Summary

In this chapter, we laid down a general foundation for data modeling that should give you the tools you need to correctly reason about your specific use cases. We have covered a lot of ground, including Cassandra's storage engine and how your CQL gets translated to that underlying model, as well as a guide for modeling time series and geospatial data.

But there are also a number of mistakes people make when modeling data for Cassandra, and we will talk about these in the next chapter on antipatterns. Make sure to read on, so that you can avoid these common pitfalls.