Jouri Lagarde

Exploring Data Modelling on Snowflake

Snowflake has emerged as a prominent cloud-native data platform, providing scalability, flexibility, and efficiency for managing and analyzing data. As organizations increasingly adopt Snowflake, the need for robust data modelling practice grows. Although Snowflake allows for highly flexible code and complex data models, good governance is required to stimulate consistency across data models. Different organizations may need different development experiences. In this blog, we will delve into various solutions that integrate with Snowflake, enhancing data modelling capabilities and user-experience.

Matillion – Drag 'n Drop Shop

Matillion is a popular ETL and data transformation tool that seamlessly integrates with Snowflake, offering comprehensive data modelling capabilities. This solution empowers organizations with a user-friendly interface, drag-and-drop functionalities, and pre-built connectors. Users can easily define and manage complex data transformations, orchestrate workflows, and build data models within a visually appealing environment. Matillion orchestrates the process of loading, cleansing, transforming, and preparing data for analysis in Snowflake. Though, it’s low-code environment could also be a challenge when organizations require complex data transformations.

DBT - Load your Code

DBT (Data Build Tool) focuses on building data workflows, empowering data analysts and engineers to transform raw data into curated and reliable analytics-ready datasets. By leveraging code efficiently, DBT enables version-controlled data transformations, documentation, and testing, resulting in robust data models. DBT also encourages a modular approach, enabling reusability and scalability in the data modelling process. While the focus on code provides a strong tool for a code-savvy data engineer, the solution tends to have a steeper learning curve than other solutions.

Coalesce – Embrace the Interface

Coalesce is uniquely built for Snowflake and integrates seamlessly with its features and capabilities to simplify and scale data management. It has a user-friendly interface and many built-in features within its environment. While offering a low-code experience with drag-and-drop experience, it provides the flexibility to edit code geared to your needs. Coalesce creates an out-of-the-box documentation for data models including lineage for columns as well as your model building nodes. While provides a simplified data modeling experience, it may lack some advanced features available in more complex data modeling tools.


We’ve only plotted a few of the many third-party solutions built for Snowflake, each providing its own enhanced capabilities to streamline data transformation and modelling processes. Whether through ETL (or ELT), data exploration, analytics engineering, or data integration tools, these options empower organizations to construct robust data models and simplify the documentation process. By seamlessly integrating with Snowflake, these third-party tools expand the already rich functionality of the platform, supporting data-driven decision-making and fuel business growth. Choosing between them will heavily depend on organizational needs and embracement of code or low-code.

Would you like to know more about this topic, let us know. In the mean time you can check out how Joran explains Snowflake. Or explore our expertise page for additional resources and insights.