Radar check
What we found when we opened it. Every bar is one fact, with its date.
Last code pushed 26 Oct 2026, the same day before this check.
Latest release apache-hamilton-v1.90.0-incubating-RC0 on 25 Apr 2026.
Public since February 2023, a little over 3 years.
2,617 stars and 225 forks.
73 commits and 16 new issues in the last 90 days.
108 contributors. 69 pull requests merged in 90 days.
A security policy says how to report a problem. No published security advisories.
How the bars are scored
- Alive. Days since the last code push: a week or less 100, a month 90, three months 70, six months 50, a year 30, eighteen months 15, older 0.
- Releases. Age of the latest GitHub release, on a similar scale; no bar when the project publishes no releases.
- Maturity. Age of the project: eight years 100, five 90, three 70, two 55, one 40, six months 25, younger 10.
- Adoption. Stars on a log scale: 100,000 stars is 100, 10,000 is 80, 1,000 is 60, 100 is 40.
- Activity. Commits in the last 90 days: 300 or more 100, 100 85, 30 65, 10 45, any 25.
- Community. People who have contributed: 100 or more 100, 30 80, 10 60, 5 40, 2 25, one 10.
- Security. A published security policy 80, none 40; published advisories are listed, not scored.
Checked by Repo Radar on 26 Oct 2026 from the repository on GitHub.
In simple English
Data scientists and engineers often write messy scripts to move and clean data. These scripts quickly become hard to test or change. Apache Hamilton lets you write normal Python functions that point to each other using their parameter names. The library reads these functions and automatically links them into an ordered pipeline. You do not need to pay anything, and the code runs wherever Python runs. If you want to track your pipeline runs, you can also run an optional local web server.
What it is
Apache Hamilton is an open-source Python library for defining directed acyclic graphs (a way to structure tasks so data flows in one direction without loops) of data transformations. It separates how data pipelines are defined from how they are executed, so you can reuse the same code in scripts, web servers, or cloud schedulers.
What it is good for
Solo builders who need to organize complex data processing, machine learning feature engineering, or retrieval-augmented generation pipelines in Python without setting up heavy workflow tools.
- Turns regular Python functions into structured data pipelines automatically.
- Keeps your data transformation code clean and easy to unit test.
- Includes an optional web interface to track execution results and data lineage.
- Requires learning a specific function-naming convention to define dependencies.
- Only works for Python codebases, so it will not help with other languages.
- Requires a separate visualization tool installation to draw pipeline graphs.
messy procedural Python scripts and manual workflow orchestration code
pandas
- dagster-io/dagster16k stars
A heavier orchestration platform that also manages data assets and pipelines.
- dbt-labs/dbt14k stars
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- tobymao/sqlglot9.7k stars
Reads, rewrites and translates SQL between more than twenty database dialects, without a database.
- patroni/patroni8.8k stars
Watches a group of PostgreSQL copies and promotes a new leader when the leader fails.
- pgEdge/spock758 stars
An add-on that lets several PostgreSQL databases all accept changes and keep each other in step.
source venv/bin/activate pip install "apache-hamilton[visualization]"