Data Modeling with dbt Core and Snowflake

This project implements a complete data model for an e-commerce dataset using dbt Core and Snowflake. It transforms raw CSV data into a well-structured, analytics-ready model within two Snowflake databases: raw and analytics.

The purpose;

• Transform raw CSV data into a well-structured, analytics-ready model accessible in Snowflake.
• Enable efficient querying and analysis of e-commerce metrics like sales trends, customer behavior, and product performance.
• Provide a foundation for building dashboards, reports, and further analytical models.

Tools Used;

• Snowflake
• dbt Core
• git
• VS Code (IDE)

The architechture

Key features include:

• Stage area: Transforms and cleans raw data before loading into dimensions and facts.

• Dimensional tables: Stores normalized attributes for customers, products, categories, shippers, and employees.

• Fact tables: Aggregates sales data from orders and order details for analysis.

• dbt Core: Automates model development, testing, and deployment for reliable data pipelines.

The tables and the ERP Diagram generated

• Orders
• Orders details
• Customers
• Employees
• Products
• Categories
• Shippers

The ERP Diagram

Documentation:

• Model descriptions and documentation can be found in the models directory.
• For general dbt documentation, refer to https://docs.getdbt.com.