Comparison
great_expectations vs automl-gs
Verdict
Pick great_expectations if great Expectations is a Python library that helps maintain data quality through unit testing mechanisms known as expectations; pick automl-gs if automl-gs: Python tool for automated machine-learning model creation from CSV data.
Markdown twin · great_expectations alternatives · automl-gs alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | great_expectations | automl-gs |
|---|---|---|
| Maintenance | Very active (0d since push) As of 3w · github_public_v1 | Dormant (2477d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | Published findings As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- great_expectations
- Always know what to expect from your data
- automl-gs
- Automatically generate machine-learning models and code with input CSV and target field
Stars
- great_expectations
- 12k
- automl-gs
- 1.9k
Forks
- great_expectations
- 1.8k
- automl-gs
- 181
Open issues
- great_expectations
- 39
- automl-gs
- 28
Language
- great_expectations
- Python
- automl-gs
- Python
Adopt for
- great_expectations
- Great Expectations is a Python library that helps maintain data quality through unit testing mechanisms known as expectations.
- automl-gs
- automl-gs: Python tool for automated machine-learning model creation from CSV data
Persona
- great_expectations
- -
- automl-gs
- -
Runtime
- great_expectations
- -
- automl-gs
- -
License
- great_expectations
- Great Expectations is available under the Apache-2.0 license.
- automl-gs
- MIT
Last pushed
- great_expectations
- Aug 2, 2026
- automl-gs
- Oct 22, 2019
Categories
- great_expectations
- Data & Retrieval
- automl-gs
- Data & Retrieval, Model Training
Trust and health
Maintenance
- great_expectations
- Very active (96%)
- automl-gs
- Dormant (18%)
Days since push
- great_expectations
- 0d
- automl-gs
- 2477d
Open issues (now)
- great_expectations
- 39
- automl-gs
- 28
Owner type
- great_expectations
- Organization
- automl-gs
- User
Full report
- great_expectations
- Trust report
- automl-gs
- Trust report
Choose great_expectations if…
- License: great_expectations is Apache-2.0, automl-gs is MIT.
- Requirements: Supports Python versions 3.10 through 3.13, with experimental support for Python 3.14 and later via an environment variable..
- Tags unique to great_expectations: data-engineering, data-quality, exploratory-data-analysis, mlops.
- When you need detailed and automated documentation for each set of validation results to simplify your data quality processes while preserving institutional knowledge.
When NOT to use great_expectations
- For environments that strictly require adherence to Python versions 3.9 or lower, since Great Expectations supports only 3.10 through 3.13 natively.
- If your data integration requirements are not compatible with those listed in the Great Expectations compatibility reference.
Choose automl-gs if…
- License: automl-gs is MIT, great_expectations is Apache-2.0.
- Tags unique to automl-gs: automl, keras, machine-learning, python.
- Also covers Model Training.
- Need to rapidly prototype models with limited ML expertise
When NOT to use automl-gs
- Complex feature engineering or non-standard data inputs required
- Sensitive about licensing of the generated code
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (fivetran/great_expectations) · observed Aug 2, 2026
- GitHub forks (fivetran/great_expectations) · observed Aug 2, 2026
- Last push (fivetran/great_expectations) · observed Aug 2, 2026
- License file (Apache-2.0) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (minimaxir/automl-gs) · observed Aug 4, 2026
- GitHub forks (minimaxir/automl-gs) · observed Aug 4, 2026
- Last push (minimaxir/automl-gs) · observed Oct 22, 2019
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: great_expectations 12k · automl-gs 1.9k (synced Aug 2, 2026).
Common questions
- What is the difference between great_expectations and automl-gs?
- great_expectations: Always know what to expect from your data. automl-gs: Automatically generate machine-learning models and code with input CSV and target field. See the comparison table for live GitHub stats and shared categories.
- When should I choose great_expectations over automl-gs?
- Choose great_expectations over automl-gs when License: great_expectations is Apache-2.0, automl-gs is MIT; Requirements: Supports Python versions 3.10 through 3.13, with experimental support for Python 3.14 and later via an environment variable.; Tags unique to great_expectations: data-engineering, data-quality, exploratory-data-analysis, mlops; When you need detailed and automated documentation for each set of validation results to simplify your data quality processes while preserving institutional knowledge.
- When should I choose automl-gs over great_expectations?
- Choose automl-gs over great_expectations when License: automl-gs is MIT, great_expectations is Apache-2.0; Tags unique to automl-gs: automl, keras, machine-learning, python; Also covers Model Training; Need to rapidly prototype models with limited ML expertise.
- When should I avoid great_expectations?
- For environments that strictly require adherence to Python versions 3.9 or lower, since Great Expectations supports only 3.10 through 3.13 natively. If your data integration requirements are not compatible with those listed in the Great Expectations compatibility reference.
- When should I avoid automl-gs?
- Complex feature engineering or non-standard data inputs required Sensitive about licensing of the generated code
- Is great_expectations or automl-gs more popular on GitHub?
- great_expectations has more GitHub stars (11,690 vs 1,869). Stars measure visibility, not whether either tool fits your constraints.
- Are great_expectations and automl-gs open source?
- Yes - both are open-source projects on GitHub (great_expectations: Apache-2.0, automl-gs: MIT).
- Where can I find alternatives to great_expectations or automl-gs?
- GraphCanon lists graph-backed alternatives at great_expectations alternatives and automl-gs alternatives (great_expectations markdown twin, automl-gs markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, great_expectations or automl-gs?
- great_expectations: Very active. automl-gs: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for great_expectations and automl-gs?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: great_expectations trust report; automl-gs trust report.