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That all sounds exciting!

I'm not a server side person but from my experience, other types of data app developer might want to join are various kinds of product-specific data, like feature flags or user-specific dimensions for each request. These aren't typically in the trace data itself.

These are different from what product-agnostic "performance engineers" tend to look into, so I understand if this is out of scope. Although I think product people should look into these numbers as well, instead of just throwing them into the performance team's plate :-/

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It depends on what exactly you are referring to.

I should have mentioned that correlating observability data (or sometimes product metrics collected via Prometheus) with product data (or any other data really like business data) can be super useful and totally possible with Promscale because PostgreSQL is under the hood. So you could have a copy that data into the same PostgreSQL instance used by Promscale or maybe use Foreign Data Wrappers (1). This would allow you to analyze, for example, api request latency by product plan the customer is subscribed to or based on which feature flags are enabled for their account, etc. without having to add all those attributes as labels to all your metrics which can be technically complex and also costly.

[1] https://www.postgresql.org/docs/current/ddl-foreign-data.htm....

We actually do this within Timescale Cloud, and it's amazing.

It allows us to cohort performance data across data stored in others microservice databases (e.g., by account types, projects, billing data, etc.). JOINs across foreign data wrappers using TimescaleDB + Postgres, all within the database and without ETL or application code needed.

So you could look at Prometheus data for your trialers vs. customers, for customers running more than X services, for customers that pay more than $X per month or have been a customer for more than 6 months, etc.

It's super useful across operations, support, product, customer care, and more...

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