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reminds me of this tweet [0] Them: Can you just quickly pull this data for me? Me: Sure, let me just: SELECT * FROM some_ideal_clean_and_pristin
by mritchie712 3y ago
reminds me of this tweet [0]
Them: Can you just quickly pull this data for me?
Me: Sure, let me just:
SELECT * FROM some_ideal_clean_and_pristine.table_that_you_think_exists
GPT-4 is good on a single CSV, but breaks down quickly applied to a real database / data warehouse. I know they're using multiple tables in the paper, but it appears to be a pristine schema that's very easy to reason about. In the real world, when you're trying to join postgres to hubspot and stripe data, an LLM isn't able to write the SQL from scratch get the right answer.
We're working on an approach using a semantic layer at https://www.definite.app/ https://www.definite.app/ if you're interested in this sort of thing.
0 - https://twitter.com/sethrosen/status/1252291581320757249?lang=en https://twitter.com/sethrosen/status/1252291581320757249?lan...
- deleted 3y ago[deleted]
- neeleshs 3y agoIt goes beyond just joining postgres to hubspot and stripe even when humans are doing it. Typos in source systems, duplicative data, unwarranted prefixes, suffixes, stuff you don't care about, columns named c0,c1,c2 etc. A semantic layer is just really all about defining data models in the domain of interest. It's the hardest part in dealing with data strategies, very manual, very company and process and history specific. Once it's defined, the next set of tasks is to make sure that the data in the model is correct and coherent. And only then, querying this data, applying ML etc start becoming worthwhile. We at https://syncari.com https://syncari.com take the centralized data model centric approach. https://www.definite.app/ https://www.definite.app/ also looks very cool!