sickbeard_mp4_automator VS amundsen

I have been using MP4 automator for over 5 years; https://github.com/mdhiggins/sickbeard_mp4_automator . I choose MP4 as it works on preety much any device i have ever tryied, H264, AAC stereo and AC3 5.1. Have also used Tdarr for H265 conversion; it really uses 35-50% less space for same quality (4k and I wasnt able to see difference).

They do extended versions of some existing containers that add extra stuff. I use sabnzbd-extended for the SMA support.

SMA, Automatically converts media files to a standardized format, and tags them with the appropriate metadata from TMDB if the container supports tagging. Made to integrate with Radarr, Sonarr, etc. but can be run stand alone. Python required (3+, although 2.7 was still supported, last I knew). Free. https://github.com/mdhiggins/sickbeard_mp4_automator

Sickbeard mp4 Automator for media conversation and metadata matching. Combined with mediaelch for bulk renaming and poster downloads, they’re essential for plex hosting. Also use subler for quick metadata/language code fixes.

You can use something like the following to automate the transcoding: https://github.com/mdhiggins/sickbeard_mp4_automator

Going back to https://github.com/mdhiggins/sickbeard_mp4_automator, I was following the steps for sonarr/radarr and went to add a connection (custom script). Browsed to /usr/local/sma/postSonarr.py and clicked the test button, and I got an error that the script exited with -1. I am wondering if it has something to do with this weird instruction from the github page:

We'll be using WSL2 for this guide, and we'll start by cloning this repo and its submodules:

You can use Amundsen data builder to send data to Apache Atlas, https://github.com/amundsen-io/amundsen/blob/main/databuilder/example/scripts/sample_atlas_search_extractor.py If you don’t have to configure Apache Atlas then why not, but the server side validation the last time when I used it was absent. You couldn’t validate the JSON body sent to the REST API endpoints.

Other than that, maybe you could try amundsen (https://github.com/amundsen-io/amundsen/issues/608) which now has a connector to extract delta lake metadata via Spark.

How does it differ from something like Amundsen : https://github.com/amundsen-io/amundsen

Metadata Engine: - Datahub https://github.com/linkedin/datahub - Amundsen https://github.com/amundsen-io/amundsen/ - Marquez https://marquezproject.github.io/ - Egeria - Open Metadata and Governance https://egeria.odpi.org

A final category worth highlighting is Discovery, where it seems every notable company developed an internal Data Catalogue tool that now is available as an open-source or paid service. Some examples are Amundsen (Lyft), Datahub (LinkedIn), Metacat (Netflix), Databook (Uber), and Dataportal (Airbnb).