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Why Should You Replace Custom Python With DBT For Transformations?

Updated: Mar 13

Author: Akshaya Jayakumar


If you are a Data Engineer or an Architect, or an Analyst, who is thinking of using Python to write transformation scripts, this blog is for you. There are several advantages to choosing the Data Build Tool (DBT) for transformations. Here you can find what each does and when each of them best fits your architecture.

DBT vs. Python: Which one to choose for transformation?

Dbt (Data Build Tool) is an open-source command-line tool that helps analysts and data engineers write, test, and maintain their SQL code. It has a simple but powerful framework that can be used for building, testing, and deploying data transformations using SQL. DBT is specifically designed to handle SQL-based data transformations. It allows you to create reusable SQL transformations (macros) and test them, which makes it easier to maintain, version, and share the data pipeline with other team members. DBT is written in Python, so it can easily integrate with other Python tools and libraries.

Python, on the other hand, is a general-purpose programming language that can be used for a wide range of tasks, including data transformation. It provides a number of powerful libraries, such as pandas, numpy, and scikit-learn, for data manipulation, cleaning, and analysis. With Python, you can write custom scripts to transform data, and you have much more control over the entire process. You can also use Machine Learning libraries like scikit-learn, Tensorflow, and Pytorch to perform complex data transformation tasks.

DBT vs. Python - Comparison:




Ease of Development

- Low code

- More focus on transformation. No need to extract and load data. Works directly on the warehouse.

- This decreases the development effort

Need to extract, make necessary transformations and load back the data.


Better performance, as transformations occur within Snowflake

Response time for some voluminous transformations are usually longer when compared to DBT.

Data Transformation & Modeling

Can be done with SQL and Python models.

Need to write python code. Modeling needs external tools.

Data Grouping

Logical grouping of tranformation logics can be simplified using DBT tags.

Grouping of transformation logics is complex and time consuming.


Monitoring is direct and simpler with Run History of DBT

There is no direct window available for Monitoring. We should rely on any third party tools.

Data Movement

Data resides on the Data Warehouse (does not leave Snowflake) and DBT works upon the warehouse.

Python takes the Data outside the Data warehouse initially, and push the data back to the Warehouse.


DBT has built-in testing capabilities, including the ability to specify test cases.

Custom Python requires the use of external testing frameworks


We can build pipelines and apply CICD with the help of DBT

Custom python needs external tools to maintain CICD

Version Control

DBT has a built-in version control integration system, such as gibhub and gitlab.

Customer Python requires the use of manual version control and collaboration tools.


Simpler, need to write SQL select statements

Comparatively Complex

Job / Scheduling

Built-in jobs scheduling feature available

Custom python requires external tool or functionality for jobs scheduling.

Data Lineage & Documentation

DBT also generates DAG

Not any.


DBT comes with two offerings:

  1. DBT Cloud (paid)

- DBT Cloud Developer (free)

- DBT Cloud Team (paid)

- DBT Cloud Expertise (paid)

2. DBT Core (open source)

- Cost of running DBT will also depend on the underlying data warehousing platform.

Python is fully open source

Value DBT brings in place of Custom Python transformations

What can DBT do in addition to transformations?

For those who are not very familiar with DBT, below is the list of tasks that DBT can do:

  • Data Modeling: DBT can be used to model your data by defining tables and relationships between them in SQL.

  • Data Transformation: Can perform complex data transformations and create reusable SQL macros. You can also write transformations in Python with the DBT Python models feature.

  • Data Warehousing: DBT can build a data warehouse by defining the structure of your data and transforming it into the desired format.

  • Data Testing: Tests can be written to validate the accuracy and quality of your data. DBT provides built-in testing frameworks and test assertions that you can use to write tests.

  • Documentation: DBT automatically generates documentation for your data models and transformations. This makes it easier to understand how your data is being transformed and provides a single source of truth for your data pipeline.

  • Version Control: DBT integrates with version control systems such as Git, making it easy to manage and track changes to your data pipeline over time.

  • Deployment: DBT provides a deployment process that makes it easy to move your transformations from development to production.


Both DBT and custom Python scripts can be used for data transformation and analysis, but DBT is specifically designed for working with SQL and provides a more streamlined workflow for data engineers and analysts, while custom Python scripts offer more flexibility and can be used for a wider range of tasks beyond just SQL.

DBT is a highly evolving tool with simple but effective transformation strategies along with additional services. DBT can be a very reliable, future-proof transformation tool for many organizations. You need to decide on which one would suit your requirement and organization in the long run.




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