Data Pipelines

Hamilton

Lightweight library for defining data transformations as DAGs.

Status
Generally available
Last checked
Checked · Oct 10, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

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.

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.

When it fits — and when it doesn’t

A good fit for

  • Developers who need to create clear and maintainable data processing pipelines.

  • Data science teams working on feature engineering for machine learning models.

Look elsewhere if

  • Projects requiring real-time streaming data processing (Hamilton is batch-oriented).

  • Teams looking for a fully managed service solution.

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Limited integrations with other data processing tools

    Lack of native support for popular ETL frameworks like Apache Airflow or Luigi

  2. 02

    Small community and slower issue resolution

    GitHub issues have longer response times compared to more established libraries

What it does well

  • Defines data transformations as DAGs for clarity and maintainability.

  • Simplifies complex data processing workflows.

  • Supports feature engineering and machine learning pipelines.

Pricing

Free tier
YesOpen source — free to use
Paid from
$0
Model
Flat rate
Enterprise plan
No

Pricing not independently confirmed — check the vendor’s page.

Performance benchmarks

How Fast Is It?

Set up Hamilton

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →
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