[stickfigure]

Having implemented effectively the same customer-facing analytics problem in BQ, Keen, Mongo, and Postgres, I'll tell you specifically:

* Column stores like BQ and Keen don't let you efficiently slice and dice data by factors other than time. If you're slicing by customer or product, your queries become incredibly slow and expensive. You start writing hacky shit like figuring out when your customer's first sale was so you can narrow the time slightly, but that barely helps.

* MongoDB doesn't do joins. So you denormalize big chunks of your data, and now you have update problems because 1) you have to hunt all that down and 2) you don't have transactions that span collections. Also the aggregation language is tedious compared to SQL, requiring you to do most of the work of a query planner yourself.

* Some other person in this thread said MongoDB was faster than Postgres, but I found quite the opposite to be true. For the same real-world workload, basic aggregations on an index, we found Postgres to be much faster than Mongo. No idea what that other person is talking about.

[lomnakkus]

Very well put... and this was the point I was making about "decent" performance. If you have super-special requirements (you don't), you'll probably discover it along the way to SUCCESS. If you don't any old SQL database will probably be more than sufficient AND it will be flexible enough to allow you to evolve your schema along the way.