Exposures and Semantic Layer
Semantic Layer Architecture
Architecture Diagram
┌─────────────────────────────────────────────────────────────────────────────┐
│ DBT SEMANTIC LAYER ARCHITECTURE │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ SEMANTIC MODELS │ │
│ │ │ │
│ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────────┐ │ │
│ │ │ ENTITIES │ │ DIMENSIONS │ │ MEASURES │ │ │
│ │ │ │ │ │ │ │ │ │
│ │ │ • Primary │ │ • Time │ │ • Additive │ │ │
│ │ │ • Foreign │ │ • Categorical│ │ • Semi-additive │ │ │
│ │ │ • Unique │ │ • Numeric │ │ • Non-additive │ │ │
│ │ └──────────────┘ └──────────────┘ └──────────────────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ METRICS STORE │ │
│ │ │ │
│ │ ┌─────────────────────────────────────────────────────────────┐ │ │
│ │ │ • Derived metrics • Simple metrics • Cumulative metrics │ │ │
│ │ │ • Conversion metrics • Ratio metrics • Window metrics │ │ │
│ │ └─────────────────────────────────────────────────────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ EXPOSURES │ │
│ │ │ │
│ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────────┐ │ │
│ │ │ DASHBOARDS │ │ APPLICATIONS │ │ EXPORTS │ │ │
│ │ │ │ │ │ │ │ │ │
│ │ │ • Looker │ │ • APIs │ │ • Data sets │ │ │
│ │ │ • Tableau │ │ • ML models │ • External systems │ │ │
│ │ │ • PowerBI │ │ • Services │ │ • File exports │ │ │
│ │ └──────────────┘ └──────────────┘ └──────────────────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
Metrics Flow
Architecture Diagram
┌─────────────────────────────────────────────────────────────────────────────┐
│ METRICS DEFINITION AND USAGE │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ SEMANTIC MODEL │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ name: orders_semantic │ │
│ │ defaults: │ │
│ │ agg_time_dimension: order_date │ │
│ │ entities: │ │
│ │ - name: order_id │ │
│ │ type: primary │ │
│ │ - name: customer_id │ │
│ │ type: foreign │ │
│ │ dimensions: │ │
│ │ - name: order_date │ │
│ │ type: time │ │
│ │ type_params: │ │
│ │ time_granularity: day │ │
│ │ measures: │ │
│ │ - name: order_total │ │
│ │ type: sum │ │
│ │ expr: amount │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ METRICS DEFINITION │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ - name: total_revenue │ │
│ │ type: simple │ │
│ │ type_params: │ │
│ │ measure: order_total │ │
│ │ description: "Total revenue from all orders" │ │
│ │ │ │
│ │ - name: avg_order_value │ │
│ │ type: derived │ │
│ │ type_params: │ │
│ │ expr: total_revenue / order_count │ │
│ │ description: "Average order value" │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ QUERY INTERFACE │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ metrics: [total_revenue, order_count] │ │
│ │ group_by: [order_date__day, customer_segment] │ │
│ │ where: "order_date >= '2024-01-01'" │ │
│ │ │ │
│ │ Result: │ │
│ │ ┌────────────┬─────────┬──────────┬────────────┐ │ │
│ │ │ Date │ Segment │ Revenue │ Orders │ │ │
│ │ ├────────────┼─────────┼──────────┼────────────┤ │ │
│ │ │ 2024-01-01 │ Premium │ $150,000 │ 1,500 │ │ │
│ │ │ 2024-01-01 │ Standard│ $85,000 │ 2,100 │ │ │
│ │ └────────────┴─────────┴──────────┴────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
Exposure Dependencies
Architecture Diagram
┌─────────────────────────────────────────────────────────────────────────────┐
│ EXPOSURE DEPENDENCY GRAPH │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────┐ │
│ │ RAW DATA │ │
│ └────────┬────────┘ │
│ │ │
│ ┌───────────────────┼───────────────────┐ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ stg_orders │ │ stg_customers│ │ stg_products │ │
│ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │
│ │ │ │ │
│ └────────────────────┼────────────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────┐ │
│ │ fct_orders │ │
│ └──────┬───────┘ │
│ │ │
│ ┌─────────────────────┼─────────────────────┐ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ dim_customers│ │ dim_products │ │ dim_dates │ │
│ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │
│ │ │ │ │
│ └────────────────────┼────────────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────┐ │
│ │ Order │ │
│ │ Analytics │ │
│ │ Dashboard │ │
│ │ (Exposure) │ │
│ └──────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
Detailed Explanation
The dbt semantic layer provides a standardized way to define metrics, dimensions, and business logic that can be consumed by downstream applications.
