How does DBT handle incremental data loading?

DBT (Data Build Tool) does not have a built-in feature for incremental data loading, but it can be accomplished by using DBT’s filtering and macro capabilities in combination with a database’s incremental loading functionality. This can be done by using the following steps:

1. Use a database’s incremental loading feature (e.g. INSERT INTO … ON DUPLICATE KEY UPDATE) to only load new or updated rows into a staging table.
2. In DBT, create a model that filters the data from the staging table to only include new or updated rows. This can be done by using a macro to generate a SQL statement that selects rows from the staging table based on a timestamp or other unique identifier.
3. Create a DBT model that transforms the incremental data and loads it into the final table. This can be done by using a DBT model with the incremental=True configuration to specify that the model should only load new rows and update existing rows.
4. Run DBT with the target models in each run. This can be done by specifying the -m option and the name of the model to run.
5. Schedule the DBT run with incremental data loading in a cron job or cloud function to update the final table periodically.

Here is an example of how you might use DBT to handle incremental data loading:

1. Use a database’s incremental loading feature (e.g. INSERT INTO … ON DUPLICATE KEY UPDATE) to only load new or updated rows into a staging table.
2. In DBT, create a model that filters the data from the staging table to only include new or updated rows.

{% set incremental_data = 
    (select * from {{ref('staging_table')}}
     where updated_at > (select max(updated_at) from {{this.schema}}.incremental_table)) %}

{{incremental_data}}

{{ config(materialized='table', incremental=True) }}

select 
    id,
    name,
    address
from {{ref('incremental_data')}}

dbt run -m incremental_data
dbt run -m final_table