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Andrew Taft's avatar

While not “solving the metadata problem,” I think that a few basic standards can enable BI and other tools to leverage dbt as a full semantic layer. I put together a dbt project to show how this would work: https://github.com/flexanalytics/dbt-business-intelligence

In short, it leverages dbt as a semantic layer to define metrics, dimensions, aggregations, calculations, data relationships, business-friendly names and descriptions, synonyms, formatting and more. Then, BI tools can just plug in without needing to create a metadata/semantic model. “Semantic-free BI”:

https://towardsdatascience.com/semantic-free-is-the-future-of-business-intelligence-27aae1d11563

One big problem is that standards require buy-in from big name vendors to take off, but big name vendors don’t necessarily want to buy in because then you can more easily switch to another vendor. Another problem is that these standards are not “baked in” to dbt, and perhaps they shouldn’t be, but this forces more non-standard usage of dbt’s ‘meta’ tag. Also, the metadata needs to be more “active” with the ability for many roles to update catalogues in a friendly UI (not just YAML).

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Mark T's avatar

> Maybe the pattern here looks something like...

Sounds a lot like the "emergent layer" pattern! Where something scarce suddenly becomes abundant, solving one set of problems and moving us on to a new set of constraints. https://medium.com/swlh/emergent-layers-chapter-1-scarcity-abstraction-abundance-5705666e4f15

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