I use TimescaleDB to store data from logs (syslog, http logs, render logs), timeseries host metrics (CPU, Disk, Memory, Network), system inventory data, FDW to

I use TimescaleDB to store data from logs (syslog, http logs, render logs), timeseries host metrics (CPU, Disk, Memory, Network), system inventory data, FDW to LDAP, network flows, etc. I do analysis across multiple dimensions for a variety of business uses. 1. Render job logs, combined with host metrics to determine if any systems are being under utilized. 2. Intranet network usage for monitoring purposes. 3. General monitoring of log events, system metrics, etc with alerting based on specific query criteria. 4. Temperature / Humidity monitoring and trend analysis. Basically if there are things that happen somewhat often (http logs, system logs, host metrics, power usage, temperature change, etc) and you want to analyze that data, and one of the factors in your analysis is time, then time series databases are for you. In the end each TSDB has different trade offs, and there are a number of considerations to make, and how you weigh them will change a lot depending on scale. If you are writing 1-20GB/Day vs 1-20TB/Day, you will have different considerations. If you have relatively simple queries vs complex queries with complex joins on other data you will have different considerations. In the end you need to have some idea what your data is, how much of it there is, what the growth looks like and what your write/query usage patterns are going to look like. Insert / Query performance isn't everything, unless for your business it is.