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Dbt Cloud Semantic

⬢ LIVELLO 2Tecniche
Alto
Impatto sullo stipendio
2 mesi
Tempo di apprendimento
Medio
Difficoltà
—
Carriere
In sintesi

dbt Cloud Semantic Layer enables defining metrics, dimensions, and relationships once, then consuming them across all BI tools. Instead of redefining 'revenue' in Tableau and Looker separately, define it once in dbt. Senior practitioners earn 15-20% premium because they ship self-service analytics platforms that scale. Learning: 4-6 weeks (combines data modeling + dbt + SQL knowledge).

Cos'è Dbt Cloud Semantic

dbt Cloud Semantic Layer is a declarative way to define business metrics, dimensions, and relationships. Once defined in dbt, metrics can be queried from any BI tool (Looker, Tableau, Metabase). Prevents duplicating metric definitions across tools and ensures consistency. Example: Define revenue = sum(amount) by date once in dbt → Looker and Tableau both query revenue metric from dbt → Both get same definition, guaranteed consistent.

🔧 STRUMENTI ED ECOSISTEMA
dbt Clouddbt-corePython (for dbt packages)SQL (dbt models)dbt metrics and dimensionsdbt semantic layerBI tools (Looker, Tableau, Metabase)Expo (dbt's querying layer)Git (version control)YAML (configuration)

📋 Prima di iniziare

Git Version Control

💰 Stipendio per regione

RegioneLivello baseMidLivello esperto
USA$80k$140k$215k
UK£50k£85k£130k
EU€55k€92k€140k
CANADAC$75kC$135kC$205k

❓ Domande frequenti

What's the semantic layer?
A layer that defines business logic (metrics, dimensions, relationships) once. BI tools query semantic layer instead of raw tables. Example: Looker and Tableau both get same 'revenue' definition. No duplication, consistency guaranteed.
How is dbt semantic layer different from traditional BI semantic layers?
Traditional (Looker, Tableau native): UI-driven, closed to other tools. dbt semantic: code-driven, open standard, works with multiple BI tools. dbt metric can be queried from Looker, Tableau, Metabase, API, CLI.
What's a metric in dbt?
A computed value (revenue, MAU, ARPU) built from dimensions and facts. Defined once in YAML. Example: `revenue = sum(amount)` grouped by `date_month`. BI tools query metrics without redefining logic.
Can I version control metrics?
Yes. Metrics are defined in Git. Changes require PR, review, merge. Full audit trail. No hidden changes in BI tool UIs.
How do I test metrics?
dbt allows assertions on metric outputs. Example: 'metric MUST be >= yesterday'. Tests run automatically. Catch metric definition bugs before BI tools use them.

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