31 May Project setup part 1

How to start a Data Vault project: Folder Structure

Embarking on a Data Vault project can be both exciting and daunting.

In this four-part series, we will guide you through the essential steps for starting a Data Vault project using AutomateDV, the powerful Data Vault automation tool built on dbt.

What is AutomateDV?

Formerly known as dbtvault, AutomateDV streamlines Data Vault development by automating ETL processes through metadata-driven workflows. The tool offers a suite of templates tailored for Data Vault implementation on dbt, simplifying code creation and enhancing productivity. By strictly adhering to Data Vault 2.0 standards, AutomateDV ensures consistency and compatibility, making it an invaluable asset for data engineers when working on Data Vault projects. AutomateDV solves the creation of Data Vault structures so developers can spend more time focusing on delivering business value and meeting the business’ needs.

The Significance of Project folder Structure

The first step in any Data Vault project is establishing a robust and scalable project folder structure. A well-designed folder layout is paramount for the long-term success of your Data Vault project when using AutomateDV. It provides the basis for scaling the codebase of your project and helps to reduce maintenance overhead. It fosters maintainability as the project evolves, serving as the backbone of your project files.

Benefits of Structured Folders

Scalability: A well-structured layout accommodates growth in data volume and complexity.

Maintainability: Organised folders simplify code maintenance and updates.

Clarity: Intuitive folder structure enhances readability and comprehension.

Simplicity: Effective management of complexity in larger projects.

Data Vault Project Models Folder Structure

While we recommended a specific folder structure for organising Data Vault projects, it is important to note that you can tweak to accommodate your individual needs. Here’s our suggested approach for sub-folders within the parent “models” folder:

Raw Vault: Divided into hubs, links, and satellites for storing raw data.

Example: hub_customer.sql, lnk_customer_order.sql.

Staging: Contains staging SQL files.

Example: stg_customer.sql, stg_orders.sql.

Data Products: Stores SQL files for generating data marts or other data products.

Example: mart_orders.sql, mart_revenue.sql.

Embracing Naming Standards

Consistent naming conventions for hubs, links, satellites, and other components are vital for project organisation and coherence. Clear naming fosters understanding and streamlines collaboration among team members.

Conclusion: Setting the Stage for Success

A well-structured folder hierarchy sets the foundations for long term success in your Data Vault project. By investing time upfront to craft an organised structure, you pave the way for smoother development, minimising errors, and potential refactoring needs down the line.

With AutomateDV and a robust folder architecture at your disposal, you’re equipped to unlock the full potential of your data assets.