What I would say is that documentation is never in production, but the semantic layer always is. Therefore, it is kept up to date, or people's reports don't do what they want. Whereas, with documentation it goes stale without anyone realising.
Thanks for linking the article from Rohan, this is stellar!
Sadly it looks like not much has happened since 2022 when I wrote this Metric Layer overview, except of course the acquisition of Transform/MetricFlow.
Linking the original article in the case anyone interested:
Great article! The challenge with both options is the need to manually document the semantic layer. AI can help... a little... but you need a dedicated solution for it. I actually just share some content about it today: https://journey.getsolid.ai/p/semantic-layer-for-ai-lets-not-make?r=5b9smj&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false
What I would say is that documentation is never in production, but the semantic layer always is. Therefore, it is kept up to date, or people's reports don't do what they want. Whereas, with documentation it goes stale without anyone realising.
Agree 100%
Thanks for linking the article from Rohan, this is stellar!
Sadly it looks like not much has happened since 2022 when I wrote this Metric Layer overview, except of course the acquisition of Transform/MetricFlow.
Linking the original article in the case anyone interested:
https://medium.com/@vfisa/an-overview-of-metric-layer-offerings-a9ddcffb446e
It might be interesting to get Rohan's opinion on other metric layer technologies mentioned.
Yes in some ways things have gone backwards but the remaining players like Cube and dbt have improved their offerings.