For federated queries: “N” requires all data from the source to be copied into SQL Server 2016 and then filtered. For “Y”, the query is pushed down into the data source and only the results are returned back, which can be much faster for large amounts of data.
I mention “Maybe” for age out data in SQL DW as you can use PolyBase to access the aged-out data in blob or Azure Data Lake Storage (ADLS), but it will have to import all the data so may have slower performance (which is usually ok for accessing data that is aged-out). For SQL Server 2016, it will have to import the data unless you use HDP/Cloudera, in which case the creation of the MapReduce job will add overhead.
The thing that I like about this chart is that the new Polybase sources (SQL Server, Oracle, Teradata, Mongo, and generic ODBC) do support predicate pushdown. For large data sets, that’s huge: it lets the database engine on the opposite end do as much filtering as possible before sending results back to your SQL Server head node.