I tried using MongoDB for the customer-facing analytics of a large e-commerce marketplace. It didn't work very well. The problem is that at some point you end up wanting joins.<p>MongoDB was actually the third try. My first two attempts were BigQuery and Keen, neither of which worked out because they support only one index - time. Users want to slice and dice by various axes! And there's an obvious additional index you need - "merchant" - which column stores usually say propose setting up isolated partitions for. If you do that, you can't ask questions across the whole system!<p>We ended up with Postgres. It was actually faster than MongoDB for simple aggregations, and joins made it much better/faster for complicated queries. Of course it only works quickly if your dataset fits in RAM, but terabyte-size instances are pretty affordable and give you a lot of headroom.<p>That was a couple years ago. I don't know what they're using now, probably the same. It was a frantic few weeks figuring out what was going to work - each of those systems made it to production and quickly discovered to be inadequate in vivo. If you're in a startup, even if you're using exotic NoSQL systems like Google Cloud Datastore or DynamoDB - just use Postgres or MySQL for analytics. It will work long enough for you to figure out something else when you need it.