Yeah, I was pondering a few months away when I checked Pathway, an ETL solution.
I've never heard about it but I saw some news that they have created a better model than transformer. So stats:
A co-creator of Pathway here - noticed some inbound from the HN link and was a bit shocked to see the discussion. Of course, Airflow is one of the most widely used technologies out there, even if sometimes considered part of the "legacy" orchestration stack.
For software that is anything other than a modifiable template, stars and forks typically serve users as bookmarks. In the distant past, forks were the prevailing bookmark mechanism. Users who joined Github long after the star functionality was added generally prefer stars over forks for bookmarking. Personally, when evaluating current production use of a project, I index most strongly on the count and type of issues raised.
As for the Pathway framework specifically, if you dive in, the star-gazer demographics is split roughly evenly between: (A) folks, like parent, who heard about the tech one way or another and thought it was cool enough to engage with; and (B) participants of bootcamps and hackathons based on this framework and run in partnerships with top-tier universities globally. The license also restricts forking, and is less welcoming to external contributions than Apache projects.
- link: https://github.com/pathwaycom/pathway
- watch: 115, fork: 1.6k, star: 63.5k
- issues: 32, PR-s: 3
And compare to other ETL tool, like Apache Airflow - used by me and many machine learning folks:
- link https://github.com/apache/airflow
- watch: 777, forks 16.9k!!!!!, Stars: (only!) 45.1k
- issues: 1200 (!!!), PR-s (501!!!).