Great Expectations

Open source

Open-source framework for validating and documenting data quality in pipelines.

by Great ExpectationsOfficial site ↗Source code ↗

Pricing: free
Free tier: true
License: open-source
Open source: Yes

Editorial review

Great Expectations has not yet been editorially reviewed. User reviews and published field data below are unaffected — we say so rather than implying an editorial verdict.

Facts & attributes

Every value carries its evidence state. “—” means unknown — never a silent default (P-02).

Identity · Taxonomy

Documentation(HTTPS URL): https://docs.greatexpectations.io/docs/Verified
Tags(open-source;cloud-native): data quality, data validation, open source, Python, data engineeringVerification pending

Platform · Deployment

Delivery model(SaaS;self-hosted;desktop;mobile;embedded;managed-cloud;appliance): self-hostedVerification pending

Pricing · Licensing

Free plan(true/false): trueVerification pending
License model(proprietary;open-source;source-available;dual-license): open-sourceVerification pending
Pricing model(free;subscription;usage;seat;asset;transaction;revenue-share;perpetual;custom): freeVerified
Source repository(HTTPS URL): https://github.com/great-expectations/great_expectationsVerified
SPDX license IDs(Apache-2.0;MIT): Apache-2.0Verification pending

Features · Product · Fit

Core capabilities(authoring;automation;analytics): define Expectations for data quality, validate data from Python and Jupyter notebooks, Data Docs rendering of visualizations and plain-language test results, Actions for automated responses alongside orchestrators, prevent bad data from entering data sources and moving downstreamVerification pending

Support · Docs · Services

Support channels(email;ticket;chat;phone;community): Slack community, Discourse forum, documentationVerified

Audience · Use · Case

Best for(Neutral editorial summary): Data engineering teams validating pipeline data in Python with an open-source framework and auto-generated Data Docs.Verification pending
Company-size fit(individual;SMB;mid-market;enterprise): individual, SMB, mid-market, enterpriseVerification pending
Primary use cases(build;monitor;automate): data validation in pipelines, shared data quality toolset for technical teams, stakeholder communication of test resultsVerification pending
Skill level(beginner;intermediate;advanced;specialist): advancedVerification pending

Lifecycle · Versioning

Maintenance status(active;maintenance;deprecated;EOL;archived): activeVerified

Vendor · Maintainer

Vendor type(commercial;foundation;community;government;consortium;individual): commercialVerification pending

User reviews

Review policy

No published reviews yet.