That said I find it's fairly tedious to do a lot of time-series analysis and pattern discovery/anomaly detction across rich event models (think aws cloudtrail events).
Anything TimescaleDB can help with here? Are there case studies you can point us to? It feels like there is probably home for both just in my domain and quite obviously in the broader context of large enterprise ops/security.
Here is a doc on using TimescaleDB as a horizontally-scalable, easy-to-deploy, operationally-mature data store for Prometheus data (i.e., metrics), put together by another of our engineering teams:
Building an open-source analytical platform for Prometheus
I'm also happy to discuss privately if you'd prefer - ajay (at) timescale.com.
ShiftLeft - code analysis and security scanning to catch vulnerabilities [https://blog.shiftleft.io/time-series-at-shiftleft-e1f981969...]
k6 - a load testing tool that scales to 100k concurrent users, analyzes performance over time, etc. [https://www.timescale.com/case-studies/k6]
If you want to talk specific scenarios, you can reach out alex @ timescale or on Slack - slack.timescale.com.
I haven't done anything with regards to anomaly/trend detection yet, but it's planned. Not really sure where you see a database (TimescaleDB) fitting into that though?
I feel that we could probably use a time-series database to reflect our streams as 'last observed state' type collections as well as do the aggregations that we need to feed back into anomaly detection.
I'd like to also use something like that to create a 'heat map service' where you can feed a property/window/range and get back scalar for color coding and possibly a slice of values for sparkline type UI.
Without getting hands on, though, it's hard to say for sure.