clarifies mf timespine & creates mini guide

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

@@ -10,22 +10,41 @@ tags: [Metrics, Semantic Layer]
 
 It's common in analytics engineering to have a date dimension or "time spine" table as a base table for different types of time-based joins and aggregations. The structure of this table is typically a base column of daily or hourly dates, with additional columns for other time grains, like fiscal quarters, defined based on the base column. You can join other tables to the time spine on the base column to calculate metrics like revenue at a point in time, or to aggregate to a specific time grain.
 
-MetricFlow requires you to define at least one dbt model which provides a time-spine, and then specify (in YAML) the columns to be used for time-based joins. MetricFlow will join against the time-spine model for the following types of metrics and dimensions:
+To use MetricFlow with time-based metrics and dimensions, you _must_ provide a time spine table. This table serves as the foundation for time-based joins and aggregations. You can either:
+
+- Create a time spine SQL table from scratch (check out the [example time spine tables](#example-time-spine-tables) section for examples), or
+- Use an existing table in your project, like a `dim_date` table
+
+And once you have a time spine table, you need to configure it in YAML to tell MetricFlow how to use it.
+
+
+:::tip
+Check out our mini guide on [how to create a time spine table](/guides/mf-time-spine) to get started!
+:::
+
+## Prerequisites
+MetricFlow requires you to define at least one dbt model which provides a time-spine, and then specify (in YAML) the columns to be used for time-based joins. This means you need to:
+
+- Define at least one [time spine table](#example-time-spine-tables) at the whichever granularity needed for your metrics (either daily or hourly). You can optionally define additional tables for coarser grains (like monthly or yearly).
+- [Configure each time spine table in a YAML file](#configuring-time-spine-in-yaml) to define how MetricFlow recognizes and uses its columns.
+
+ MetricFlow will then join against the time spine model for the following types of metrics and dimensions:
 
 - [Cumulative metrics](/docs/build/cumulative)
 - [Metric offsets](/docs/build/derived#derived-metric-offset)
 - [Conversion metrics](/docs/build/conversion)
 - [Slowly Changing Dimensions](/docs/build/dimensions#scd-type-ii)
 - [Metrics](/docs/build/metrics-overview) with the `join_to_timespine` configuration set to true
 
-To see the generated SQL for the metric and dimension types that use time spine joins, refer to the respective documentation or add the `compile=True` flag when querying the Semantic Layer to return the compiled SQL.
+To see the generated SQL for the metric and dimension types that use time spine joins, refer to the respective documentation or add the `compile=true` flag when querying the Semantic Layer to return the compiled SQL.
 
 ## Configuring time spine in YAML
 
  Time spine models are normal dbt models with extra configurations that tell dbt and MetricFlow how to use specific columns by defining their properties. Add the [`models` key](/reference/model-properties) for the time spine in your `models/` directory. If your project already includes a calendar table or date dimension, you can configure that table as a time spine. Otherwise, review the [example time-spine tables](#example-time-spine-tables) to create one. If the relevant model file doesn't exist, create it and add the configuration mentioned in the [next section](#creating-a-time-spine-table).
 
  Some things to note when configuring time spine models:
 
+- Make sure you already have a time spine SQL table defined in your project.
 - Add the configurations under the `time_spine` key for that [model's properties](/reference/model-properties), just as you would add a description or tests.
 - You only need to configure time-spine models that the Semantic Layer should recognize.
 - At a minimum, define a time-spine table for a daily grain.
@@ -145,6 +164,13 @@ Note that if you're migrating from a `metricflow_time_spine.sql` file:
 
 ## Example time spine tables
 
+The following examples show how to create time spine tables at different granularities:
+
+- [Daily](#daily)
+- [Daily (BigQuery)](#daily-bigquery)
+- [Hourly](#hourly)
+
+
 ### Daily
 
