Great Expectations is an open-source Python framework for validating, documenting, and profiling data quality. Teams define expectations as expressive unit tests for their data using an intuitive API, then validate datasets against those rules in CI/CD pipelines or production workflows. It connects to pandas, Spark, and SQL sources, generates data documentation automatically, and integrates with orchestrators like Airflow and Prefect for continuous data quality monitoring.
Alternatives to Dagster
2 editor-selected alternatives · Dagster overview →
source: tools.alternatives · stored order · active records only; review scores are annotations and never change membership or order
A directional evidence panel appears only when the substitute rationale, trade-offs, sources, and verification date have been recorded. Older selections without that panel remain visible but are unclassified under the new evidence contract.
Meltano is a declarative, code-first data integration engine with 500+ Singer connectors for building ELT pipelines. It replaces custom API integration code with configuration-driven pipeline definitions that live in version control alongside application code. Integrates with dbt for transformation, supports scheduling and monitoring through a unified CLI, and powers production pipelines at scale.
Open-source Dagster alternatives
Great Expectations, Meltano — see all open-source developer tools.
More DevOps & Deployment tools
same category, not editor-selected alternatives — see how Dagster compares →
Dagster head-to-head
FAQ
Which Dagster alternative is listed first?
Great Expectations is first in the editor-selected list of 2 Dagster alternatives. The stored order is editorial; review scores do not determine membership or position.
Are there open-source Dagster alternatives?
Yes — Great Expectations, Meltano are open source.