dagster-snowflake-pandas

What it is and what it does

This package is a Dagster integration that bridges Snowflake and Pandas, letting you declare data assets in Python that read from or write to Snowflake and process data with Pandas. It sits on top of Dagster's core orchestration engine, which manages scheduling, dependency resolution, and observability for your data pipelines.

You use it by decorating Python functions as Dagster assets, specifying Snowflake as a resource or I/O manager, and letting Dagster handle the rest—running your functions at the right time, tracking lineage, and surfacing data quality issues. It's designed for teams building and maintaining data tables, datasets, and reports that live in Snowflake, with the flexibility to transform and analyze them locally in Pandas or push computation back to Snowflake.

Use it for:

• Build and maintain a Snowflake data warehouse with Dagster orchestrating daily or hourly refreshes of fact and dimension tables.
• Extract data from Snowflake, transform it with Pandas locally, and load results back for reporting or downstream consumption.
• Define a multi-stage asset graph where some stages run SQL in Snowflake and others use Pandas for statistical modeling or feature engineering.
• Monitor data quality and lineage across Snowflake tables using Dagster's built-in observability and asset catalog.
• Integrate Snowflake pipelines into a CI/CD workflow, running tests and staging deployments before production rollout.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Integrates Snowflake and Pandas with Dagster's data pipeline orchestration, enabling you to define, run, and observe data assets built on Snowflake and processed with Pandas.

Yes, if you are building data pipelines on Snowflake and want Dagster's orchestration, observability, and asset management. The package is actively maintained, has low install friction, carries a permissive license, and integrates two mature ecosystems (Dagster and Snowflake). Install it when you need to move beyond ad-hoc SQL scripts or simple schedulers and want declarative, testable, lineage-aware data asset management.

Install

dagster-snowflake-pandas on PyPI

pip install dagster-snowflake-pandas

uv add dagster-snowflake-pandas

poetry add dagster-snowflake-pandas

Installing dagster-snowflake-pandas

Before you install

Low install friction with a pure-Python wheel. Actively maintained with a recent release and strong repository signals (15996 stars, last commit 2026-08-14). Depends on dagster, pandas, and Snowflake connectors—all stable, widely-used libraries.

License in practice

Apache 2.0 permissive license allows use in commercial and private projects with minimal restrictions; you must retain license notices and disclaim warranties.

Quickstart

pip install dagster-snowflake-pandas

import dagster as dg
from dagster_snowflake_pandas import snowflake_resource

@dg.asset
def my_snowflake_asset() -> pd.DataFrame:
    # Define and return your data asset
    pass

Requires Python 3.10 or later (supports up to 3.14). Snowflake account credentials and network access to Snowflake must be configured.

Verify before relying

• Specific Snowflake connector version compatibility and any known limitations with particular Snowflake editions.
• Whether the package includes built-in I/O managers for Snowflake or requires custom configuration.
• Performance characteristics and scalability limits for large datasets or high-frequency asset refreshes.

Package facts

Evidence: dagster_snowflake_pandas-0.29.18-py3-none-any.whl

Tags

More Distributed Computing packages