 <File name="metricflow_time_spine.sql">

website/docs/guides/mf-time-spine.md

@@ -0,0 +1,247 @@
+---
+title: "Quickstart with MetricFlow time spine"
+id: "mf-time-spine"
+level: 'Intermediate'
+icon: 'guides'
+tags: ['Quickstart']
+hide_table_of_contents: true
+recently_updated: true
+---
+
+<div style={{maxWidth: '900px'}}>
+
+## Introduction
+
+This guide explains how to configure a time spine using the [dbt Semantic Layer Jaffle shop example project](https://github.com/dbt-labs/jaffle-sl-template) as a reference. 
+
+### What is a time spine table?
+
+A [time spine](/docs/build/metricflow-time-spine) is essential for time-based joins and aggregations in MetricFlow, the engine that powers the dbt Semantic Layer.
+
+To use MetricFlow with time-based metrics and dimensions, you must provide a time spine table. This table serves as the foundation for time-based joins and aggregations. You can either:
+
+- Create a time spine SQL table from scratch, or
+- Use an existing table in your project, like a `dim_date` table
+
+And once you have a time spine table, you need to configure it in YAML to tell MetricFlow how to use it. This guide will show you how to do both!
+
+### Prerequisites
+Before you start, make sure you have:
+
+- A dbt project set up. If you don't have one, follow the [Semantic Layer quickstart guide](/guides/sl-snowflake-qs?step=1) or the [dbt Cloud quickstart guides)(/guides?tags=Quickstart) guide to help you get started.
+
+## Add a time spine SQL model
+
+Let's get started by assuming you're creating a time spine table from scratch. If you have a dbt project set up already and have your own time spine table (like a `dim_date` type model), you can skip this step and go to [Use an existing dim_date table](https://docs.getdbt.com//guides/mf-time-spine#using-an-existing-dim-date-table).
+
+The time spine table is a dbt model that generates a series of dates (or timestamps) at a specific granularity. In this example, let's create a daily time spine table &mdash; `time_spine_daily.sql`. 
+
+1. Navigate to the `models/marts` directory in your dbt project.
+2. Add a new SQL file named `time_spine_daily.sql` with the following content:
+
+<File name='models/marts/time_spine_daily.sql'>
+
+```sql
+{{
+    config(
+        materialized = 'table',
+    )
+}}
+
+with
+
+base_dates as (
+    {{
+        dbt.date_spine(
+            'day',
+            "DATE('2000-01-01')",
+            "DATE('2030-01-01')"
+        )
+    }}
+),
+
+final as (
+    select
+        cast(date_day as date) as date_day
+    from base_dates
+)
+
+select *
+from final
+where date_day > dateadd(year, -5, current_date())  -- Keep recent dates only
+  and date_day < dateadd(day, 30, current_date());
+```
+</File>
+
+This generates a table of daily dates ranging from 5 years in the past to 30 days into the future.
+
+
+## Add YAML configuration for the time spine
+
+Now that you've created the SQL file, configure it in YAML so MetricFlow can recognize and use it.
+
+1. Navigate to the `models/marts` directory.
+2. Add a new YAML file named `_models.yml` with the following content:
+
+<File name='models/marts/_models.yml'>
+
+```yaml
+models:
+  - name: time_spine_daily
+    description: A time spine with one row per day, ranging from 5 years in the past to 30 days into the future.
+    time_spine:
+      standard_granularity_column: date_day  # The base column used for time joins
+    columns:
+      - name: date_day
+        description: The base date column for daily granularity
+        granularity: day
+```
+</File>
+
+This time spine YAML file:
+- Defines `date_day` as the base column for daily granularity.
+- Configures `time_spine` properties so MetricFlow can use the table
+
+### Using an existing dim_date table
+
+This optional approach reuses an existing table, saving you the effort of creating a new one. However if you created a time spine table from scratch, you can skip this section. 
+
+If your project already includes a `dim_date` or similar table, you can configure it as a time spine:
+
+1. Locate the existing table (`dim_date`).
