How did you come up with 0.3% number of bodies / maximum possible infections, aka total population? I'm trying to wrap my head around this whole mess...
Take Lombardy, hardest hit region in Europe. 1130 deaths / million, or about 0.1% death rate. All German states are under 100 deaths per million, which is less than 0.01% death rate. Sanity check: Bayern, 1.2k deaths/13M people ~ 0.01%.
Of course, this is a lower bound estimate assuming all people in a given region have got through the disease. Which is definitely not true. To figure out how bad is going to get we need the total number of infected people. Hopefully the random testing strategy will shed some light on that number. But at least we start [much] lower than 0.3%.
Normalizing daily deaths to population, Sweden is doing worse than California. Both are doing much better that Lombardy or NY. Somewhat encouraging, the # of daily deaths is decreasing across the board. Possibly a mix between [voluntary] lockdown effects and better palliative treatments.
[Disclaimer: I worked on BigQuery a couple lives ago]
I'd give Google BigQuery a shot. Should work fast [seconds] and scale seamlessly to [much] larger datasets and [many] more users. For a 1 TB dataset, I have a hard time imagining crafting a slow query. Maybe something outlandish like 1000[00?] joins. They also have an in-memory "BI Engine" offering, alas limited to 50GB max.
On premise, there is Tableau Data Engine. I don't think they offer a SQL interface, you have to buy into the entire ecosystem.
Long shot: I've been working on "most expressive query system over multiple tables" as an offshoot of some recent NLP work. Your use case piqued my interest. I'd love to help / understand it better. My contact is in my profile.