How to Implement Data Vault with dbt on Snowflake

Data Vault is a powerful data modelling methodology when combined with dbt and Snowflake Data Cloud. It allows you to build a scalable, agile and flexible data architecture that can be easily maintained and updated.

In this article, we are going to provide guidelines and look at some best practices of how to implement Data Vault with dbt on Snowflake.

By adhering to these, you can ensure that you get the most out of the methodology’s modular approach and of your data warehouse’s capabilities.

The benefits of implementing Data Vault with dbt on Snowflake

There are numerous advantages of leveraging dbt to implement Data Vault on the Data Cloud. Starting with ease of use, dbt provides a simple, intuitive interface for building and managing Data Vault models.

With easy-to-understand syntax and a powerful templating system, you can quickly create and modify your Data Vault models to meet your specific needs. Data Vault on dbt allows you to incorporate a wide range of data sources and types, including structured, semi-structured and unstructured data.

Data Vault’s modular works well with dbt as it ensures that any changes to the data model are localised, making maintenance and updating of the warehouse more straightforward. FInally, the use of dbt's rigorous testing and version control capabilities also helps maintain high-quality code when building complex data pipelines.

A quick refresher on Data Vault 2.0

Data Vault has three main component:

• Hubs: the unique list of business keys, the central entities in your model;
• Links: the list of relationships between hubs;
• Satellites: the contextual, descriptive and historical data about hubs and links.

Having these components in place enables you to model complex processes, preserving agility and extensibility.

Data Vault on Snowflake

Snowflake's architecture is well-suited for storing large amounts of structured and semi-structured data, which makes it a natural fit for Data Vault's hub-and-spoke model.

Data Cloud ability to separate compute and storage enables Data Vault to scale out horizontally, which is necessary for handling large amounts of data and users. Snowflake's support for schema-on-read enables Data Vault to maintain a flexible schema that can accommodate changes and updates over time. Snowflake's built-in security features ensure that Data Vault data is secured and compliant with industry regulations. Snowflake's cloud-based platform provides elasticity and the ability to easily scale up or down, making it an ideal platform for implementing Data Vault.

Implementing Data Vault 2.0 on Snowflake leveraging dbt

To implement Data Vault on Snowflake Data Cloud, you can follow these steps:

1. Create a Snowflake account and set up your data warehouse;
2. Install and configure dbt on your local machine;
3. Define your Data Vault data model using dbt's templating system;
4. Build your ETL pipelines using dbt's modular approach;
5. Test and validate your data using dbt's built-in testing capabilities;
6. Deploy your code to Snowflake using dbt's version control features.

By following these steps, you can create a scalable, flexible, and maintainable Data Vault implementation on Snowflake Data Cloud that can meet the demands of your organisation.

Step 1: Create a Snowflake account and set up your data warehouse

The first step in implementing Data Vault on Snowflake is to create a Snowflake account and set up your data warehouse. Snowflake provides a cloud-based platform that enables you to store and process massive amounts of data without worrying about infrastructure limitations. You can create a Snowflake account and set up your data warehouse in just a few clicks.

Step 2: Install and configure dbt on your local machine

The next step is to install and configure dbt on your local machine. An open-source platform that enables you to build and manage Data Vault models, dbt provides a simple and intuitive interface, making it easy to create and modify your data models.

To install and configure dbt, you can follow the instructions provided in the documentation for your operating system. [link though] Once you have installed dbt, you can configure it by providing your Snowflake credentials.

Step 3: Define your Data Vault data model

You have already set up your Snowflake account and installed dbt and now it is time to implement the Data Vault layer in your data warehouse. To do that, you need to have a data model ready for your engineers.

To create such a model, identify the business entities, attributes, and relationships that need to be represented in the data warehouse. Hubs serve as a central repository for business entities, relationships can be tracked in links and all descriptive attributes go into satellite tables.

Step 4: Build your ETL pipelines using dbt's modular approach

Once you have defined your Data Vault data model, the next step is to build your ETL pipelines using dbt's modular approach. You can create a set of dbt models that define the logic for extracting, transforming and loading data from your source systems into your Data Vault warehouse.

Leveraging dbt’s macros to generate SQL code for hubs, links and satellites helps with automatisation and saves engineering time. They can also be used to generate code that implements common ETL patterns, such as incremental loading and slowly changing dimensions.

Step 5: Test and validate your data using dbt's built-in testing capabilities

Having built your ETL pipelines, the next step is to test and validate your data using dbt's built-in testing capabilities.

You can use dbt's tests to validate that your data conforms to your Data Vault model, ensuring that your data warehouse is accurate and reliable. dbt provides a range of built-in tests, such as schema tests, column tests, and relationships tests, that you can use to validate your data model. You can also create custom tests to validate your business rules and data integrity constraints.

Step 6: Deploy your code to Snowflake using dbt's version control features

Finally, after testing and validating your code, the last step is to deploy your code to Snowflake using dbt's version control features. dbt provides built-in support for version control systems, such as Git, which enables you to track changes to your code and collaborate with other team members.

You can use dbt's built-in commands to manage your code, such as dbt run and dbt test, which enable you to build and test your code. Once you are ready to deploy your code, you can use dbt's built-in deployment features to deploy your code to Snowflake.

Best practices for implementing Data Vault with dbt on Snowflake

To get the most out of Data Vault, you should follow some best practices when implementing it with dbt on Snowflake:

1. Defining a clear data model: Defining a clear data model helps ensure that all stakeholders understand the data warehouse's structure and how it relates to their business requirements;
2. Create hubs, links and satellites: Creating a foundational set of data constructs is crucial to building a flexible and scalable data architecture;
3. Iterative approach to development: As with any development project, it is essential to take an iterative approach to building your data warehouse. Start with a small set of data constructs, build out your ETL pipeline, test, and then expand as you go;
4. Focus on testing and version control: Testing and version control are critical when building complex data pipelines. Use dbt's built-in testing and version control capabilities to ensure data quality and maintainable code;
5. Leveraging Snowflake’s scalability: Snowflake's near-infinite scalability is a significant advantage for enterprise projects, so make the most of it by using Data Vault's modular approach to partition your data in a way that suits your specific needs. This way, you can ensure that your data warehouse scales effortlessly as your business grows.

By following these steps, you can implement Data Vault on Snowflake Data Cloud, creating a scalable, flexible, and maintainable data warehouse that meets the demands of your business.

We are here to help

Infinite Lambda has extensive experience helping companies leverage the full advantages of Data Vault for their enterprise data warehouses. Get in touch to explore the benefits for your project.

Meanwhile, learn more about Data Vault and its applications and take a closer look at DV components.

If you are wondering if Data Vault is the right approach for your project, get in touch and we will help you decide.