+2. Update `_models.yml` file to configure it as a time spine:
+
+<File name='_models.yml'>
+
+```yaml
+models:
+  - name: dim_date
+    description: An existing date dimension table used as a time spine.
+    time_spine:
+      standard_granularity_column: date_day
+    columns:
+      - name: date_day
+        granularity: day
+```
+</File>
+
+This time spine YAML file configures the `time_spine` property so MetricFlow can use the table.
+
+## Run and test the time spine
+
+For the time spine table you created, let's run it and validate the output.
+
+1. Run the time spine model to create the table and validate the output:
+   ```bash
+   dbt run --select time_spine_daily
+   ```
+
+2. Check that the table:
+   - Contains one row per day.
+   - Covers the date range you want (5 years back to 30 days forward)
+
+3. (Optional) If you have [metrics](/docs/build/metrics-overview) already defined in your project, you can query the table/metrics using [Semantic Layer commands](/docs/build/metricflow-commands) to validate the time spine. 
+
+   Let's say you have a `revenue` metric defined. You can query the table/metrics using the following command:
+
+    ```bash
+    dbt sl query --metrics revenue --group-by metric_time
+    ```
+
+    This will output results similar to the following in the dbt Cloud IDE:
+    <Lightbox src="/img/quickstarts/dbt-cloud/validate-mf-timespine-output.jpg" title="Validate the time spine output in dbt Cloud IDE" />
+
+4. Double check that the results are correct and returning the expected data.
+
+## Add additional granularities (optional)
+
+To support multiple granularities (like hourly, monthly), create additional time spine tables and configure them in YAML.
+
+1. Add a new SQL file named `time_spine_hourly.sql` with the following content:
+<File name='models/marts/time_spine_hourly.sql'>
+
+```sql
+{{
+    config(
+        materialized = 'table',
+    )
+}}
+
+with hours as (
+
+    {{
+        dbt.date_spine(
+            'hour',
+            "to_date('01/01/2000','mm/dd/yyyy')",
+            "to_date('01/01/2025','mm/dd/yyyy')"
+        )
+    }}
+
+),
+
+final as (
+    select cast(date_hour as timestamp) as date_hour
+    from hours
+)
+
+select * from final
+-- filter the time spine to a specific range
+where date_hour > dateadd(year, -4, current_timestamp()) 
+  and date_hour < dateadd(day, 30, current_timestamp())
+
+```
+</File>
+
+2. Then update `_models.yml` file and add the hourly time spine (below the daily time spine config):
+
+<File name='_models.yml'>
+
+```yaml
+models:
+  - name: time_spine_daily
+    ... rest of the daily time spine config ...
+
+- name: time_spine_hourly
+    description: A time spine with one row per hour, ranging from four years ago to today.
+    time_spine:
+      standard_granularity_column: date_hour
+    columns:
+      - name: date_hour
+        granularity: hour
+```
+</File>
+
+3. Run the model to create the table:
+   ```bash
+   dbt run --select time_spine_hourly
+   ```
+
+4. Validate the output by querying the generated table:
+   ```bash
+   dbt sl query --metrics revenue --group-by metric_time__hour
+   ```
+
+:::tip
+Try creating a monthly time spine! Duplicate your daily time spine model, adjust it to generate one row per month, and update the YAML file to include `granularity: month`. Give it a try!
+:::
+
+## What's next
+
+<ConfettiTrigger>
+
+Congratulations 🎉! You've set up a time spine and are ready to bring the benefits of MetricFlow and the dbt Semantic Layer to your organization. You've learned:
+
+- How to create a time spine table or use an existing table.
+- How to configure a time spine in YAML.
+- How to add additional granularities to your time spine.
+
+Here are some additional resources to help you continue your journey:
+
+- [MetricFlow time spine](/docs/build/metricflow-time-spine)
+- [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl)
+- [Build metrics](/docs/build/metrics-overview)
+- [Quickstart with dbt Semantic Layer](/guides/sl-snowflake-qs?step=1)
+
+</ConfettiTrigger>
+
+</div>