Materializing Python Assets into Your Warehouse
Return a DataFrame, let Bruin handle the rest. Learn how to use Python materialization to load data into BigQuery, Snowflake, Postgres, and more - with support for merge, append, and incremental strategies.
What
Goal - Use Python materialization to turn any Python script that returns a DataFrame into a loaded, managed table in your data warehouse. No manual to_sql, no credential wiring, no duplicate-handling code.
Audience - Data engineers who want Python assets that behave like SQL assets: typed columns, merge/append/incremental strategies, and quality checks - all driven by the asset's YAML config.
Prerequisites
- Bruin CLI installed
- A Bruin project with a configured warehouse connection (BigQuery, Snowflake, Postgres, Redshift, MSSQL, MySQL, DuckDB, etc.)
- Familiarity with Python assets - see Using the Bruin Python SDK if you're new to them
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