I run it on VM the same way as a few separate PostgreSQL instances. TimescaleDB is an extension to PostgreSQL, so there's a lot of information, guides, and community support online, and Postgres is one of the most widely used databases right now. There are some things to setup but Tiger data documentation is very good.

I've tested the migration of the main table (IoT data), from 300GB to about 25GB in a non-production environment. Everything seems right, but I'm still delaying/procrastinating the prod migration, so I wanted to know how it is going for other live projects.

Okay maybe title is not great but I really achieved this compression on real database where I have devices via MQTT connected.

If I understand your question correctly: For storage, mostly yes. Swinging-door compression went lossy because byte compression on raw floats used to be useless, but with new method like Gorilla you can compress it lossless, so you can keep every sample and still afford it. But there is another point that this Aveva historian adds filter that filters some points so I don't know how it impact ingest.

But in general now trend is to use normal databases from IT world in OT world to overcome some legacy solutions.

>But in general now trend is to use normal databases from IT world in OT world to overcome some legacy solutions.

Someone tell my company this please ;-;