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Tinyfan

Tinyfan: Minimalist Data Pipeline Kit - Generate Argo Workflows with Python

Features

  • Generate Argo Workflows manifests from Python data pipeline definitions.
  • Ease of Use and highly extendable
  • Intuitive data model abstraction: let Argo handle orchestration—we focus on the data.
  • Argo Workflows is notably lightweight and powerful – and so are we!
  • Enhanced DevOps Experience: easly testable, Cloud Native and GitOps-ready.

Our Goal

  • Minimize mental overhead when building data pipelines.

Not Our Goal

  • Full-featured orchestration framework: We don't aim to be a battery-powered, comprehensive data pipeline orchestration solution.
    No databases, web servers, or controllers—just a data pipeline compiler. Let's Algo Workflows handle all the complexity.

Installation

# Requires Python 3.10+
pipx install tinyfan

Tiny Example

# main.py

# Asset definitions

from tinyfan import asset

@asset(schedule="*/3 * * * *")
def world() -> str:
    return "world"

@asset()
def greeting(world: str):
    print("hello " + world)
# Apply the changes to argo workflow

tinyfan main.py | kubectl apply -f -

For a package, pass its directory or import name. Tinyfan statically discovers modules containing @asset, so unrelated submodules are not executed during manifest generation. Modules imported by an asset module (for example, a shared Flow definition) are loaded normally.

tinyfan src/my_package

If a project registers assets dynamically or wraps @asset in another decorator, use --import-all to opt into the previous eager-import behavior.

Real World Example (still tiny though!)

Comming soon

License

This project is licensed under the MIT License.

Metadata

Release files for tinyfan 0.4.0

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