Update project for Fusion compatibility

.gitignore

@@ -14,3 +14,4 @@ profiles.yml
 
 .ruff_cache
 __pycache__
+dbt_internal_packages/

README.md

@@ -4,6 +4,9 @@ This is a sandbox project for exploring the basic functionality and latest featu
 
 This README will guide you through setting up the project on dbt Cloud. Working through this example should give you a good sense of how dbt Cloud works and what's involved with setting up your own project. We'll also _optionally_ cover some intermediate topics like setting up Environments and Jobs in dbt Cloud, working with a larger dataset, and setting up pre-commit hooks if you'd like.
 
+> [!NOTE]
+> **The `main` is compatible with [dbt Fusion](https://docs.getdbt.com/docs/fusion/about-fusion).** and dbt Core v1.12 and higher. It uses the latest Semantic Layer YAML spec, with semantic models embedded in model YAML files, `type: simple` metrics replacing measures, and `type_params` promoted to top-level keys. If you're looking for the legacy project using the pre-Fusion YAML spec, check out the [`jaffle-shop-old`](../../tree/jaffle-shop-old) branch.
+
 > [!NOTE]
 > This project is geared towards folks learning dbt Cloud with a cloud warehouse. If you're brand new to dbt, we recommend starting with the [dbt Learn](https://learn.getdbt.com/) platform. It's a free, interactive way to learn dbt, and it's a great way to get started if you're new to the tool. If you just want to try dbt locally as quickly as possible without setting up a data warehouse check out [`jaffle_shop_duckdb`](https://github.com/dbt-labs/jaffle_shop_duckdb).
 

dbt_project.yml

@@ -36,3 +36,6 @@ models:
       +materialized: view
     marts:
       +materialized: table
+
+flags:
+  require_generic_test_arguments_property: true

models/marts/customers.yml

@@ -1,23 +1,38 @@
 models:
   - name: customers
     description: Customer overview data mart, offering key details for each unique customer. One row per customer.
+    semantic_model:
+      enabled: true
+    agg_time_dimension: first_ordered_at
     data_tests:
       - dbt_utils.expression_is_true:
-          expression: "lifetime_spend_pretax + lifetime_tax_paid = lifetime_spend"
+          arguments:
+            expression: "lifetime_spend_pretax + lifetime_tax_paid = lifetime_spend"
     columns:
       - name: customer_id
         description: The unique key of the orders mart.
         data_tests:
           - not_null
           - unique
+        entity:
+          type: primary
+          name: customer
       - name: customer_name
         description: Customers' full name.
+        dimension:
+          type: categorical
       - name: count_lifetime_orders
         description: Total number of orders a customer has ever placed.
       - name: first_ordered_at
         description: The timestamp when a customer placed their first order.
+        granularity: day
+        dimension:
+          type: time
       - name: last_ordered_at
         description: The timestamp of a customer's most recent order.
+        granularity: day
+        dimension:
+          type: time
       - name: lifetime_spend_pretax
         description: The sum of all the pre-tax subtotals of every order a customer has placed.
       - name: lifetime_tax_paid
@@ -28,72 +43,47 @@ models:
         description: Options are 'new' or 'returning', indicating if a customer has ordered more than once or has only placed their first order to date.
         data_tests:
           - accepted_values:
-              values: ["new", "returning"]
-
-semantic_models:
-  - name: customers
-    defaults:
-      agg_time_dimension: first_ordered_at
-    description: |
-      Customer grain mart.
-    model: ref('customers')
-    entities:
-      - name: customer
-        expr: customer_id
-        type: primary
-    dimensions:
-      - name: customer_name
-        type: categorical
-      - name: customer_type
-        type: categorical
-      - name: first_ordered_at
-        type: time
-        type_params:
-          time_granularity: day
-      - name: last_ordered_at
-        type: time
-        type_params:
-          time_granularity: day
-    measures:
+              arguments:
+                values: ["new", "returning"]
+        dimension:
+          type: categorical
+    metrics:
       - name: customers
         description: Count of unique customers
+        label: Customers
+        type: simple
         agg: count_distinct
         expr: customer_id
       - name: count_lifetime_orders
         description: Total count of orders per customer.
+        label: Count Lifetime Orders
+        type: simple
         agg: sum
       - name: lifetime_spend_pretax
-        description: Customer lifetime spend before taxes.
+        description: Customer's lifetime spend before tax
+        label: LTV Pre-tax
+        type: simple
         agg: sum
       - name: lifetime_spend
-        agg: sum
         description: Gross customer lifetime spend inclusive of taxes.
+        label: Lifetime Spend
+        type: simple
+        agg: sum
 
