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docs: Add details on Designing for metrics #7493

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Dec 21, 2023
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add details to designing for metrics
tonykau1 Dec 5, 2023
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Update designing-metrics.mdx
tonykau1 Dec 20, 2023
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56 changes: 54 additions & 2 deletions docs/pages/guides/designing-metrics.mdx
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Expand Up @@ -85,7 +85,7 @@ Views are exposed as tables in Cube SQL API, dimensions can be queried as is

## Metrics-first
In metrics-first approach, views are built around measures, or metrics, in your data model.
Views are built as denormalzied tables, containing one measure and all the relevant dimensions from different cubes.
Views are built as denormalzied tables, containing one measure and all the relevant dimensions from different cubes. These include all the dimensions you would group or filter by, and up to one time dimension.
Views are usually named after that single measure.

```yaml
Expand Down Expand Up @@ -115,4 +115,56 @@ SELECT
MEASURE(average_order_value)
FROM average_order_value
GROUP BY 1
```
```

NOTE: If a metric is interesting across more than one time dimension, create multiple views (metrics), one with each time dimension, and name the metric distinctly for each.
This approach improves compatibility with BI tooling and adds clarity for the consumer.

```yaml
views:
- name: order_count_by_order_date

cubes:
- join_path: orders
includes:
- order_count

- status
- created_at

- name: order_count_by_ship_date

cubes:
- join_path: orders
includes:
- order_count

- status
- shipped_at
```

## Integrations with BI Tools
Some metrics-based BI tools will specify requirements for the views or be able to accept additional metadata to enrich the experience.
Below is an example of using the `meta` property to do this.

```yaml
views:
- name: order_count_by_order_date
description: For finance team to track orders on accrual/earned basis.
meta:
type: metric
owner: alice@acme.com

cubes:
- join_path: orders
includes:
# MEASURE
- order_count

# TIME
- created_at

# DIMENSIONS
- status
- city
```