MarineTL;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 Python app builders to use for creating 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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Feel free to reach out if you have any questions.
