There are a whole bunch of attempts to unify metrics, logs and traces into a single DB now.

* InfluxDB (the newest Rust rewrite)

* http://openobserve.ai/

* https://uptrace.dev/

* Clickhouse powered solutions (eg https://signoz.io)

* ... ?

I'm quite skeptical about the "store raw data" approach. It makes querying much more complex and slower, storage much more expensive, etc.

Columnar databases that can store the data very efficiently are the way to go, IMO. They can still benefit from cheap long-term storage like S3.

In the article the "Materialized view for data derivation" part does the heavy lifting.

I assume this means they are creating time-series (indices) on-the-fly (with eventual backfill of the data). For the "exploratory analytics" the techniques developed for Dremel/Drill/Impala [0] are sufficient, and for anything else raw data crunching speeds are really impressive nowadays. (And they claim they can ingest 1B JSON records in ~10-30 seconds [1].)

[0] https://en.wikipedia.org/wiki/Dremel_(software)

[1] https://greptime.com/blogs/2025-03-18-jsonbench-greptimedb-p...