dbt vs Stored Procedures: Which One Is Better for Data Transformation?

Data transformation is an essential step in modern analytics, machine learning, and data-driven decision-making. Whether you’re working with a legacy database or building a new data system, transforming raw data into clean, structured tables is crucial for effective analysis.

When it comes to data transformation, many people are unsure whether to use code-based tools like dbt or rely on stored procedures directly within the database. While stored procedures can certainly perform transformations, dbt offers several advantages that improve the scalability and efficiency of data processes. Stored procedures and dbt (Data Build Tool) are two of the most common data transformation techniques. Stored procedures run inside the database, while dbt uses a modern, code-based approach that makes it easier to scale, manage changes, and collaborate with a team.

In this blog post, we’ll compare dbt vs stored procedures based on maintainability, governance, and functionality to help you choose the approach that best fits your needs.

What Are Stored Procedures?

SQL scripts that are stored inside a database and can be run repeatedly are called stored procedures. Structured conversions such as data cleansing, record insertion, and updating of the table can be made possible by using stored procedures. To keep data ready for routine processes, companies like to create stored procedures to run at a fixed time interval. They do need human intervention for updating and modifying because they are directly connected to the database.

For example, stored procedures are typically coded to execute at a specific time, say 6 AM daily, to update tables and make the data available for use in daily operations. Stored procedures can also be nested, where one procedure calls another, thus allowing the execution of structured workflows. Such procedures can be invoked by native database tools such as SQL Agent Jobs or third-party orchestration tools such as SSIS, Informatica, or Apache Airflow.

What is dbt?

dbt (Data Build Tool) is a new data transformation tool. Rather than coding SQL directly in the database as with stored procedures, dbt allows you to write SQL code in an independent project that’s easy to structure and manage. It doesn’t save the logic to the database—instead, it builds and executes the SQL as necessary. This makes your data work more agile, easier to automate, and more controlled.

dbt is a more advanced compiler that generates SQL scripts dynamically and runs them against the database. This makes it more modular and flexible, with users able to apply configurations to multiple transformations simultaneously.

Additionally, since dbt operates as a Python-based project, it can also be executed through command-line interfaces, making it highly automatable. Thus, users can integrate it into CI/CD pipelines and execute transformations programmatically without directly interacting with the database.

Why Use Modular dbt Models Instead of Stored Procedures?

dbt (Data Build Tool) is a new method to transform data in a data warehouse. Instead of using stored procedures, dbt is preferred due to the following reasons:

1. Improved Uptime

Before dbt, the team was refreshing pipelines manually for 6-8 hours a day. This downtime rendered their data warehouse nearly unusable during those hours. Once they switched to dbt, their uptime increased from 65% to 99.9%, building trust and reliability among data consumers.

2. Enabling New Use Cases

By adopting dbt, the team was then able to enable critical use cases that were not previously feasible due to the constraints of stored procedures. dbt made it easier for them to create data models that are simple to extend, modify, and evolve as the business requirements changed.

Challenges of Using Stored Procedures

Stored procedures have long been a popular method for data transformation, but new data pipelines require features such as transparent documentation, testability, and code reuse—areas in which stored procedures are lacking. One of the main disadvantages is that stored procedures hide the data flow, making it hard to follow intermediate steps. They also do not have built-in testability, so debugging and validation become harder. Redundancy is another issue, where similar logic is repeated across numerous stored procedures and causes code bloat and decreased team productivity.

We can visualize this challenge like this :

Why dbt is a Better Alternative

dbt provides a modular, step-by-step data transformation process that is self-documenting, testable, and reusable. Modularity is one of the fundamental principles of dbt—each business object within a data pipeline (orders data), say, is defined within a single model. These models are consistently grouped into layers to have a clean path from raw to analysis-ready data. This makes it more maintainable and minimizes redundancy, decreasing confusion between development teams.

