Great Expectations
The most widely used open-source tool for writing data quality checks in Python — you define what your data should look like as code, and it validates every pipeline run against those expectations automatically.
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Description
Before most commercial data quality platforms existed, data engineers were already writing custom Python scripts to check that a column wasn't null or that a number fell in an expected range. Great Expectations grew out of exactly that instinct and turned it into a proper open-source framework: you write 'expectations' — data checks, essentially — as code, and the tool validates every pipeline run against them, generating human-readable reports when something fails.
It integrates directly with the tools data engineers already use — Airflow for orchestration, and warehouses like Databricks, BigQuery, and Snowflake — rather than asking you to route data through a separate platform. With a community of 11,000+ practitioners, it's become something of a default answer for teams that want data validation without buying a commercial platform. GX Core (the open-source library) is free under Apache 2.0; GX Cloud adds a hosted layer with a free Developer tier and paid Team/Enterprise tiers for collaboration features, though exact pricing on those isn't publicly listed.
💬 Our review
The short version: Great Expectations is the natural starting point if your team is comfortable writing Python and doesn't want a subscription — it's free, well-established, and integrates with tools you're probably already using.
Against commercial platforms like Soda, Anomalo, or Monte Carlo, Great Expectations trades a lower (zero) cost for more manual setup — you write the expectations yourself rather than getting AI-driven anomaly detection out of the box. Against Soda specifically, which offers a friendlier no-code interface on its Team tier, Great Expectations stays closer to a code-first, engineer-facing tool throughout, which is exactly right for some teams and a real barrier for others without Python-comfortable staff. For a team with data engineers who can write and maintain expectations as code, it's genuinely hard to beat on cost — the main ongoing cost is engineering time rather than a subscription, which is a fundamentally different trade-off than every quote-based competitor in this list. <!-- ai-generated -->
💰 Pricing
📊 Global score
🤖 AI-enriched data
GX Core gratuit (Apache 2.0) ; GX Cloud Developer gratuit ; Team/Enterprise sur devis
Pros
100% gratuit et open-source (core)
Communauté large et établie
Intégrations natives Airflow/Databricks/Snowflake
Cons
Nécessite d'écrire du code Python
Pas de détection d'anomalies IA automatique
Prix GX Cloud Team/Enterprise non publics