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Automating dbt Model Creation with Python: Streamlining Data Integration

To introduce efficiency in the development of data pipelines and to address the repetitive and time-consuming task of creating dbt models, that is commonly faced by data engineers, we have developed a custom Python solution that simplifies and automates the process of generating dbt models.

Motivation and User Requirements

We started by identifying a problem that data engineers in our company frequently encountered. In addition to being time-consuming and error-prone, the manual process of creating DBT models on Snowflake from data analysts' perspective was also laborious. We decided to create a Python script that automates the construction of dbt models from View Definitions in order to speed up the development process and guarantee that the models were ready for testing in a timely way after seeing the need for a more effective solution.

Problem Statement

The initial phase of our project involved understanding the requirements and challenges faced by data analysts. They often created views in their private schema and requested developers to make these views available for others in different schemas. However, the sheer volume of views made manual creation a daunting task.

Solution and Use

To address this issue, we created a potent Python script that makes use of Snowflake's features and Python's capabilities. By extracting view specifications, parsing and organizing the data, and dynamically creating DBT models, the script simplifies the procedure.

Here’s a step-by-step outline of the approach

Establishing Connection:

Using the Snowflake connector library, the script creates a secure connection to Snowflake that makes it simple to examine definitions kept in data analysts' private schema.

Retrieving View Definitions:

After connecting, SQL queries are run on the Snowflake database in order to obtain the view definitions needed in order to create a DBT model.

Parsing and Structuring:

The solution parses and organizes the unformatted text of view definitions using Python and regular expressions (regex). Table names and column names are among the essential elements that can be extracted using regex patterns.

Generating dbt Models:

The solution arranges the extracted data into an organized format compliant with dbt models by utilizing Python's string manipulation features. The dbt project is managed by using this structured output to dynamically generate SQL files, dbt transformation models, and configuration files (like schema.yml). Essentially, our method uses Python, regex, and Snowflake to automate the process of creating dbt models from View Definitions. The script increases productivity for both developers and data analysts by optimizing the data integration workflow and utilizing sophisticated parsing techniques in conjunction with Snowflake's cloud data platform.

Key Features and Benefits

Streamlined Workflow:

The solution saves up to 70% of development and testing time by automating the creation of dbt models.

Ease of Use:

Its modular structure allows developers to navigate and execute specific functions easily.

Improved Collaboration:

By automating the generation of DBT models, the solution facilitates better collaboration between data analysts and developers, hence minimizing the need for repeated interactions.

Error Elimination:

By doing away with manual writing, the solution lowers the possibility of human error and guarantees correctness and consistency while creating DBT models.


The solution offers flexibility in various data integration scenarios and can be readily adjusted to meet individual project requirements.


In summation, Our solution offers a comprehensive solution to the challenges of manual dbt model creation. By automating this process, we have significantly improved efficiency, collaboration, and productivity for our clients.

With our extensive knowledge of data integration and automation, we can assist your business in development and implementations of SAP specific analytical solutions. We execute on areas of Data and Analytics by embracing and organically expanding your current skills, whether it's developing new solutions or supporting your operations. Contact us right off the bat and we'll take it from there.

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