tiny-world-map
Mage
| tiny-world-map | Mage | |
|---|---|---|
| 3 | 77 | |
| 1,315 | 7,171 | |
| 18.2% | 5.1% | |
| 8.4 | 9.9 | |
| 12 days ago | about 13 hours ago | |
| HTML | Python | |
| ODC Open Database License v1.0 | 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.
tiny-world-map
- FLaNK AI-April 22, 2024
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Tiny World Map
edit: OP apparently [fixed it](https://github.com/tinyworldmap/tiny-world-map/issues/6)
6 places now, so it's [accurate to 11cm](
Mage
- FLaNK AI-April 22, 2024
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A mage on the Hero’s 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 there’s 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 isn’t well documented yet, and also https://www.neuronsphere.io - which doesn’t have a public solution YET (they’re 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 📈
7️⃣ 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
What are some alternatives?
dagster - An orchestration platform for the development, production, and observation of data assets.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
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.
mito - The mitosheet package, trymito.io, and other public Mito code.
Data-Science-Roadmap - Data Science Roadmap from A to Z
Data-science - Collection of useful data science topics along with articles, videos, and code
layerx-community - LayerX-AI is a comprehensive platform to annotate and manage your machine learning data.
superset - Apache Superset is a Data Visualization and Data Exploration Platform
datascience - Curated list of Python resources for data science.
astro-sdk - Astro SDK allows rapid and clean development of {Extract, Load, Transform} workflows using Python and SQL, powered by Apache Airflow.
versatile-data-kit - One framework to develop, deploy and operate data workflows with Python and SQL.