A time series database (TSDB) offers a convenient perspective on modeling tasks involving time-varying entities. Unlike traditional entities, which model an instantaneous view, time-varying entities describe the variations of all their time-varying attributes over time. TSDBs have a more sophisticated relationship modeling than entities, distinguishing between relationships between static entities, time-varying entity and static ones, and two time-varying entities. The relationship cardinality is orthogonal to the discriminatory aspect of whether time-varying attributes characterize the relationship. Modeling these complex relationships requires advanced techniques, including windowing and pivoting, as demonstrated in the provided InfluxDB query example. Effective data modeling involves mapping time-varying entities into TSDBs while preserving identifying nature of attribute and using tags for performance optimization.