 metrics:
-  - name: lifetime_spend_pretax
-    description: Customer's lifetime spend before tax
-    label: LTV Pre-tax
-    type: simple
-    type_params:
-      measure: lifetime_spend_pretax
-  - name: count_lifetime_orders
-    description: Count of lifetime orders
-    label: Count Lifetime Orders
-    type: simple
-    type_params:
-      measure: count_lifetime_orders
   - name: average_order_value
     description: LTV pre-tax / number of orders
     label: Average Order Value
     type: derived
-    type_params:
-      metrics:
-        - count_lifetime_orders
-        - lifetime_spend_pretax
-      expr: lifetime_spend_pretax / count_lifetime_orders
+    expr: lifetime_spend_pretax / count_lifetime_orders
+    input_metrics:
+      - name: count_lifetime_orders
+      - name: lifetime_spend_pretax
 
 saved_queries:
   - name: customer_order_metrics
+    description: Key customer order metrics grouped by customer.
+    label: Customer Order Metrics
     query_params:
       metrics:
         - count_lifetime_orders

models/marts/locations.yml

@@ -1,24 +1,26 @@
-semantic_models:
+models:
   - name: locations
     description: |
       Location dimension table. The grain of the table is one row per location.
-    model: ref('locations')
-    defaults:
-      agg_time_dimension: opened_date
-    entities:
-      - name: location
-        type: primary
-        expr: location_id
-    dimensions:
+    semantic_model:
+      enabled: true
+    agg_time_dimension: opened_date
+    columns:
+      - name: location_id
+        entity:
+          type: primary
+          name: location
       - name: location_name
-        type: categorical
+        dimension:
+          type: categorical
       - name: opened_date
-        expr: opened_date
-        type: time
-        type_params:
-          time_granularity: day
-    measures:
+        granularity: day
+        dimension:
+          type: time
+    metrics:
       - name: average_tax_rate
         description: Average tax rate.
-        expr: tax_rate
+        label: Average Tax Rate
+        type: simple
         agg: average
+        expr: tax_rate

models/marts/metricflow_time_spine.sql

@@ -1,19 +1,24 @@
--- metricflow_time_spine.sql
-with
-
-days as (
-
-    --for BQ adapters use "DATE('01/01/2000','mm/dd/yyyy')"
-    {{ dbt_date.get_base_dates(n_dateparts=365*10, datepart="day") }}
+{{
+    config(
+        materialized = 'table',
+    )
+}}
+
+with days as (
+
+    {{
+        dbt.date_spine(
+            'day',
+            "to_date('01/01/2000','mm/dd/yyyy')",
+            "to_date('01/01/2030','mm/dd/yyyy')"
+        )
+    }}
 
 ),
 
-cast_to_date as (
-
+final as (
     select cast(date_day as date) as date_day
-
     from days
-
 )
 
-select * from cast_to_date
+select * from final

models/marts/metricflow_time_spine.yml

@@ -0,0 +1,8 @@
+models:
+  - name: metricflow_time_spine
+    description: Time spine model used by MetricFlow for temporal joins and aggregations.
+    time_spine:
+      standard_granularity_column: date_day
+    columns:
+      - name: date_day
+        granularity: day