awesome-flake8-extensions
Airflow
awesome-flake8-extensions | Airflow | |
---|---|---|
4 | 169 | |
1,193 | 34,570 | |
- | 1.4% | |
6.4 | 10.0 | |
about 1 month ago | 3 days ago | |
Python | ||
GNU General Public License v3.0 or later | 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.
awesome-flake8-extensions
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A Tale of Two Kitchens - Hypermodernizing Your Python Code Base
Ultimately we want to test our code with Flake8 and plugins to enforce a more consistent code style and to encourage best practices. When you first introduce flake8 or a new plug-in commonly you have a lot of violations that you can silence with a #noqa comment. When you first introduce a new flake8 plugin, you will likely have a lot of violations, which you silence with #noqa comments. Over time these comments will become obsolete because you fixed the. yesqa will automatically remove these unnecessary #noqa comments.
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Python toolkits
flake8 for linting along with following plugin (list of awesome plugin can be found here, but me and my teammates have selected the below one. Have linting but don't make it too hard.) flake8-black which uses black for code formatting check. flake8-isort which uses isort for separation of import in section and formatting them alphabetically. flake8-bandit which uses bandit for security linting. flake8-bugbear for finding likely bugs and design problems in your program. flake8-bugbear - Finding likely bugs and design problems in your program. pep8-naming for checking the PEP-8 naming conventions. mccabe for Ned’s script to check McCabe complexity flake8-comprehensions for writing better list/set/dict comprehensions.
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Write better Python - with some help!
In addition to this out of the box -linting, there are loads of flake8 extensions that can help you with for example switching from .format() to using f-strings or checking that your naming follows the PEP8 guidelines. For example, adding flake8-length adds line length checking to the linting.
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Standards to be aware of
And if you're using flake8, make sure to check out its plugins. Here's a good list: https://github.com/DmytroLitvinov/awesome-flake8-extensions
Airflow
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Building in Public: Leveraging Tublian's AI Copilot for My Open Source Contributions
Contributing 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.
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Navigating Week Two: Insights and Experiences from My Tublian Internship Journey
In week Two, I contributed to the Apache Airflow repository.
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Airflow VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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Best ETL Tools And Why To Choose
Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows.
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Simplifying Data Transformation in Redshift: An Approach with DBT and Airflow
Airflow is the most widely used and well-known tool for orchestrating data workflows. It allows for efficient pipeline construction, scheduling, and monitoring.
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Share Your favorite python related software!
AIRFLOW This is more of a library in my opinion, but Airflow has become an essential tool for scheduling in my work. All our ML training pipelines are ordered and scheduled with Airflow and it works seamlessly. The dashboard provided is also fantastic!
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Ask HN: What is the correct way to deal with pipelines?
I 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…
- "Você veio protestar para ter acesso ao código fonte da urnas. O que é o código fonte?" "Não sei" 🤡
- Cómo construir tu propia data platform. From zero to hero.
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Is it impossible to contribute to open source as a data engineer?
You can try and contribute some new connectors/operators for workflow managers like Airflow or Airbyte
What are some alternatives?
black - The uncompromising Python code formatter
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.
unimport - :rocket: The ultimate linter and formatter for removing unused import statements in your code. [Moved to: https://github.com/hakancelikdev/unimport]
dagster - An orchestration platform for the development, production, and observation of data assets.
pep8-naming - Naming Convention checker for Python
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
pyre-check - Performant type-checking for python.
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.
flakes - list of flake8 plugins and their codes
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
portray - Your Project with Great Documentation.
Dask - Parallel computing with task scheduling