"Time-series data is just data where every record has a "time" field. That's it." That's a pretty big simplification. It's like saying one could go running in

"Time-series data is just data where every record has a "time" field. That's it." That's a pretty big simplification. It's like saying one could go running in dress shoes. (Yes, it's possible, but don't you want to use the right tool for the job?) As one time-series database example, because TimescaleDB [0] is focused on time-series data, its users benefit from [1]: * 40-50x compression for metrics data (so storage costs for compressed data are 2-2.5% what they would normally be) * Versatile continuous aggregate policies * Variable data retention policies * Overall much more efficient compute and memory utilization (because of faster insert and query rates) * And yes, also time-based query operators for gap filling, first/last, LOCF, etc (And I'm sure roskilli could describe M3DB's own advantages over non-time-series DBs.) [0] Disclaimer to other readers, I'm a co-founder (although OP already knows this, as we've jousted on HN before :-) ) [1] All of our benchmarks and other engineering notes are published here: https://blog.timescale.com/tag/engineering/ https://blog.timescale.com/tag/engineering/