However, databases also started being increasingly used for analytics, which has very different access patterns compared to OLTP. Usually an analytic query scans over a huge number of records, and calculates aggregate statistics (such as count, sum, or average) rather than returning the individual records to the user. For example, a business analyst at a supermarket chain may want to answer analytic queries such as:
What was the total revenue of each of our stores in January?
How many more bananas than usual did we sell during our latest promotion?
Which brand of baby food is most often purchased together with brand X diapers?
The reports that result from these types of queries are important for business intelligence, helping the management decide what to do next. In order to differentiate this pattern of using databases from transaction processing, it has been called online analytic processing (OLAP)