Hamilton
Lightweight library for defining data transformations as DAGs.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is Hamilton?
Hamilton is a lightweight Python library that helps developers author reliable feature engineering and machine learning pipelines by defining data transformations as directed-acyclic graphs (DAGs). It simplifies the creation of complex data processing workflows, ensuring clarity and maintainability.
Key differentiator
“Hamilton stands out by providing a lightweight, Python-based approach to defining complex data transformations as DAGs, focusing on clarity and maintainability in feature engineering and machine learning pipelines.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Lack of native support for popular ETL frameworks like Apache Airflow or Luigi
GitHub issues have longer response times compared to more established libraries
Fit analysis
Who is it for?
✓ Best for
Developers who need to create clear and maintainable data processing pipelines.
Data science teams working on feature engineering for machine learning models.
✕ Not a fit for
Projects requiring real-time streaming data processing (Hamilton is batch-oriented).
Teams looking for a fully managed service solution.
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Works well with
Next step
Get Started with Hamilton
Step-by-step setup guide with code examples and common gotchas.