The Pipeline🛠️Repos Showdown⚔️: Python 🐍 Edition

#python #github #programming #pipeline

TL;DR

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.

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

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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

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

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)

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

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

Conclusion

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!

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