Exploring the Collective Knowledge of Databases

This section is all about the implementation of what we've learned in this chapter. So far, we have gone through the nitty-gritty of databases and discussed MySQL for RDBMS and NoSQL databases such as MongoDB and Cassandra. Now, let's take the FashionMart store example and think about a scenario where we have the following schema:

Figure 5.68: Star schema of FashionMart

In a case where the number of daily active users is 1 million and the Sales data is around 5 billion, then performing joins using MySQL between the dimensions will cause serious overhead. But here, the benefit is consistency, so if you want to update a Sales record, then it will be referenced in your next query.

Now, think about solving this use case using MongoDB. If we embedded a Sales entity inside Products by denormalizing the sales data, then it will drastically reduce latency as no data is shuffling or joins over the network...