This is exactly what I've tried (and failed at) doing! Can I ask how you handle normalization from vendor data when it contains relationships and multilevel nesting? How do you know when to create a new child table, and which ones to create, and their relationships etc. I haven't found a good balance yet.
Question-- with the yelp api now being paywalled-- and obviously you'd have to write code to handle scraping-- but can you legally scrape yelp to do this?
Does the linkedin scraping lawsuit permit scraping yelp?
> Scanning and updating every value because you need to update some subset sucks.
Mirrors my experience exactly. Querying json can get complex to get info from the db. SQLite is kind of forgiving because sequences of queries (I mean query, modify in appliation code that fully supports json ie js, then query again) are less painful meaning it's less moprtant to do everytning in the database for performance reasons. But if you're trying to do everything in 1 query, I think you pay for it at application-writing time over and over.
> These days, my go-to solution is SQLite with two fields (well, three, if you count the implicit ROWID, which is invaluable for paging!): ID and Data, the latter being a JSONB blob.
Really!? Are you building applications by chance or something else? Are you doing raw sql mostly or an ORM/ORM-like library? This surprises me because my experience dabbling in json fields for CRUD apps has been mostly trouble stemming from the lack of typechecks. SQLite's fluid type system haa been a nice middle ground for me personally. For reference my application layer is kysely/typescript.
Assume there's just a 0.1% of a meltdown and you're better off sticking to Vanguard ETFs.
https://archive.is/G3cYF