What Is Materializations In Dbt?

In dbt, materializations determine how the results of a model are stored in the data warehouse. Materializations define whether the model’s output should be materialized as tables, views, or other forms in the data warehouse. Common materializations include tables (table), views (view), and incremental models (incremental). Materializations allow dbt to efficiently manage and update the output of models, providing flexibility in terms of storage and performance optimizations.

Example for “table” materialization –

-- my_table_model.sql
-- Specify the materialization type as 'table'
{{ config(
    materialized='table'
) }}

-- model logic
SELECT
user_id,
COUNT(*) AS order_count
FROM
    orders
GROUP BY
user_id;

The config(materialized=’table’) statement specifies that the model should be materialized as a table in the data warehouse.

The SQL logic defines the transformation or aggregation, here it is counting the number of orders per user.
When you run dbt against this model, it will execute the SQL logic and create a physical table in the data warehouse, named after the model (e.g., my_table_model). The resulting table will contain the aggregated information specified in the model.

Example for “view” materialization –

-- my_view_model.sql
-- Specify the materialization type as 'view'

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

-- model logic
SELECT
user_id,
AVG(order_amount) AS avg_order_amount
FROM
    orders
GROUP BY
user_id;

The config(materialized=’view’) statement specifies that the model should be materialized as a view in the data warehouse. The SQL defines the transformation or aggregation. Here it is calculating the average order amount per user.

When you run dbt against this model, it will execute the SQL logic and create a logical view in the data warehouse, named after the model (e.g., my_view_model). The resulting view will contain the aggregated information specified in the model.

Note – Using the view materialization is useful when one wants to create a reusable logical layer on top of your raw data, providing a simplified and abstracted representation for downstream analytics users.

Incremental materialization in DBT

In dbt, incremental materialization is a technique used to efficiently update a target table with only the new or modified rows since the last run. This is particularly useful for large datasets, where recreating the entire table every time might be resource-intensive. Here’s an example:

-- my_incremental_model.sql
{{ config(
    materialized='incremental'
) }}

-- Specify the strategy for identifying new or modified rows
{{ config(
unique_key='user_id'
) }}

-- model logic
SELECT
user_id,
MAX(order_date) AS latest_order_date
FROM
    orders
GROUP BY
user_id;

The config(materialized=’incremental’) statement specifies that the model should be incrementally materialized.The config(unique_key=’user_id’) statement specifies the unique key(s) that dbt will use to identify new or modified rows. In this example, we use the user_id as the unique key.The SQL logic transformation or aggregation here it is finding the latest order date for each user.

When you run dbt against this model, it will:

Identify the existing rows in the target table based on the unique key.

Use this information to update or insert only the new or modified rows into the target table.

Note – This incremental materialization strategy is efficient for maintaining up-to-date analytics tables without recomputing everything, making it suitable for scenarios where data changes gradually over time.

We at Helical have more than 10 years of experience in providing solutions and services in the domain of data and have served more than 85+ clients. We are also DBT partners, hence in case if you are looking for certain assistance, consulting, services please do reach out on nikhilesh@Helicaltech.com