When should I use Hamilton instead of Kedro?

Defining dataflow as pure Python functions where dependencies are resolved automatically by name. Feature engineering pipelines where lineage, documentation, and reusability across contexts matter. Teams who want to reuse the same function graph for training, serving, and batch processing

When should I use Kedro instead of Hamilton?

Structuring and standardizing ML and data science projects for reproducibility and production readiness. Teams who want a framework to enforce modularity, data catalog management, and pipeline documentation. Transitioning notebook-based experiments into maintainable, testable Python pipeline code

What are the main weaknesses of Hamilton?

Small community with fewer tutorials, integrations, and Stack Overflow answers than mainstream tools. No built-in scheduler — requires Airflow, Prefect, or a similar tool for recurring execution. Mental model shift required: functions define graph nodes implicitly, not through explicit wiring

What are the main weaknesses of Kedro?

Opinionated project structure feels rigid for simple use cases or small scripts. Not a scheduler — needs Airflow, Prefect, or a similar tool to run pipelines on a schedule. Node, pipeline, and catalog abstraction has a learning curve that can slow initial productivity

Hamilton vs Kedro: Key Differences for Python Data Engineering

Orchestration Tools

Hamilton

DAG-Based Data Transformation Library

★ 4.2

BSD-3-Clause

pip install sf-hamilton

Kedro

Python Data Pipeline Framework

★ 4.4

Apache-2.0

pip install kedro

Side-by-Side Comparison

Hamilton

Kedro

Hamilton

Kedro

Best For

✓Defining dataflow as pure Python functions where dependencies are resolved automatically by name
✓Feature engineering pipelines where lineage, documentation, and reusability across contexts matter
✓Teams who want to reuse the same function graph for training, serving, and batch processing

✓Structuring and standardizing ML and data science projects for reproducibility and production readiness
✓Teams who want a framework to enforce modularity, data catalog management, and pipeline documentation
✓Transitioning notebook-based experiments into maintainable, testable Python pipeline code

Best For

✓Defining dataflow as pure Python functions where dependencies are resolved automatically by name
✓Feature engineering pipelines where lineage, documentation, and reusability across contexts matter
✓Teams who want to reuse the same function graph for training, serving, and batch processing

✓Structuring and standardizing ML and data science projects for reproducibility and production readiness
✓Teams who want a framework to enforce modularity, data catalog management, and pipeline documentation
✓Transitioning notebook-based experiments into maintainable, testable Python pipeline code

Weaknesses

•Small community with fewer tutorials, integrations, and Stack Overflow answers than mainstream tools
•No built-in scheduler — requires Airflow, Prefect, or a similar tool for recurring execution
•Mental model shift required: functions define graph nodes implicitly, not through explicit wiring

•Opinionated project structure feels rigid for simple use cases or small scripts
•Not a scheduler — needs Airflow, Prefect, or a similar tool to run pipelines on a schedule
•Node, pipeline, and catalog abstraction has a learning curve that can slow initial productivity

Weaknesses

•Small community with fewer tutorials, integrations, and Stack Overflow answers than mainstream tools
•No built-in scheduler — requires Airflow, Prefect, or a similar tool for recurring execution
•Mental model shift required: functions define graph nodes implicitly, not through explicit wiring

•Opinionated project structure feels rigid for simple use cases or small scripts
•Not a scheduler — needs Airflow, Prefect, or a similar tool to run pipelines on a schedule
•Node, pipeline, and catalog abstraction has a learning curve that can slow initial productivity

License

BSD-3-Clause

Apache-2.0

License

BSD-3-Clause

Apache-2.0

Install

pip install sf-hamilton

pip install kedro

Install

pip install sf-hamilton

pip install kedro

Rating

★ 4.2

★ 4.4

Rating

★ 4.2

★ 4.4

Key Features

Hamilton

1Micro-framework where Python function names define the data dependency DAG
2Functions with typed inputs and outputs automatically form a directed graph
3Executors: sync, async, Spark, Dask, and Ray for scalable execution
4Built-in lineage tracking and documentation generation from docstrings
5Feature engineering-focused with pandas, Polars, and numpy support

