LookML Explore. Looker automatically translates your Explore topology, joins, dimensions, and measures into database-native analytic model DDL statements and creates and manages the model in your database.

Quoted from Tableau’s docsThis enables you to add or update data in your database with clean, prepped data from your flow each time the flow is run.

Quoted from Power BI’s docsContent creators who make Power BI semantic models can use OneLake integration to write model tables to Delta tables in OneLake.

Quoted from Metabase’s docsTransforms allow you to wrangle your data in Metabase, write the query results back to your database, and reuse them in Metabase as sources for new queries.

Quoted from Alteryx’s docsUse the Write Data In-DB tool to create or update a table in the database, or use the Data Stream Out tool to stream the In-DB results to a workflow for standard processing.