Mage
jq
Mage | jq | |
---|---|---|
77 | 306 | |
7,050 | 25,063 | |
3.5% | - | |
9.9 | 0.0 | |
7 days ago | 11 months ago | |
Python | C | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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,Ā 2024
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A mage on the Heroās 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 thereās 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 isnāt well documented yet, and also https://www.neuronsphere.io - which doesnāt have a public solution YET (theyāre 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 š
7ļøā£ 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
jq
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GNU Parallel, where have you been all my life?
That should recursively list directories, counting only the files within each, and outputĀ² jsonl that can be further mangled within the shellĀ². You could just as easily populate an associative array for further work, or $whatever. Unlike bash, zsh has reasonable behaviour around quoting and whitespace too.
Ā¹ https://zsh.sourceforge.io/Doc/Release/User-Contributions.ht...
Ā² https://github.com/jpmens/jo
Ā³ https://github.com/stedolan/jq
- How do i edit reputation?
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Jj: JSON Stream Editor
What I miss from jq and what is implemented but unreleased is platform independent line delimiters.
jq on Windows produces \r\n terminated lines which can be annoying when used with Cygwin / MSYS2 / WSL. The '--binary' option to not convert line delimiters is one of those pending improvements.
https://github.com/stedolan/jq/commit/0dab2b18d73e561f511801...
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Building and deploying a web API powered by ChatGPT
If you have jq installed you can use it to make the output look nicer.
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Search in your Jupyter notebooks from the CLI, fast.
It requires jq for JSON processing and GNU parallel for concurrent searches in the notebooks.
- Check the jq manual!
- mkv vs mp4 metadata
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Amazon Begs Employees Not to Leak Corporate Secrets to ChatGPT
jq is your friend.
- Memes are all cool and all. But this is your daily remaining that 10000! =
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How to export/import/externally-edit/whatever WI entries?
The jq command (https://stedolan.github.io/jq/) is useful pulling that information out.
What are some alternatives?
dagster - An orchestration platform for the development, production, and observation of data assets.
yq - Command-line YAML, XML, TOML processor - jq wrapper for YAML/XML/TOML documents
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
dasel - Select, put and delete data from JSON, TOML, YAML, XML and CSV files with a single tool. Supports conversion between formats and can be used as a Go package.
vscode-dvc - Machine learning experiment tracking and data versioning with DVC extension for VS Code
gojq - Pure Go implementation of jq
sqlmesh - Efficient data transformation and modeling framework that is backwards compatible with dbt.
json5 - JSON5 ā JSON for Humans
mito - The mitosheet package, trymito.io, and other public Mito code.
jp - Validate and transform JSON with Bash
Data-Science-Roadmap - Data Science Roadmap from A to Z
nushell - A new type of shell