By dbt, data pipelines become streamlined and transparent:

A further essential dbt strength is the ease with which it can be integrated with version control software like Git. This makes it easy for teams to track changes, collaborate, and test out changes before going live. Legacy teams using stored procedures typically don’t have much formal change tracking and, therefore, may have more complicated analytics workflows. While this isn’t entirely a stored procedure issue but rather a workflow management issue, legacy tools complicate data engineering excessively:

dbt vs Stored Procedures: A Comparison

Feature	Stored Procedures	dbt (Data Build Tool)
Architecture	Logic operates on your database directly.	Runs on top of cloud data warehouses, such as Snowflake and BigQuery.
Code Style	Step-by-step instructions (in imperative SQL).	Simply say what you want in SQL (declarative).
Collaboration	The monitoring of changes is difficult, and there is just a single developer doing it.	Designed for teamwork. Capitalizes on Git, pull requests, and code reviews.
Testing	Testing is typically done by hand or is skipped.	Built-in tests include nulls, duplicates, and relationships.
Documentation	Usually undocumented or coded in.	Clean, visual records are created automatically, so there is data lineage.
Modularity	Reuse is difficult. Reasoning is frequently repeated.	Encourages the use of SQL models and macros for developing reusable, modular code.
Deployment	Manual and error-prone. You often cut and paste code directly into the database.	The automated builds are executed through tools like GitHub Actions and dbt Cloud.
Performance	Fast for tiny in-place database operations.	Appropriate for incremental and materialized big data analytics.
Best For	Legacy systems have operational logic in databases.	Clean and scalable cloud data pipelines and new analytics processes.

• Governance Issues with Stored Procedures: Stored procedures make quality control more complex because they lack inherent testing and documentation. Teams need to verify data manually, develop extra verification queries, and develop custom constraints, which vary between databases. The majority of cloud data warehouses do not have strict constraints like primary keys, hence leaving errors and low-quality data open to exposure.

• How dbt Improves Governance: dbt tests data automatically, detecting issues like duplicates or missing values before they affect reports. It also generates tidy documentation of data lineage and dependency for increased transparency. With version control and testing incorporated into it, dbt renders data transformations stable, transparent, and controllable.

When to Use Stored Procedures vs. dbt

Choose Stored Procedures if:

• Your application is heavily rooted in an old database with procedures already in place.
• Performance and security within the database are a top concern.
• Your team is very SQL-savvy and prefers to remain within the database.

Choose dbt if:

• You are working with newer cloud data warehouses like Snowflake, BigQuery, or Redshift.
• You need a modular, testable, and version-controlled process of transformation.
• Your team follows the best practices of modern data engineering and values scalability, collaboration, and documentation.

For flexibility, transparency, and maintainability, dbt is the better choice in today’s data workflows.

Advantages of Stored Procedures

• Optimized Performance: Runs in the database itself, removing latency and increasing execution speed.
• Increased Security: Limits direct access to raw tables, increasing data security.
• Familiarity & Legacy Support: The majority of teams are already familiar with stored procedures, increasing adoption.

Limitations of Stored Procedures

• Hard to Maintain:  Large, monolithic SQL scripts are difficult to debug and collaborate.
• Limited Modularity:  Limited code reuse, leading to repeated and inefficient queries.
• Difficult Testing: Difficult setup for testing and validation.

Advantages of dbt

• Modular & Scalable: Divides transformations into reusable SQL templates with simple maintenance.
• Automated Testing & Documentation: Ensures data quality through built-in testing and auto-generated documentation.
• Enhanced Collaboration: Natively integrates with Git and CI/CD, allowing teams to collaborate seamlessly.
• Optimized for New Warehouses: Designed for engines like Snowflake, BigQuery, and Redshift, best suited for ELT workflows.

Limitations of dbt

• Cloud Dependency: Best with cloud data warehouses and limiting on-premises usage.
• Learning Curve: Stored procedure teams will have to get used to it.
• Not a Full Replacement: dbt excels at transformations but not at complex procedural logic.

Conclusion

While both dbt and stored procedures are used for data transformation, dbt offers several advantages in terms of maintainability, automation, and governance. Stored procedures are tightly integrated with the database and may be suitable for simpler workflows but often lack features like version control, modularity, and automated quality checks.

dbt is designed to foster collaboration, streamline development workflows, and enforce best practices in data transformation. With modern tools like Git and YAML configurations, dbt offers a scalable and sustainable approach to managing transformation logic.

That’s exactly where Hevo Transformer comes in. Built on top of dbt Core, Hevo Transformer simplifies data transformation with an intuitive interface, seamless Git integration, and native scheduling, helping teams scale their data workflows without the operational overhead.

Looking to modernize your data transformations? Try Hevo Transformer today and experience the power of dbt, minus the complexity.

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