Merge branch 'current' into nfiann-bigquery-cloud-config

website/docs/docs/build/measures.md

@@ -102,7 +102,7 @@ semantic_models:
     description: A record of every transaction that takes place. Carts are considered  multiple transactions for each SKU.
     model: ref('schema.transactions')
     defaults:
-      agg_time_dimension: metric_time
+      agg_time_dimension: transaction_date
 
 # --- entities ---
     entities:
@@ -167,7 +167,7 @@ semantic_models:
         
 # --- dimensions ---
     dimensions:
-      - name: metric_time
+      - name: transaction_date
         type: time
         expr: date_trunc('day', ts) # expr refers to underlying column ts
         type_params:
@@ -204,15 +204,15 @@ semantic_models:
     description: A subscription table with one row per date for each active user and their subscription plans. 
     model: ref('your_schema.subscription_table')
     defaults:
-      agg_time_dimension: metric_time 
+      agg_time_dimension: subscription_date
 
     entities:
       - name: user_id
         type: foreign
         primary_entity: subscription_table
 
     dimensions:
-      - name: metric_time
+      - name: subscription_date
         type: time
         expr: date_transaction
         type_params:

website/docs/docs/build/metricflow-time-spine.md

@@ -463,9 +463,22 @@ For example, if you use a custom calendar in your organization, such as a fiscal
 
 - This is useful for calculating metrics based on a custom calendar, such as fiscal quarters or weeks. 
 - Use the `custom_granularities` key to define a non-standard time period for querying data, such as a `retail_month` or `fiscal_week`, instead of standard options like `day`, `month`, or `year`.
-- Ensure the the `standard_granularity_column` is a date time type.
 - This feature provides more control over how time-based metrics are calculated.
 
+<Expandable alt_header="Data types and time zone considerations">
+
+When working with custom calendars in MetricFlow, it's important to ensure:
+
+- Consistent data types &mdash; Both your dimension column and the time spine column should use the same data type to allow accurate comparisons. Functions like `DATE_TRUNC` don't change the data type of the input in some databases (like Snowflake). Using different data types can lead to mismatches and inaccurate results.
+
+  We recommend using `DATETIME` or `TIMESTAMP` data types for your time dimensions and time spine, as they support all granularities. The `DATE` data type may not support smaller granularities like hours or minutes.
+
+- Time zones &mdash; MetricFlow currently doesn't perform any timezone manipulation. When working with timezone-aware data, inconsistent time zones may lead to unexpected results during aggregations and comparisons.
+
+For example, if your time spine column is `TIMESTAMP` type and your dimension column is `DATE` type, comparisons between these columns might not work as intended. To fix this, convert your `DATE` column to `TIMESTAMP`, or make sure both columns are the same data type.
+
+</Expandable>
+
 ### Add custom granularities
 
 To add custom granularities, the Semantic Layer supports custom calendar configurations that allow users to query data using non-standard time periods like `fiscal_year` or `retail_month`. You can define these custom granularities (all lowercased) by modifying your model's YAML configuration like this:

website/docs/docs/cloud-integrations/configure-auto-exposures.md

@@ -25,7 +25,6 @@ To access the features, you should meet the following:
 3. You have set up a [production](/docs/deploy/deploy-environments#set-as-production-environment) deployment environment for each project you want to explore, with at least one successful job run. 
 4. You have [admin permissions](/docs/cloud/manage-access/enterprise-permissions) in dbt Cloud to edit project settings or production environment settings.
 5. Use Tableau as your BI tool and enable metadata permissions or work with an admin to do so. Compatible with Tableau Cloud or Tableau Server with the Metadata API enabled. 
-6. Run a production job _after_ saving the Tableau collections.
 
 ## Set up in Tableau
 
@@ -63,7 +62,6 @@ To set up [personal access tokens (PATs)](/docs/dbt-cloud-apis/user-tokens#using
    dbt Cloud automatically imports and syncs any workbook within the selected collections. New additions to the collections will be added to the lineage in dbt Cloud during the next automatic sync (usually once per day).
    <Lightbox src="/img/docs/cloud-integrations/auto-exposures/cloud-select-collections.jpg" title="Select the collections you want to include for auto exposures."/>
 5. Click **Save**. 
-6. Run a production job _after_ saving the Tableau collections.
 
 dbt Cloud imports everything in the collection(s) and you can continue to view them in Explorer. For more information on how to view and use auto-exposures, refer to [View auto-exposures from dbt Explorer](/docs/collaborate/auto-exposures) page.
 

website/docs/docs/collaborate/data-tile.md

@@ -92,7 +92,7 @@ Follow these steps to embed the data health tile in PowerBI:
 
     ```html
         Website =
-        "<iframe src='https://1234.metadata.us1.dbt.com/exposure-tile?uniqueId=exposure.jaffle_shop.OrderQualityDashboard&environmentType=staging&environmentId=123456789&token=YOUR_METADATA_TOKEN' title='Exposure status tile' height='400'></iframe>"
+        "<iframe src='https://1234.metadata.ACCESS_URL/exposure-tile?uniqueId=exposure.EXPOSURE_NAME&environmentType=staging&environmentId=123456789&token=YOUR_METADATA_TOKEN' title='Exposure status tile' height='400'></iframe>"
     ```
 
     <Lightbox src="/img/docs/collaborate/dbt-explorer/power-bi-measure-tools.png" width="90%" title="In the 'Measure tools' tab, replace your values with the iFrame code."/>
@@ -120,12 +120,34 @@ Follow these steps to embed the data health tile in Tableau:
 3. Insert a **Web Page** object.
 4. Insert the URL and click **Ok**.
 
-    `https://metadata.cloud.getdbt.com/exposure-tile?uniqueId=exposure.snowflake_tpcds_sales_spoke.customer360_test&environmentType=production&environmentId=220370&token=<YOUR_METADATA_TOKEN>`
-
+    ```html
+    https://metadata.ACCESS_URL/exposure-tile?uniqueId=exposure.EXPOSURE_NAME&environmentType=production&environmentId=220370&token=<YOUR_METADATA_TOKEN>
+    ```
+
     *Note, replace the placeholders with your actual values.*
 5. You should now see the data health tile embedded in your Tableau dashboard.
 
 </TabItem>
+
+<TabItem value="sigma" label="Sigma example">
+
+Follow these steps to embed the data health tile in Sigma:
+
+<Lightbox src="/img/docs/collaborate/dbt-explorer/sigma-example.jpg" width="90%" title="Embed data health tile in Sigma"/>
+
+1. Create a dashboard in Sigma and connect to your database to pull in the data.
+2. Ensure you've copied the URL or iFrame snippet available in dbt Explorer's **Data health** section, under the **Embed data health into your dashboard** toggle.
+3. Add a new embedded UI element in your Sigma Workbook in the following format:
+
+    ```html
+    https://metadata.ACCESS_URL/exposure-tile?uniqueId=exposure.EXPOSURE_NAME&environmentType=production&environmentId=ENV_ID_NUMBER&token=<YOUR_METADATA_TOKEN>
+    ```
+
+    *Note, replace the placeholders with your actual values.*
+4. You should now see the data health tile embedded in your Sigma dashboard.
+
+</TabItem>
+
 </Tabs>
 
 ## Job-based data health <Lifecycle status="Legacy"/>