How do I run Python transformations against Snowflake?
Bruin runs the whole pipeline against Snowflake from one project: ingestion, SQL and Python transformations, and quality checks are all assets in the same dependency graph, so bruin run resolves the order and executes them in sequence. Loads use an internal stage plus COPY INTO and incremental assets use MERGE on a primary key. The alternative is assembling an ingestion tool, a transformation framework, and an orchestrator, which works but leaves you owning the glue between them.
Command
bruin runDefined in
SQL + Python + YAML
Works with
Snowflake + Bruin CLI
What you get
How to do it
- 1
Run bruin init and add a snowflake connection for Snowflake.
- 2
Declare an ingestion asset so raw data lands in Snowflake via an internal stage plus COPY INTO.
- 3
Add a SQL asset that models the raw table, with depends naming its upstream.
- 4
Declare column checks on the keys and amounts that matter.
- 5
Run bruin validate to confirm the graph resolves, then bruin run.
- 6
Schedule it in CI or Bruin Cloud, and switch heavy assets to MERGE on a primary key.
How it works in code
/* @bruin
name: mart.orders
materialization:
type: table
depends: [raw.orders]
columns:
- name: order_id
checks:
- name: unique
@bruin */
SELECT order_id, customer_id, order_total
FROM raw.ordersRun bruin run and Bruin builds the asset on Snowflake and runs its checks before anything downstream reads it.
Worth knowing
On Snowflake, a virtual warehouse left running with no auto-suspend bills credits while doing nothing
Other ways to do this
Bruin is not always the right answer. Here is where the alternatives are stronger.
| Option | When it is the better choice |
|---|---|
| Bruin | Build and run a complete Snowflake pipeline with Bruin: ingest, transform in SQL or Python, and check the output, all from one project. |
| dbt + Fivetran + Airflow | The conventional split. Mature and well documented, but three tools to run and integrate for one Snowflake pipeline. |
| SQLMesh | Strong on Snowflake with virtual environments that cut the compute cost of reviewing a change. Transformation only, so you still need ingestion. |
| Native Snowflake tooling | Staying inside Snowflake avoids another vendor, at the cost of portability if you ever move warehouse. |
Common questions
How do I build an end-to-end data pipeline on Snowflake?
Define each stage as an asset in one project and let the framework resolve the order. With Bruin, ingestion, SQL and Python transformations, and quality checks are all assets, and bruin run executes the graph against Snowflake. Loads use an internal stage plus COPY INTO.
Do I need an orchestrator to run Snowflake pipelines?
Not for a straightforward pipeline. bruin run resolves dependencies itself, so a CI runner on a schedule is enough. A dedicated orchestrator earns its keep once you need complex retries, backfills, and cross-team scheduling.
What is the cheapest way to run pipelines on Snowflake?
On Snowflake the main lever is warehouse auto-suspend and right-sizing, since idle compute is the usual overspend. Incremental models using MERGE on a primary key matter more than which tool you pick.
Related use cases
Run Python transformations on BigQuery
How do I run Python transformations against BigQuery?
Python transformationsRun Python transformations on Databricks
How do I run Python transformations against Databricks?
Python transformationsRun Python transformations on Postgres
How do I run Python transformations against Postgres?
One pipeline, end to end
Open source. Ingestion, SQL and Python transformations, and checks in one graph.