What is Materialization in dbt: Explanation and Examples

In dbt, materialization is how dbt decides to build and store your models in the database, such as creating tables, views, or incremental tables. It controls whether your data is saved as a physical table, a virtual view, or updated incrementally.

How It Works

Materialization in dbt is like choosing how you want to save your work after cooking a meal. You can either save it as a full dish (a table), a recipe card that you can use anytime (a view), or just add new ingredients to an existing dish (incremental).

When you run dbt, it looks at the materialization setting for each model and decides how to build it in your database. For example, if you choose table, dbt will create a full table with all the data. If you choose view, dbt creates a virtual table that runs the query every time you access it, saving storage but possibly slower reads.

This system helps you balance speed, storage, and freshness of data depending on your needs.

Example

This example shows how to set materialization to table and view in dbt model files.

sql

/* models/my_table_model.sql */
{{ config(materialized='table') }}

select * from source_table

/* models/my_view_model.sql */
{{ config(materialized='view') }}

select * from source_table

Output

When you run dbt, 'my_table_model' creates a physical table in your database, storing data permanently. 'my_view_model' creates a view that runs the query live each time you access it.

When to Use

Use table materialization when you want fast query performance and can afford storage space, like for large datasets you query often.

Use view materialization when you want to save storage and your data updates frequently, but can accept slower query times.

Use incremental materialization when your data grows over time and you want to update only new or changed records, saving time and resources.

For example, a sales report updated daily might use incremental, while a small lookup table might use view.

Key Points

• Materialization controls how dbt builds and stores models in your database.
• Common types are table, view, and incremental.
• table stores data physically for fast access.
• view creates virtual tables that run queries on demand.
• incremental updates only new data, saving time on large datasets.

Key Takeaways

Materialization in dbt decides how your data models are stored or accessed in the database.

Choose table for fast queries and persistent storage.

Choose view to save storage but accept slower query times.

Use incremental to efficiently update growing datasets.

Materialization helps balance speed, storage, and data freshness.