The question I have is schema updates. The biggest pain I have had with things like Mongo is dealing with old data records. Use case example for Uber: 1. In 2

The question I have is schema updates. The biggest pain I have had with things like Mongo is dealing with old data records. Use case example for Uber: 1. In 2011, a driver joined. They made a bunch of trips 2. In 2012, Uber added more detail about the trip. Information not collected for the 2011 trips. 3. And so on, each year there are 'just a few changes' Given the above: In 2016, Uber want to run a query to reward all drivers based on some piece of information that was only present in 2014 on. At this point the historical trip information from 2011 is in a significantly different format than in 2016. In a RDB, at least the old columns are there - or if the db was migrated to a new schema ( a pain ) the issue of the missing fields was addressed. But dealing with data in old formats was an Uber pain. And the lack of visibility into just knowing the schema used to generate that JSON object is a PITA. God forbid if you had new code that never even knew about the old 2011 format. Lastly, what happens if a bug slips through and some JSON field is missing, has odd spelling ( capitalization wrong ), etc. I would love to hear about how old data is handled in schemaless. My experience with MongoDB was less than pleasant.