ETL stands for Extract, Transform and Load. So this is a data pipeline framework. Nextdoor please put that in your blog post as I do not believe it is a very common acronym.
It's not that uncommon, especially in data science/analytics/engineering. I've definitely heard "ETL" more often that data pipeline or analytics pipeline.
It’s not a small niche. Search any job board and ETL will show up in a large presentage of job posts and resumes related to software QA and data validations.
It is the kind of acronym that is known by anyone who needs it, modulo those two random people somewhere in the world who just discovered today that they need it but haven't googled the problem yet.
Admittedly, it is also the sort of acronym that, when you don't know it, is really annoying in a headline.
I'll leave you with a link to Wikipedia [1]. ETL goes back a long, long way, and is still commonly used all through the industry, except maybe in startups composed of all young, inexperienced people.
I would have myself made the mistakes of assuming the terms is ubiquitous and everyone already knows it. I have heard it used liberally at every job I've done in the last 15 years.
I would have termed it a "very very common acronym." Right up there with API.
But your post reminds me that not everyone's experiences are the same.
Great work. I'm in the process of writing an ETL and while I don't think this will suite our needs (so unfortunately I need to keep writing) this article provides a lot of great detail that helped me see the process more clearly.