september 21, 2025
Using dbt (data build tool) in traditional data warehouse development
Data Build Tool (dbt) – has become an essential tool for analysts and data engineers, streamlining data transformation and management processes in modern data warehouses.
By defining acyclic dependencies and automating model builds, dbt helps ensure the consistency and freshness of analytical data: the framework consolidates and connects all logic in one place – from raw inputs to final outputs and their reuse.
Our experience with dbt comes from developing a real-world data warehouse for a client, which has been operating and maintained successfully for over a year. This validates the tool’s reliability and effectiveness in a production environment.
A model in dbt is a SQL query that transforms raw data into a ready-to-use table or view, with configuration options for storage and data descriptions, including built-in testing capabilities (e.g. automated tests can check for column uniqueness, duplicates, null values, or pattern conformity). In other words, a model represents a data transformation step.
To dynamically generate SQL code — significantly increasing flexibility and automation when creating models, scripts, and configurations — dbt makes active use of Jinja templating.
In scenarios where reference tables in the warehouse are continuously updated, the optimal approach is to use a merge strategy with incremental materialisation. This method updates only new or modified records, modifying the existing table without needing to rebuild it entirely.
Example of a dbt model configuration for updating a reference table from a source table:
A model with this configuration updates only the changed or new records in the reference table using the unique key.
To reduce response time when working with large data volumes, ensure faster and more resource-efficient access to the model, and improve query performance on the reference table, a clustered index can be created using a post-hook (an SQL script that performs additional actions on the target table during the post-processing stage of a dbt model).
Since the index is created only once when the table is initially built, the post-hook script uses the condition «if not is_incremental()». This ensures that the index is created only during the initial model build or its complete rebuild using the «—full-refresh» parameter, avoiding repeated execution of this operation during incremental updates.
Example of a complete dbt model for a reference table with incremental updates:
In conclusion, it is safe to say that using dbt significantly simplifies and standardises data transformation processes, ensuring transparency and repeatability of analytical models. The integration of Jinja templating provides flexible mechanisms for generating dynamic SQL, making it easy to adapt models to various business requirements.
The experience of running a production-grade data warehouse built with dbt for over a year confirms its stability, scalability, and ease of maintenance. Thus, dbt proves to be a reliable and efficient solution for building modern analytical platforms.
