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dagster-pandera

Integration layer for dagster and pandera.

dagster-pandera v0.29.18 127.1K downloads/30d#11,753 on PyPI15,996
Permissive license Apache-2.0 Active released

What it is and what it does

dagster-pandera is a bridge between two Python data tools: Dagster (a declarative data orchestrator) and Pandera (a schema validation library). It lets you attach Pandera schema checks to Dagster assets, so your data pipeline can validate that outputs match expected column types, constraints, and structure before downstream tasks consume them.

The package is maintained as part of the Dagster project and provides decorators or asset checks that integrate Pandera's validation rules into Dagster's asset graph. Rather than writing separate validation logic, you define a Pandera schema once and apply it to your Dagster assets, combining orchestration and data quality in a single declarative model. It assumes you are already using both Dagster for pipeline management and Pandera for schema definition.

Use it for:

  • Validate DataFrame outputs from Dagster assets match expected column names, types, and nullable constraints before passing to downstream tasks.
  • Catch data quality regressions early by running Pandera schema checks as part of asset materialization in a Dagster pipeline.
  • Enforce schema contracts across team-owned data assets in a shared Dagster instance without duplicating validation logic.
  • Build reusable schema definitions in Pandera and apply them consistently across multiple Dagster assets in the same project.
  • Flag data anomalies (missing columns, type mismatches, constraint violations) in production pipelines with Dagster's observability.

Worth the install?

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

Integrates Pandera data validation with Dagster's asset orchestration, enabling schema validation checks within data pipeline workflows.

Yes, if you are already using both Dagster and Pandera. The integration is actively maintained, has no known vulnerabilities, and low install friction. It is worth installing to avoid writing custom validation wrappers. If you use only Dagster or only Pandera, this package adds no value.

Install

dagster-pandera on PyPI

pip

pip install dagster-pandera

uv

uv add dagster-pandera

poetry

poetry add dagster-pandera

Installing dagster-pandera

Before you install

Low friction install with a pure Python wheel. Active maintenance with a recent release and strong upstream project health (15996 GitHub stars, last commit 2026-08-14).

License in practice

Apache-2.0 permissive license allows use in commercial and open-source projects without copyleft obligations.

Quickstart

pip install dagster-pandera

import dagster as dg
from dagster_pandera import pandera_asset_check
import pandera as pa

# Use pandera_asset_check decorator to validate asset outputs against a schema

Requires Python 3.10 or later (supports up to 3.14); dagster and pandera must be installed as runtime dependencies.

Verify before relying

  • Specific validation patterns and decorators available in this integration version
  • Whether asset checks run inline or as separate observability steps
  • Performance overhead of schema validation at orchestration time
  • Support for custom Pandera validators and error handling strategies

Package facts

License Apache-2.0 (permissive)
Python support supports the current Python release (<3.15,>=3.10)
Install friction low — pure-Python wheel
Runtime dependencies 3 — dagster, pandas, pandera
Maintenance actively maintained — 0 days since the last release
Last repo commit
First released
Downloads 127,140/month — #11,753 on PyPI (30-day window, as of 2026-08-14)
Known vulnerabilities none known (OSV.dev, checked 2026-08-14)

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

Tags

dagster pandera integrationdata validation in dagsterschema validation orchestrationpandera asset validationdagster data quality checkspipeline schema enforcementvalidated data assets
data-validationorchestrationschema-checking

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