When using incremental models in dbt, dbt will update the target table based on changes in the source table, but it does not alter the structure of the target table.
In an incremental model, dbt uses a combination of SQL queries and metadata to identify new or changed records in the source table, and then updates the target table with these changes. However, dbt does not modify the schema of the target table, including the columns and their data types, unless you explicitly tell it to do so.
For example, let’s say you have an incremental model that pulls data from a source table called my_source_table and updates a target table called my_target_table. Here’s what might happen if you run dbt run after adding a new column to my_source_table:
- dbt will identify that there is a new column in
my_source_table. - dbt will create a temporary table with the same structure as
my_target_table, but with the new column added. - dbt will use SQL queries to update the records in
my_target_tablewith any new or changed records frommy_source_table. - dbt will drop the temporary table it created in step 2.
As you can see, dbt does not modify the structure of my_target_table to include the new column. If you want to add the new column to my_target_table, you would need to modify the table structure manually, either by running a SQL command directly against the target database or by using a dbt schema.yml file to manage table schema changes.
When using incremental models in dbt, dbt will update the target table based on changes in the source table, but will not modify the schema of the target table unless you explicitly tell it to do so.
when you use dbt to build your data pipeline, you are responsible for ensuring that the schema of your target tables matches the schema of your source data. This means that if you make a change to the schema of your source data, such as increasing the length of a VARCHAR column, you will need to modify the schema of your target table to match.
To update the schema of a target table in dbt, you can use a schema.yml file to define the desired schema of the table. This file specifies the columns and data types of the target table, as well as any constraints or indexes that should be applied. When you run dbt run, dbt will apply any changes specified in the schema.yml file to the target table.
For example, to increase the length of a VARCHAR column in a target table, you would modify the schema.yml file to reflect the new length of the column:
# schema.yml
version: 2
models:
- name: my_target_table
columns:
- name: id
type: int
primary_key: true
- name: name
type: varchar(20) # <-- Increase the length of the "name" column
When you run dbt run after making this change, dbt will alter the name column in my_target_table to match the new length of the column in the source data.
It is important to remember that you are ultimately responsible for ensuring that the schema of your target tables matches the schema of your source data.
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