If not having to adjust queries is a major driver for your considerations, then I would highly recommend looking at SQLGlot (https://github.com/tobymao/sqlglot), a transpiler that makes you (more) independent of query dialects. They already support 30 dialects (big vendors such as Snowflake, Databricks, BigQuery, but also loads of the specialists such as ClickHouse, SingleStore or Exasol). Repo is maintained extremely well.

Picking the best solution for your concrete workload (and your future demands) should be equally important to the implementation effort, to avoid that you run into walls later on. At least as long as data volume, query complexity or concurrency scalability can be challenges.

Are you using a transpiler technology such as SQLGlot for the multi-vendor SQL generation?

I do agree. I still think that the article articulates a very interesting thought... the better the input for a problem, the better the output. This applies both to LLMs but also for colleagues.

When people complain about large language models, I often feel like they're complaining about their coworkers without realizing it...

I do agree. SQL is simply an access API for so many systems, and nice as it's a declarative language rather than a normal programming language. LLMs are super powerful to express questions to data that can then be translated into SQL.

For translation between dialects, you could use projects such as SQLGlot. The advantage of SQL is the standardization over many decades (yes, I know that it's still a mess with the different dialects).

SQLGlot is amazing. In many ways it helps erase the differences and bridge between dialects. It is so useful for moving complex queries between platforms.

Agreed, and it's an amazingly well-maintained GitHub repo: https://github.com/tobymao/sqlglot

Big kudos to Toby and the team.

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