You see this often in the data side of things, we delegate very laborious or very complicated tasks to powerful the powerful engines behind Postgres, BigQuery, Spark, and the like, and while they provide you with a high level interface that make common tasks very convenient, mastery can take years as you have to study the architecture and implementation behind them.
It's a huge investment on your end to do that, and people do it readily because the return is being able to process datasets of sizes and complexity that you otherwise wouldn't be able to.
People haven't found it to be a good investment in their time to commit themselves to Prolog's engine.
It's a huge investment on your end to do that, and people do it readily because the return is being able to process datasets of sizes and complexity that you otherwise wouldn't be able to.
People haven't found it to be a good investment in their time to commit themselves to Prolog's engine.