Incremental updates allow DBT to process only new or updated records since the last run, reducing the time and resources required to transform the data. This is particularly useful when dealing with historical data that doesn’t change often.
To set up incremental updates in DBT, you’ll need to configure the “materialization” setting in your DBT model. Materializations control how DBT transforms your data and writes it to the destination table. The incremental materialization appends new or updated records to the existing table instead of rebuilding the entire table from scratch.
Here’s a detailed example using the table freshers_cust_table:
1. Create a DBT model, let’s say freshers_cust_table.sql. Write a SQL query to select data from your source table(s) that you want to transform and load into the freshers_cust_table. For example:
SELECT
customer_id,
first_name,
last_name,
signup_date,
country
FROM
source_schema.source_table
2. Configure the model to use the incremental materialization. You can do this by adding a config block at the top of your freshers_cust_table.sql file:
{{ config(materialized='incremental') }}
SELECT
customer_id,
first_name,
last_name,
signup_date,
country
FROM
source_schema.source_table
3. Add a filter to your SQL query to process only new or updated records since the last run. You can use the DBT_MAX_TIMESTAMP macro, which returns the maximum value of a timestamp column from the target table. Modify your SQL query like this:
{{ config(materialized='incremental') }}
SELECT
customer_id,
first_name,
last_name,
signup_date,
country
FROM
source_schema.source_table
WHERE
-- Process only records with a signup_date greater than the max signup_date in the target table
signup_date > (
SELECT
COALESCE(MAX(signup_date), '1900-01-01')
FROM
{{ this }}
)
With these configurations, when you run dbt run, DBT will only process new or updated records with a signup_date greater than the maximum signup_date in the freshers_cust_table. This makes the update process more efficient by only processing the data that has changed since the last run.
Please note that this example assumes you have a source table with the necessary data columns and a timestamp column (signup_date in this case) to track changes. You may need to adapt the example based on your data schema and requirements.
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