🍔 US Fast-Food Restaurant Dashboard

🛠️ Technologies Used

• BigQuery – Cloud data warehouse for storing and querying large datasets
• dbt – Data transformation tool for building and managing ELT pipelines
• Looker Studio – Data visualization and reporting platform

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🔍 Project Overview

This project demonstrates an end-to-end analytics solution using BigQuery, dbt, and Looker Studio.

The dataset is synthetic restaurant data about a US fast-food chain (orders, customers, suppliers, products, etc.), generated with jaffle-shop-generator.

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📊 Dashboard

👉 Explore the live dashboard:
US Fast-Food Dashboard

⚠️ If some charts don’t load properly, try refreshing the page.

📥 Or download as PDF:
Dashboard PDF

Dashboard Pages

1. Overview Page – General chain-wide metrics (revenue, customers, number of products sold, etc.) with a year filter.
2. Location Page – Same metrics broken down per location, with a filter to analyze individual restaurants.

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🔄 Data Transformation (dbt)

The dbt project follows a layered approach:

1. Stage Layer

Primary access to source data from BigQuery. Includes type casting, renaming, and initial cleaning.
Tables:

• Customers (who place orders)
• Orders (from those customers)
• Products (food & beverages)
• Order Items (linking orders and products)
• Supplies (ingredients and materials)
• Stores (where orders are fulfilled)

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2. Intermediate Layer

Contains auxiliary models used for joins, enrichments, and calculations.

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3. Marts Layer

Final layer, modeled using a Kimball star schema:

• Fact Table: fct_orders
• Dimensions: dim_customers, dim_locations, dim_products
• Dashboard Table: looker_dashboard – combines fact and dimension tables for efficient reporting

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