Semantic Models
Semantic models are the foundation of the semantic layer. They define:
- Entities: Keys that identify records (primary, foreign, unique)
- Dimensions: Qualitative attributes for grouping and filtering
- Measures: Quantitative values that can be aggregated
Metrics
Metrics are defined calculations based on semantic models:
- Simple metrics: Direct aggregations of measures
- Derived metrics: Calculations based on other metrics
- Cumulative metrics: Running totals over time
- Conversion metrics: Funnel analysis and conversion rates
Exposures
Exposures define how data is consumed downstream:
- Dashboards: BI tool dashboards (Looker, Tableau, PowerBI)
- Applications: Data applications and APIs
- Exports: Data exports to external systems
Benefits of the Semantic Layer
- Single source of truth: Consistent metric definitions
- Governance: Centralized business logic management
- Reusability: Metrics defined once, used everywhere
- Lineage: Track metric dependencies and usage
- Performance: Pre-computed aggregations
Code Examples
Semantic Model Definition
# semantic/order_semantic.yml
version: 2
semantic_models:
- name: orders_semantic
description: "Semantic model for order analytics"
defaults:
agg_time_dimension: order_date
model: ref('fct_orders')
entities:
- name: order_id
type: primary
expr: order_id
- name: customer_id
type: foreign
expr: customer_id
dimensions:
- name: order_date
type: time
type_params:
time_granularity: day
- name: order_status
type: categorical
expr: status
- name: customer_segment
type: categorical
expr: segment
measures:
- name: order_total
description: "Total revenue from orders"
agg: sum
expr: amount
- name: order_count
description: "Count of orders"
agg: count
expr: order_id
- name: avg_order_value
description: "Average order value"
agg: average
expr: amount
Metrics Definition
# semantic/metrics.yml
version: 2
metrics:
- name: total_revenue
description: "Total revenue from all orders"
type: simple
type_params:
measure: order_total
filter: |
{{ Dimension('order_status') }} != 'cancelled'
- name: order_count
description: "Total number of orders"
type: simple
type_params:
measure: order_count
- name: avg_order_value
description: "Average order value"
type: derived
type_params:
expr: "total_revenue / order_count"
- name: revenue_growth
description: "Month-over-month revenue growth"
type: derived
type_params:
expr: "(current_month_revenue - prev_month_revenue) / prev_month_revenue"
- name: conversion_rate
description: "Conversion rate from views to purchases"
type: conversion
type_params:
measure: order_count
conversion_measure: view_count
window: 7 days
Exposure Definition
# exposures/order_dashboard.yml
version: 2
exposures:
- name: order_analytics_dashboard
type: dashboard
description: "Main dashboard for order analytics"
url: https://looker.example.com/dashboards/order_analytics
depends_on:
- ref('fct_orders')
- ref('dim_customers')
- ref('dim_products')
owner:
name: Analytics Team
email: analytics@company.com
meta:
refresh_frequency: hourly
data_source: Looker
- name: ml_feature_store
type: ml
description: "ML feature store for customer segmentation"
url: https://ml.example.com/features/customer_segmentation
depends_on:
- ref('customer_features')
- ref('order_features')
owner:
name: Data Science Team
email: ds@company.com
- name: data_export_daily
type: application
description: "Daily data export to external system"
url: https://api.example.com/exports/daily
depends_on:
- ref('fct_daily_metrics')
owner:
name: Data Engineering
email: data-eng@company.com
Semantic Model with Joins
# semantic/order_customer_semantic.yml
version: 2
semantic_models:
- name: order_with_customer
description: "Orders with customer dimensions"
defaults:
agg_time_dimension: order_date
model: ref('fct_orders')
joins:
- name: customers
join_type: left
sql: "ON {{ entity('order_id') }} = {{ entity('customer_id') }}"
entities:
- name: order_id
type: primary
expr: order_id
dimensions:
- name: order_date
type: time
type_params:
time_granularity: day
- name: customer_name
type: categorical
expr: "customers.customer_name"
- name: customer_segment
type: categorical
expr: "customers.segment"
measures:
- name: total_revenue
agg: sum
expr: amount
Querying the Semantic Layer
# Query definition for Looker
query:
metrics:
- total_revenue
- order_count
- avg_order_value
group_by:
- order_date__day
- customer_segment
where: |
{{ Dimension('order_date') }} >= '2024-01-01'
AND {{ Dimension('order_status') }} = 'completed'
order_by:
- order_date__day
limit: 1000
Performance Metrics
| Component | Description | Performance Impact |
|---|---|---|
| Semantic Model | Query compilation | Low |
| Metrics | Aggregation | Medium |
| Dimensions | Grouping | Low |
| Exposures | Dependency tracking | Minimal |
| Cache | Query result caching | High (positive) |
Best Practices
- Define semantic models for all analytical entities
- Use consistent naming for metrics and dimensions
- Document all exposures with clear descriptions
- Define relationships between semantic models
- Use appropriate measures for different aggregation types
- Filter at the metric level to ensure consistency
- Monitor exposure usage to understand data consumption
- Version control all semantic definitions