To operate tools on data stored on disk you always have to create in memory definitions or bitmaps first.
MakeTableView is much simpler, works on one table and is more reliable. It can make a subset of records and a subset of fields, but sometimes other tools get confused eg Intersect overlays.
MakeQueryTable attempts to set up a relational database expression across several tables in the same database. It may work for sample sets, but never works for me with real sized datasets. After trying it for a while I avoid it at all costs. Often it just hangs or crashes after hours.
So what to do? If you are using Model Builder or are trying to do interactive selections you are stuck with it. Simplify and reduce first by exporting to a temporary featureclass or partition into smaller sets and repeat.
If you are using Python there are much better options. You can copy the data straight into Numpy or Pandas and run python functions. Note SQL expressions have to be all in one database. Break this by extracting the whole dataset use a SearchCursor() then use python set functions to do joins. This means you are not limited to one database. You can use python dictionaries that are very fast lookups and can be very large say 1 million records. I often use list comprehensions with a SearchCursor to extract the table. Then loop through another table in another database with the dictionary. Alternatively I use a huge list with an SQL IN clause to do the selection, make sure the field is indexed in the database and you will have your selection in milliseconds compared to hours with MakeQueryView.
