This is also true of SQL databases. It just depends on _what_ your aggregating. In this case if you need to aggregate the count of something then in a DB like M

This is also true of SQL databases. It just depends on _what_ your aggregating. In this case if you need to aggregate the count of something then in a DB like MySQL you can lean on the index to get a quick count, but if you need to SUM or AVG you will be doing just as much CPU level work as a NoSQL solution. The difference is in MySQL its a simple query with the AVG operator, in MongoDB its a Map-Reduce query which is much harder to write. In general you need to know what your doing under the hood and how either solution effects your problem domain. Where I work we need to aggregate billions of data points on demand. This can't be done in real time without pre-aggregating the results ( and even then it takes tons of I/O just to process the aggregated data set )