How do you weight the value of semantic layers when all of the labs are chasing shell usage benchmarks like TerminalBench?
Aside from the enterprise stuff like consistent metrics I’m not convinced semantic layers improve agent performance. Case in point is Snowflake Analyst has been routing 90%+ of queries to traditional SQL as opposed to their own semantic SQL dialect.
A semantic layer is a concept from 2018 in the BI world. Metadata is one thing but semantic layer implies an abstraction from physical data with lossy translations.
I’m a big fan of the strict naming as an abstraction above tables and reuse as blend key approach, it also greatly simplifies aggregate resolution like you have. (Landed on the same abstraction level when building a semantic model personally).
Lots of 404s on the docs pages - might be worth an audit of links?
How do you weight the value of semantic layers when all of the labs are chasing shell usage benchmarks like TerminalBench?
Aside from the enterprise stuff like consistent metrics I’m not convinced semantic layers improve agent performance. Case in point is Snowflake Analyst has been routing 90%+ of queries to traditional SQL as opposed to their own semantic SQL dialect.
A semantic layer is a concept from 2018 in the BI world. Metadata is one thing but semantic layer implies an abstraction from physical data with lossy translations.
I’m a big fan of the strict naming as an abstraction above tables and reuse as blend key approach, it also greatly simplifies aggregate resolution like you have. (Landed on the same abstraction level when building a semantic model personally).
Lots of 404s on the docs pages - might be worth an audit of links?
What a terrible landing page. Detailed install instructions. No idea what it does.
Go up a level in the URL, or click on the logo in the upper-left on the "try" page the post links to.
https://strata.do/