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The Pipeline🛠️Repos Showdown⚔️: Python 🐍 Edition


In the data engineering and automation ever-evolving landscape, Python has seen several worflow orchestrators emerge. In this article, I will cover 6 Python libraries and some of their main features.

Introduction GIF

1. Taipy

Taipy is an open-source Python library for building production-ready applications front-end & back-end.
For Python developers, Taipy is one of the easiest frameworks to create pipelines thanks to its pipeline graphical editor (Taipy Studio).
Then, from a Python script, you can easily execute and orchestrate the pipelines. A really cool central feature is that each pipeline execution is registered.
This enables easy what-if analysis, KPI monitoring, data lineage, etc.

🔑 Features:

  • Graphical Pipeline Editor
  • Integration with Taipy Front-end capabilities for an end-to-end deployment
  • Scheduling
  • Versioning of pipelines
  • Smart features like caching

Taipy illustration


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2. Kedro

Kedro is an open-source Python framework.
It provides a toolbox for production-ready data science pipelines.
Indeed, Kedro easily integrates with well-established Python ML libraries and provides a unified way to implement an end-to-end framework.

🔑 Features:

  • Data Catalog
  • Notebooks integration
  • Project template
  • Opinionated as it enforces specific conventions

Kedro illustration

3. Airflow

Airflow has been a well-known actor in the pipeline landscape for over a decade.
Airbnb created airflow to address the internal challenges of data processing and workflow needs.
This robust open-source platform is known to have a steep learning curve but with an extensive array of capabilities.
The platform allows you to create and manage workflows through building DAGs (directed acyclic graphs).

🔑 Features:

  • DAG-based definition
  • Rich web-based UI for monitoring: visualization of DAGs, failures, retries…
  • Various integration
  • Dynamic task execution and scheduling
  • Flexible thanks to its Python-centric identity.
  • Strong community

Airflow illustration

4. Prefect

Prefect is a data pipeline development framework.
Prefect strategically positions itself in direct competition with Airflow, standing out with a distinctive identity based on simplicity, user-friendliness, and flexibility.
Prefect is a good in-between if you want a mature product with various features but an easier learning curve than Airflow.

🔑 Features:

  • Control panel
  • Caching
  • Flow-based structure
  • Dynamic parametrization & dependency management
  • Hybrid execution ( Local/Cloud)

Prefect illustration

5. Dagster

Dagster, one of the newer libraries in this compilation, is a cloud-native data pipeline orchestration aiming to unify data integration, workflow orchestration, and monitoring.
In comparison to other tools, Dagster places an emphasis on the DataOps aspect of the workflow creation and management.

🔑 Features:

  • Declarative pipeline setup
  • Opinionated structure
  • Versioning
  • Integration with Hadoop
  • Comprehensive metadata tracking

Dagster illustration

6. Luigi

Luigi provides a data processing pipeline framework. Spotify developed this library around the same time as Airflow to tackle their complex data workflows and pipelines.
Luigi was explicitly designed for managing complex channels of batch jobs. Luigi is a good option if you are looking for something simple and have to get started quickly.

🔑 Features:

  • Built-in Hadoop support
  • Task-based workflow definition
  • Central scheduler for dependency management
  • Visualization for task dependencies

Luigi illustration


As this Python workflow orchestration landscape continuously evolves, these tools showcase major common characteristics and specific differentiators.
All these tools have different levels of complexity, and it’s essential to understand your project and team’s needs.
I recommend testing some options with very straightforward examples to gain a firsthand understanding of each framework’s usability.

Hope you enjoyed this article!

I’m a rookie writer and would welcome any suggestions for improvement!

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Feel free to reach out if you have any questions.

Top comments (12)

rym_michaut profile image

Really like this one... didn't know there were that many in the Python data orchestration world.

Save it for later, thank you!

matijasos profile image
Matija Sosic

A good one, this series keeps getting better and better :)

marisogo profile image

Thanks for the pressure haha 😉

nevodavid profile image
Nevo David

Great list!
Thank you for posting!

proteusiq profile image
Prayson Wilfred Daniel

You bet me to it. I had planned to write the same 😃☺️. Just addition for ML specific pipelines

mosenturm profile image
Andreas Kaiser

Great article. But do not forget!

marisogo profile image

Noted! Thanks Andreas

fernandezbaptiste profile image

Saved it!

biplobsd profile image
Biplob Sutradhar

👍 list

srbhr profile image
Saurabh Rai

Python data processing and orchestration pipelines are on the rise!!

aleajactaest78 profile image

Oh, I discovered some of them for the first time! Thanks for the list!

proteusiq profile image
Prayson Wilfred Daniel • Edited

I started my pipeline journey with Airflow, then Dagster and now Kendro. I still love Dagster UI and repository linkage.

We are missing kestra which is unique due to it yaml declarations that looks like GitHub Actions

Thank you 🙏🏾 for sharing