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Fast, accurate and scalable probabilistic data linkage with support for multiple SQL backends
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You should try it out and see. Here are the test cases
https://github.com/tobymao/sqlglot/blob/main/tests/fixtures/...
I can even parse and optimizer TPC-H https://github.com/tobymao/sqlglot/blob/main/tests/fixtures/....
SQLGlot's parser handles a superset of SQL, so it's much more flexible than any one engine.
SQLGlot is great. We've used it to build a FOSS probabilistic data linking library[1] that's capable of executing against a variety of SQL backends (Spark, Presto, DuckDB, Sqlite), significantly widening our potential user base.
We implement all our logic in SQL, and then use SQLGlot to transpile to the target execution engine. One big motivation was to futureproof our work - we're no longer tied down to Spark, and as the 'next big thing' (GPU accelerated SQL for analytics?) comes along, it should be relatively straightforward to support it by writing another adaptor.
Working on this has highlighted some of the really tricky problems associated with translating between SQL engines, and we haven't hit any major problems, so kudos to the author!
[1] https://github.com/moj-analytical-services/splink/tree/splin...
