Mage
Airflow
Mage | Airflow | |
---|---|---|
77 | 169 | |
7,050 | 34,485 | |
3.5% | 1.1% | |
9.9 | 10.0 | |
2 days ago | 7 days ago | |
Python | Python | |
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.
Mage
- FLaNK AI-April 22, 2024
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A mage on the Heroâs Journey: a fantasy epic on how a startup rose from the ashes
In the coming years, Mage will create a cooperative experience so that developers can build data pipelines with their team and level up together. After that journey, Mage will go on an epic quest to create the 1st open world community experience in the data universe.
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Data sources episode 2: AWS S3 to Postgres Data Sync using Singer
Link to original blog: https://www.mage.ai/blog/data-sources-ep-2-aws-s3-to-postgres-data-sync-using-singer
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What are some open-source ML pipeline managers that are easy to use?
I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home
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Mage Battlegrounds: Craft insights from real-time customer behavior analysis
You're invited to participate in the very first Mage Battlegrounds: Craft insights from real-time customer behavior analysis, a 24-hour virtual hackathon hosted by Shashank Mishra! This data engineering competition will take place on Saturday, April 15, 2023 beginning at 11am (PST). This will be a global event open to all participants who register.
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Looking for an open-source project
Try this feature: https://github.com/mage-ai/mage-ai/issues/1166
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Daskqueue: Dask-based distributed task queue
Seeing if we can use it in https://github.com/mage-ai/mage-ai
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Data Pipeline on a Shoestring
That being said thereâs a solid family of services just breaking ground that make the local pipeline deployment easier (check out https://www.mage.ai, which does have a clear path to cloud deployment of locally developed pipes, it just isnât well documented yet, and also https://www.neuronsphere.io - which doesnât have a public solution YET (theyâre internally testing an alpha) but they built a cloud deployable solution for their paying customers and working to release one for freemium use)
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Trending ML repos of the week đ
7ïžâŁ mage-ai/mage-ai
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Delta without using Spark
Yes, check out how Mage does it: https://github.com/mage-ai/mage-ai/tree/master/mage_integrations/mage_integrations/destinations/delta_lake_s3
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 directoryâŠ
- "VocĂȘ veio protestar para ter acesso ao cĂłdigo fonte da urnas. O que Ă© o cĂłdigo fonte?" "NĂŁo sei" đ€Ą
- CĂłmo 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?
dagster - An orchestration platform for the development, production, and observation of data assets.
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.
vscode-dvc - Machine learning experiment tracking and data versioning with DVC extension for VS Code
sqlmesh - Efficient data transformation and modeling framework that is backwards compatible with dbt.
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
mito - The mitosheet package, trymito.io, and other public Mito code.
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.
Data-Science-Roadmap - Data Science Roadmap from A to Z
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
Data-science - Collection of useful data science topics along with articles, videos, and code
Dask - Parallel computing with task scheduling