Merge branch 'current' into Tonayya-patch-1

website/blog/2021-09-15-september-21-product-email.md

@@ -4,7 +4,6 @@ description: "dbt v1.0 is coming up! Don't forget to update your projects to the
 slug: dbt-product-update-2021-september
 authors: [lauren_craigie] 
 
-tags: [dbt updates]
 hide_table_of_contents: false
 
 date: 2021-09-15

website/blog/2021-10-15-october-21-product-update-email.md

@@ -4,7 +4,6 @@ description: "Stay up-to-date with the latest features in dbt. Read about our Oc
 slug: dbt-product-update-2021-october
 authors: [lauren_craigie]
 
-tags: [dbt updates]
 hide_table_of_contents: false
 
 date: 2021-10-15

website/blog/2021-11-15-november-21-product-email.md

@@ -4,7 +4,6 @@ description: "Stay up-to-date with the latest features in dbt. Read about our No
 slug: dbt-product-update-2021-november
 authors: [lauren_craigie] 
 
-tags: [dbt updates]
 hide_table_of_contents: false
 
 date: 2021-11-15

website/blog/2022-08-31-august-product-update.md

@@ -4,7 +4,6 @@ description: "Coalesce is less than 2 months away!"
 slug: dbt-product-update-2022-august
 authors: [lauren_craigie] 
 
-tags: [dbt updates]
 hide_table_of_contents: false
 
 date: 2022-08-31

website/blog/2022-10-19-polyglot-dbt-python-dataframes-and-sql.md

@@ -4,8 +4,7 @@ description: "Going polyglot is a major next step in the journey of dbt Core. It
 slug: polyglot-dbt-python-dataframes-sql
 
 authors: [doug_beatty]
-
-tags: [dbt product updates]
+tags: [dbt tutorials]
 hide_table_of_contents: false
 
 date: 2022-10-18

website/blog/2023-04-26-deprecating-dbt-metrics.md

@@ -5,7 +5,6 @@ slug: deprecating-dbt-metrics
 
 authors: [callum_mccann]
 
-tags: [dbt product updates]
 hide_table_of_contents: false
 
 date: 2023-04-26

website/blog/2023-05-01-evolving-data-engineer-craft.md

@@ -5,7 +5,6 @@ slug: evolving-data-engineer-craft
 
 authors: [sung_chung, kira_furuichi]
 
-tags: [dbt product updates]
 hide_table_of_contents: false
 
 date: 2023-05-01

website/blog/categories.yml

@@ -15,10 +15,6 @@
   display_title: dbt tutorials
   description: Best practices in the usage of our favorite data transformation tool.
   is_featured: true    
-- name: dbt updates
-  display_title: dbt product updates
-  description: An archive of monthly product updates from the dbt Labs team.
-  is_featured: true
 - name: SQL magic
   display_title: SQL magic
   description: Stories of dbt developers making SQL sing across warehouses.

website/docs/docs/build/cumulative-metrics.md

@@ -6,34 +6,45 @@ sidebar_label: Cumulative
 tags: [Metrics, Semantic Layer]
 ---
 
-Cumulative metrics aggregate a measure over a given accumulation window. If no window is specified, the window is considered infinite and accumulates values over all time. 
+Cumulative metrics aggregate a measure over a given accumulation window. If no window is specified, the window is considered infinite and accumulates values over all time. You will need to create the [time spine model](/docs/build/metricflow-time-spine) before you add cumulative metrics.
 
-This metric is common for calculating things like weekly active users, or month to date revenue. The following displays the full metrics spec, along with an example:
+This metric is common for calculating things like weekly active users, or month to date revenue.  The parameters, description, and type for cumulative metrics are: 
 
+| Parameter | Description | Type |
+| --------- | ----------- | ---- |
+| `name` | The name of the metric. | Required |
+| `description` | The description of the metric. | Optional |
+| `type` | The type of the metric (cumulative, derived, ration, or simple). | Required |
+| `label` | The value that will be displayed in downstream tools. | Required |
+| `type_params` | The type parameters of the metric. | Required |
+| `measure` | The measure you are referencing. | Required |
+| `window` | The accumulation window, such as 1 month, 7 days, 1 year. This can't be used with `window`. | Optional  |
+| `grain_to_date` | Sets the accumulation grain, such as month will accumulate data for one month. Then restart at the beginning of the next. This can't be used with window. | Optional |
+
+The following displays the complete specification for cumulative metrics, along with an example:
 
 ```yaml
 metrics:
-  - name: the metric name # Required
-    description: the metric description # Optional
+  - name: The metric name # Required
+    description: The metric description # Optional
     type: simple # Required
     label: The value that will be displayed in downstream tools # Required
     type_params: # Required
-      measure: the measure you are referencing # Required
-      window: the accumulation window i.e 1 month, 7 days, 1 year. # Optional. Cannot be used with grain_to_date
-      grain_to_date: sets the accumulation grain, such as month will accumulate data for one month, then restart at the beginning of the next. 
-      # Optional. Can not be used with window. 
-
+      measure: The measure you are referencing # Required
+      window: The accumulation window, such as 1 month, 7 days, 1 year. # Optional. Can not be used with window. 
+      grain_to_date: Sets the accumulation grain, such as month will accumulate data for one month, then restart at the beginning of the next.  # Optional. Cannot be used with grain_to_date
 
 ```
 
-:::info MetricFlow time spine required
+## Cumulative metrics example
+
+
+:::tip MetricFlow time spine required
 
 You will need to create the [time spine model](/docs/build/metricflow-time-spine) before you add cumulative metrics.
 
