Incremental Materialization in dbt: What It Is and How It Works

In dbt, incremental materialization is a way to build models that update only new or changed data instead of rebuilding the entire dataset every time. This makes data processing faster and more efficient by appending or updating rows incrementally.

How It Works

Incremental materialization in dbt works like updating a growing notebook instead of rewriting the whole book each time. Imagine you have a large table of sales data that grows daily. Instead of recalculating all sales every time, dbt only adds or updates the new sales records since the last run.

This is done by defining a unique key and a condition to identify new or changed rows. When dbt runs, it checks the existing table and only inserts or updates the rows that meet the condition. This saves time and computing resources, especially with large datasets.

Example

This example shows a simple incremental model in dbt that adds new orders based on an order_date column.

sql

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

select
    order_id,
    customer_id,
    order_date,
    total_amount
from source.orders
{% if is_incremental() %}
where order_date > (select max(order_date) from {{ this }})
{% endif %}

Output

A table named after the model is created or updated with only new orders where order_date is greater than the last date in the existing table.

When to Use

Use incremental materialization when working with large datasets that grow over time and where full refreshes are slow or costly. It is ideal for tables like event logs, sales transactions, or user activity that add new records regularly.

This approach reduces processing time and resource use by only handling new or changed data. However, it requires a reliable unique key and a way to identify new or updated rows.

Key Points

• Incremental materialization updates only new or changed data, not the whole table.
• Requires a unique key to identify rows for update or insert.
• Improves performance and reduces resource use on large, growing datasets.
• Needs a condition to filter new or updated rows during incremental runs.

Key Takeaways

Incremental materialization in dbt updates only new or changed rows to save time and resources.

It requires a unique key and a filter condition to identify incremental data.

Best used for large tables that grow over time, like logs or transactions.

Helps avoid costly full table rebuilds during each dbt run.