Advanced Time Series with Cassandra

Cassandra is a suitable database for storing time series data such as performance metrics, fleet tracking, sensor data, logs, financial data, and user activity. Two strategies are typically employed when working with time series data: index strategy and materialized view strategy. The latter provides more efficient reads by allowing fetching of a time slice of events from one set of replicas. All event data is serialized as JSON in the column values. To support queries that ask for all events before a given time, it's essential to keep track of when the timeline was first started using metadata rows. In some cases, splitting each time bucket into multiple rows can help spread the write load among more nodes in the cluster. Timelines may require different time bucket sizes based on their event rate or changes over time.