This is actually an underrated question. Time-series databases offer better performance and usability for dealing with time-series data (think DevOps metrics,

This is actually an underrated question. Time-series databases offer better performance and usability for dealing with time-series data (think DevOps metrics, data from IoT devices, stock prices etc, anything where you're monitoring and analyzing how things change over time) They allow you answer questions where time is the main component of interest much more quickly and easily: eg 1: IoT Sensors) Show me the average of temperature over all my devices over the past 3 days in 15 minute intervals eg 2: Financial data) What's the price of stock X over the past 5 years eg 3: DevOps data) What's the average memory and CPU used by all my servers of the past 5 mins A normal database could be a purely relational database (e.g Postgres) or a non-relational database (e.g MongoDB). In both these cases, while you could use these databases for time-series data, they tend to offer worse performance at scale and a worse experience for doing common things (e.g real-time aggregations of data, data retention policies etc) For more on time-series data and when you'd need a time-series database, check out: https://blog.timescale.com/blog/what-the-heck-is-time-series-data-and-why-do-i-need-a-time-series-database-dcf3b1b18563/ https://blog.timescale.com/blog/what-the-heck-is-time-series...