Kedro
awesome-workflow-engines
Kedro | awesome-workflow-engines | |
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29 | 11 | |
9,362 | 5,568 | |
0.7% | - | |
9.7 | 5.8 | |
7 days ago | 8 days ago | |
Python | Java | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Kedro
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Nextflow: Data-Driven Computational Pipelines
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).
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A Polars exploration into Kedro
# pyproject.toml [project] dependencies = [ "kedro @ git+https://github.com/kedro-org/kedro@3ea7231", "kedro-datasets[pandas.CSVDataSet,polars.CSVDataSet] @ git+https://github.com/kedro-org/kedro-plugins@3b42fae#subdirectory=kedro-datasets", ]
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What are some open-source ML pipeline managers that are easy to use?
So there's 2 sides to pipeline management: the actual definition of the pipelines (in code) and how/when/where you run them. Some tools like prefect or airflow do both of them at once, but for the actual pipeline definition I'm a fan of https://kedro.org. You can then use most available orchestrators to run those pipelines on whatever schedule and architecture you want.
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How do data scientists combine Kedro and Databricks?
We have set up a milestone on GitHub so you can check in on our progress and contribute if you want to. To suggest features to us, report bugs, or just see what we're working on right now, visit the Kedro projects on GitHub.
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How do you organize yourself during projects?
you could use a project framework like kedro to force you to be more disciplined about how you structure your projects. I'd also recommend checking out this book: Edna Ridge - Guerrilla Analytics: A Practical Approach to Working with Data
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Futuristic documentation systems in Python, part 1: aiming for more
Recently I started a position as Developer Advocate for Kedro, an opinionated data science framework, and one of the things we're doing is exploring what are the best open source tools we can use to create our documentation.
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Python projects with best practices on Github?
You can also check out Kedro, it’s like the Flask for data science projects and helps apply clean code principles to data science code.
- Data Science/ Analyst Zertifikate für den Job Markt?
- What are examples of well-organized data science project that I can see on Github?
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Dabbling with Dagster vs. Airflow
An often overlooked framework used by NASA among others is Kedro https://github.com/kedro-org/kedro. Kedro is probably the simplest set of abstractions for building pipelines but it doesn't attempt to kill Airflow. It even has an Airflow plugin that allows it to be used as a DSL for building Airflow pipelines or plug into whichever production orchestration system is needed.
awesome-workflow-engines
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Looking for an orchestration engine for HTTP requests similar to “Postman flows” I can selfhost.
I've come across this list, but I'm uncertain about which option would suit my requirements without going through a trial-and-error process. Do you have any recommendations for a tool similar to "Postman flows" that enables the creation of chained HTTP requests and allows for response analysis?
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Building a distributed workflow engine from scratch
True, there are many options out there. And we looked at a good number before we made the decision to build one ourselves. But at least at the time (circa 2014), many of the existing options were either not designed for a distributed environment, were designed more particularly for data-processing use cases, were seemingly abandoned, or simply felt over-engineered to our taste.
- Building some “marketing automation” esque features into a CRM. Am I looking for a rules engine?
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Dabbling with Dagster vs. Airflow
I'd say give Temporal (https://temporal.io) a look, but there are a lot of options (https://github.com/meirwah/awesome-workflow-engines).
- Are there any good resources for building data pipelines?
- Any bpmn engine out there?
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CSV File automation
I believe the type of software you're looking for is a workflow engine. I've found this list of them, perhaps there's some in the list that could work for your needs: https://github.com/meirwah/awesome-workflow-engines
- Looking for genuine feedback on if my idea is good or not!
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Looking for a tutorial to develop a workflow service
https://github.com/meirwah/awesome-workflow-engines links to a bunch of open source workflow engines. If you don’t find a tutorial, maybe try one of those and see if the code is small enough to read easily.
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State machines are wonderful tools
[2] https://github.com/meirwah/awesome-workflow-engines
What are some alternatives?
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
xstate - Actor-based state management & orchestration for complex app logic.
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.
RE2 - RE2 is a fast, safe, thread-friendly alternative to backtracking regular expression engines like those used in PCRE, Perl, and Python. It is a C++ library.
Dask - Parallel computing with task scheduling
common-workflow-language - Repository for the CWL standards. Use https://cwl.discourse.group/ for support 😊
cookiecutter-pytorch - A Cookiecutter template for PyTorch Deep Learning projects.
processus - A simple lightweight nodejs workflow engine designed to help orchestrate multiple tasks.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
tork-web - Web UI for Tork Workflow Engine
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
proposals - Temporal proposals