Materializations in dbt

Materialization defines how your models are built in the data warehouse. Choosing the right materialization strategy is crucial for balancing performance, freshness, and resource usage.

Types of Materializations

View

{{ config(materialized='view') }}

Best For

• Staging models
• Simple transformations
• When data freshness is critical
• Low-volume queries

Pros

• Always returns fresh data
• No storage cost
• Simple to maintain

Cons

• Can be slower for complex queries
• Resource-intensive for frequently accessed data

Table

{{ config(materialized='table') }}

Best For

• Complex transformations
• Frequently accessed data
• Heavy aggregations
• Canonical models

Pros

• Better query performance
• Reduces computation load
• Good for complex joins

Cons

• Uses storage space
• Data can become stale
• Full refresh each run

Incremental

{{ config(
    materialized='incremental',
    unique_key='order_id'
) }}

Best For

• Large fact tables
• Append-only data
• Historical data that grows over time
• Models with frequent small updates

Pros

• Efficient processing of new data
• Reduced computation time
• Maintains historical data

Cons

• More complex to set up
• Requires unique key
• Need to handle schema changes

Ephemeral

{{ config(materialized='ephemeral') }}

Best For

• Intermediate transformations
• Simple helper models
• Code that's reused across models
• Testing and development

Pros

• No storage usage
• Compiled into dependent models
• Good for DRY principles

Cons

• Can't be queried directly
• Can make debugging harder
• Not suitable for complex transformations

Default Configuration

You can set default materializations in your dbt_project.yml:

models:
  your_project:
    staging:
      +materialized: view
    intermediate:
      +materialized: ephemeral
    canonical:
      +materialized: table

Key Considerations

When choosing a materialization, consider:

1. Data volume
2. Query frequency
3. Freshness requirements
4. Available resources
5. Downstream dependencies