Looker performance comes from the underlying storage of data. You can store massive amounts of data on say a Red Shift cluster and still be performant. Most visualization tools that i know are directly tied to storage tier in terms of performance.
That's correct. We (I work at Looker) are written to leverage the analytical capabilities of SQL. So we support all kinds of SQL implementations, from totally standard (PG, MySQL, MS) to MPPs (Redshift, BigQuery) to SQL-on-Hadoop (Hive, Impala, Presto, Spark).
But the JDBC -> Druid connectors that exist look pretty janky. So if someone builds a stable connector, I suspect we'd support it. But for the moment, no Druid.