 :::
 
-## Cumulative metrics example
-
 Cumulative metrics measure data over a given window and consider the window infinite when no window parameter is passed, accumulating the data over all time.
 
 ```yaml
@@ -75,9 +86,10 @@ Suppose the underlying measure `customers` which is configured to count the uniq
 
 ```yaml
 measures:
-      - name: customers
-        expr: customer_id
-        agg: count_distinct
+  - name: customers
+    expr: customer_id
+    agg: count_distinct
+
 ```
 
 We can write a cumulative metric `weekly_customers` as such: 
@@ -118,31 +130,29 @@ Suppose you (a subscription-based company for the sake of this example) have an
 Using cumulative metrics without specifying a window, you can calculate running totals for metrics like the count of active subscriptions and revenue at any point in time. The following configuration YAML displays creating such cumulative metrics to obtain current revenue or total number of active subscriptions as a cumulative sum:
 
 ```yaml
-measures: 
-  - name: revenue 
-    description: Total revenue 
-    agg: sum 
-    expr: revenue 
-  - name: subscription_count 
-    description: Count of active subscriptions 
-    agg: sum 
+measures:
+  - name: revenue
+    description: Total revenue
+    agg: sum
+    expr: revenue
+  - name: subscription_count
+    description: Count of active subscriptions
+    agg: sum
     expr: event_type
+metrics:
+  - name: current_revenue
+    description: Current revenue
+    label: Current Revenue
+    type: cumulative
+    type_params:
+      measure: revenue
+  - name: active_subscriptions
+    description: Count of active subscriptions
+    label: Active Subscriptions
+    type: cumulative
+    type_params:
+      measure: subscription_count
 
-metrics: 
-- name: current_revenue
-  description: Current revenue 
-  label: Current Revenue
-  type: cumulative 
-  type_params: 
-    measures: 
-      - revenue
-- name: active_subscriptions 
-  description: Count of active subscriptions 
-  label: Active Subscriptions
-  type: cumulative 
-  type_params: 
-    measures: 
-      - subscription_count
 ```
 
 </TabItem>
@@ -165,14 +175,14 @@ We can compare the difference between a 1-month window and a monthly grain to da
 
 ```yaml
 metrics:
- - name: cumulative_order_total_l1m #For this metric, we use a window of 1 month 
-    label: Cumulative Order total (L1M)   
+  - name: cumulative_order_total_l1m  #For this metric, we use a window of 1 month 
+    label: Cumulative Order total (L1M)
     description: Trailing 1 month cumulative order amount
     type: cumulative
     type_params:
       measure: order_total
       window: 1 month
-  - name: cumulative_order_total_mtd #For this metric, we use a monthly grain to date 
+  - name: cumulative_order_total_mtd   #For this metric, we use a monthly grain to date 
     label: Cumulative Order total (MTD)
     description: The month to date value of all orders
     type: cumulative

website/docs/docs/build/derived-metrics.md

@@ -8,7 +8,22 @@ tags: [Metrics, Semantic Layer]
 
 In MetricFlow, derived metrics are metrics created by defining an expression using other metrics. They allow performing calculations on top of existing metrics. This proves useful for combining metrics and applying arithmetic functions to aggregated columns, such as, you can define a profit metric. 
 
-The following example presents the complete specification for derived metrics, along with an example.
+ The parameters, description, and type for derived metrics are: 
+
+| Parameter | Description | Type |
+| --------- | ----------- | ---- |
+| `name` | The name of the metric. | Required |
+| `description` | The description of the metric. | Optional |
+| `type` | The type of the metric (cumulative, derived, ration, or simple). | Required |
+| `label` | The value that will be displayed in downstream tools. | Required |
+| `type_params` | The type parameters of the metric. | Required |
+| `expr` | The derived expression. | Required |
+| `metrics` |  The list of metrics used in the derived metrics. | Required  |
+| `alias` | Optional alias for the metric that you can use in the expr. | Optional |
+| `filter` | Optional filter to apply to the metric. | Optional |
+| `offset_window` | Set the period for the offset window, such as 1 month. This will return the value of the metric one month from the metric time. | Required |
+
+The following displays the complete specification for derived metrics, along with an example.
 
 ```yaml
 metrics:
@@ -70,7 +85,7 @@ metrics:
 
 ## Derived metric offset
 
-You may want to use an offset value of a metric in the definition of a derived metric. For example, you can model the retention rate by using a derived metric with an offset, which involves calculating (active customers at the end of the month/active customers at the beginning of the month) - 1.
+You may want to use an offset value of a metric in the definition of a derived metric. For example, you can model the retention rate by using a derived metric with an offset, which involves calculating (active customers at the end of the month/active customers at the beginning of the month).
 
 ```yaml
 metrics: