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Top 14 Python Workflow engine Projects
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luigi
Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Kedro
Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
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couler
Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.
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SaaSHub
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SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Project mention: Building in Public: Leveraging Tublian's AI Copilot for My Open Source Contributions | dev.to | 2024-02-12Contributing to Apache Airflow's open-source project immersed me in collaborative coding. Experienced maintainers rigorously reviewed my contributions, providing constructive feedback. This ongoing dialogue refined the codebase and honed my understanding of best practices.
Project mention: Ask HN: What is the correct way to deal with pipelines? | news.ycombinator.com | 2023-09-21I agree there are many options in this space. Two others to consider:
- https://airflow.apache.org/
- https://github.com/spotify/luigi
There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file showing up in a directory…
Project mention: Prefect: A workflow orchestration tool for data pipelines | news.ycombinator.com | 2024-03-13
Interesting, thanks for sharing. I'll definitely take a look, although at this point I am so comfortable with Snakemake, it is a bit hard to imagine what would convince me to move to another tool. But I like the idea of composable pipelines: I am building a tool (too early to share) that would allow to lay Snakemake pipelines on top of each other using semi-automatic data annotations similar to how it is done in kedro (https://github.com/kedro-org/kedro).
Project mention: Ask HN: Anyone use a code to mindmap/flowchart tool? | news.ycombinator.com | 2024-02-24https://github.com/django-extensions/django-extensions/blob/...
viewflow supports BPMN: https://github.com/viewflow/viewflow
Maybe it would help you to look at the galaxy project: GitHub main site
author seems to be describing the kind of patterns you might make with https://argoproj.github.io/argo-workflows/ . or see for example https://github.com/couler-proj/couler , which is an sdk for describing tasks that may be submitted to different workflow engines on the backend.
it's a little confusing to me that the author seems to object to "pipelines" and then equate them with messaging-queues. for me at least, "pipeline" vs "workflow-engine" vs "scheduler" are all basically synonyms in this context. those things may or may not be implemented with a message-queue for persistence, but the persistence layer itself is usually below the level of abstraction that $current_problem is really concerned with. like the author says, eventually you have to track state/timestamps/logs, but you get that from the beginning if you start with a workflow engine.
i agree with author that message-queues should not be a knee-jerk response to most problems because the LoE for edge-cases/observability/monitoring is huge. (maybe reach for a queue only if you may actually overwhelm whatever the "scheduler" can handle.) but don't build the scheduler from scratch either.. use argowf, kubeflow, or a more opinionated framework like airflow, mlflow, databricks, aws lamda or step-functions. all/any of these should have config or api that's robust enough to express rate-limit/retry stuff. almost any of these choices has better observability out-of-the-box than you can easily get from a queue. but most importantly.. they provide idioms for handling failure that data-science folks and junior devs can work with. the right way to structure code is just much more clear and things like structuring messages/events, subclassing workers, repeating/retrying tasks, is just harder to mess up.
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
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Index
What are some of the best open-source Workflow engine projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | Airflow | 34,627 |
| 2 | luigi | 17,356 |
| 3 | Prefect | 14,724 |
| 4 | Kedro | 9,374 |
| 5 | viewflow | 2,562 |
| 6 | galaxy | 1,315 |
| 7 | couler | 890 |
| 8 | NIPY | 734 |
| 9 | redun | 489 |
| 10 | jug | 406 |
| 11 | flowsaber | 40 |
| 12 | BPMN_RPA | 37 |
| 13 | typhoon-orchestrator | 29 |
| 14 | pyDag | 24 |
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