Data modeling is an important aspect of any data-driven organization. In a data-driven organization, data models are created to process the incoming data in an efficient and accurate manner. Data is processed by the data models and transformed into useful information for the stakeholders. With time, the data models become outdated and need to be updated to reflect the latest changes in the data. In this article, we will discuss how to implement auto-refreshing models for incremental models with schema changes in DBT.
Auto-refreshing models:
Auto-refreshing models are data models that automatically update their state as new data becomes available. This is a useful feature as it eliminates the need for manual updates, which can be time-consuming and error-prone. With DBT, auto-refreshing models can be implemented for incremental models that process only new data that has been added to the data source.
Implementing auto-refreshing models for incremental models with schema changes:
DBT provides a feature called “materializations”, which allows the data team to create data models that are optimized for query performance. In DBT, a materialization is a view that is created on top of the underlying data model. The materialization is used to store the transformed data, and it is updated every time the underlying data model is updated.
To implement auto-refreshing models for incremental models with schema changes in DBT, the following steps must be followed:
Create a model that represents the source data: The first step is to create a data model that represents the source data. This data model should be designed to handle the new data that is added to the source data over time.
Create a materialization: Once the data model has been created, the next step is to create a materialization that is based on the data model. The materialization is created using the “materialized” directive in the DBT code.
Update the materialization: As new data is added to the source data, the materialization must be updated to reflect the changes in the data. This can be done by running the DBT command “dbt run”.
Handle schema changes: As the data changes over time, the schema of the data may change. When this happens, the data model must be updated to reflect the new schema. If the data model is not updated, the materialization will not work correctly.
Example:
Let’s consider a scenario where a company is collecting data about its customers. The data source is a database table named “customers”. The data model for the customers table is created using the following DBT code:
{% macro customers_model(model_name) %}
{{
config(
materialized='incremental',
tags=['customers_model']
)
}}
select *
from {{ ref(model_name) }}
{% endmacro %}
{{ customers_model("customers") }}
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