Kedro

1Modular pipeline framework with a catalog-based data management layer
2Node and Pipeline abstractions separate data logic from orchestration
3DataCatalog decouples dataset reading/writing from pipeline code
4Built-in support for Jupyter, Airflow, Prefect, and Kubeflow runners
5Project template enforces consistent structure across data science projects

How Python Data Engineers Use These Tools

Hamilton

Python data engineers use Hamilton to define feature engineering pipelines where each column or feature is a Python function whose parameters declare its dependencies. Hamilton builds the DAG from function signatures automatically — engineers add a new feature by writing a new function, and Hamilton wires it into the execution graph without any manual DAG configuration.

Kedro

Data engineering teams use Kedro to structure ML and analytics pipelines as modular, testable Python functions. The DataCatalog allows engineers to define data sources (S3 Parquet files, SQL tables, local CSVs) in a YAML config — switching environments just changes the catalog config, not the pipeline code. Nodes are pure Python functions that are easy to unit test.

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Individual Tool Pages

View Hamilton details →View Kedro details →

Side-by-Side Comparison

Hamilton

Kedro

Hamilton

Kedro

Best For

✓Defining dataflow as pure Python functions where dependencies are resolved automatically by name
✓Feature engineering pipelines where lineage, documentation, and reusability across contexts matter
✓Teams who want to reuse the same function graph for training, serving, and batch processing

✓Structuring and standardizing ML and data science projects for reproducibility and production readiness
✓Teams who want a framework to enforce modularity, data catalog management, and pipeline documentation
✓Transitioning notebook-based experiments into maintainable, testable Python pipeline code

Best For

✓Defining dataflow as pure Python functions where dependencies are resolved automatically by name
✓Feature engineering pipelines where lineage, documentation, and reusability across contexts matter
✓Teams who want to reuse the same function graph for training, serving, and batch processing

✓Structuring and standardizing ML and data science projects for reproducibility and production readiness
✓Teams who want a framework to enforce modularity, data catalog management, and pipeline documentation
✓Transitioning notebook-based experiments into maintainable, testable Python pipeline code

Weaknesses

•Small community with fewer tutorials, integrations, and Stack Overflow answers than mainstream tools
•No built-in scheduler — requires Airflow, Prefect, or a similar tool for recurring execution
•Mental model shift required: functions define graph nodes implicitly, not through explicit wiring

•Opinionated project structure feels rigid for simple use cases or small scripts
•Not a scheduler — needs Airflow, Prefect, or a similar tool to run pipelines on a schedule
•Node, pipeline, and catalog abstraction has a learning curve that can slow initial productivity

Weaknesses

•Small community with fewer tutorials, integrations, and Stack Overflow answers than mainstream tools
•No built-in scheduler — requires Airflow, Prefect, or a similar tool for recurring execution
•Mental model shift required: functions define graph nodes implicitly, not through explicit wiring

•Opinionated project structure feels rigid for simple use cases or small scripts
•Not a scheduler — needs Airflow, Prefect, or a similar tool to run pipelines on a schedule
•Node, pipeline, and catalog abstraction has a learning curve that can slow initial productivity

License

BSD-3-Clause

Apache-2.0

License

BSD-3-Clause

Apache-2.0

Install

pip install sf-hamilton

pip install kedro

Install

pip install sf-hamilton

pip install kedro

Rating

★ 4.2

★ 4.4

Rating

★ 4.2

★ 4.4

Key Features

Hamilton

1Micro-framework where Python function names define the data dependency DAG
2Functions with typed inputs and outputs automatically form a directed graph
3Executors: sync, async, Spark, Dask, and Ray for scalable execution
4Built-in lineage tracking and documentation generation from docstrings
5Feature engineering-focused with pandas, Polars, and numpy support

Kedro

1Modular pipeline framework with a catalog-based data management layer
2Node and Pipeline abstractions separate data logic from orchestration
3DataCatalog decouples dataset reading/writing from pipeline code
4Built-in support for Jupyter, Airflow, Prefect, and Kubeflow runners
5Project template enforces consistent structure across data science projects

How Python Data Engineers Use These Tools