Data Modeling: Part 2 — Method for Time Series Databases

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.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.