On the other hand, our Presto cluster runs pretty much anything we throw at it, and when it fails, the failures are easier to anticipate and mitigate. It's also quite simple to deploy and operate.

Apart from adding new features (e.g., ACID support), a lot of effort is still put into optimizing queries and runtime. In essence, Hive is a tool specialized for SQL, so it tries to implement all the optimizations you can think of in the context of executing SQL queries. For example, Hive implements vectorized execution, whose counterpart in Spark was implemented only in a later version (with the introduction on Tungsten IIRC). Hive even supports query re-execution: if a query fails with a fatal error like OOM, Hive re-generates a new query after analyzing the runtime statistics collected by then. The second query usually runs much faster, and you can also update the column statistics in Metastore.

In contrast, Spark is a general-purpose execution engine where SparkSQL is just an application. I remember someone comparing Spark to Swiss army knife, which enables you to do a lot of things easily, but is no match against specialized tools developed for a particular task. My (opinionated) guess is that SparkSQL will be replaced by Hive and Presto, and Spark streaming will be replaced by Flink.

I think that now, maintaining that compatibility is less of a need for Spark and Hive has introduced a lot of goodies in the meantime, so there might not be a need for the SQL flavors to be in lockstep anymore.