A use case requiring time series or log-based, event-driven data is a very good fit for Cassandra. In this case, rows are written once, never updated, and are seldom subject to random deletes. Business logic for time series data typically results in queries for data that occurred around a similar period of time, usually resulting in reads that can be served by a single node.
Datasets that are large and will grow over time are another good use case for Cassandra. This is where horizontal scalability comes into play. Additional nodes can be added over time to support an expanding data footprint. However, it is important in this scenario to ensure that your data model is designed to distribute well, while simultaneously supporting ...
