https://github.com/augo-amos/dbt-bigquery-analytics
A modern data analytics platform built on Google BigQuery that transforms raw e-commerce data into actionable business intelligence.
https://github.com/augo-amos/dbt-bigquery-analytics
analytics bigquery dbt gcp
Last synced: 28 days ago
JSON representation
A modern data analytics platform built on Google BigQuery that transforms raw e-commerce data into actionable business intelligence.
- Host: GitHub
- URL: https://github.com/augo-amos/dbt-bigquery-analytics
- Owner: augo-amos
- Created: 2025-11-22T12:10:02.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-11-22T21:33:05.000Z (7 months ago)
- Last Synced: 2026-05-23T16:44:54.112Z (28 days ago)
- Topics: analytics, bigquery, dbt, gcp
- Homepage:
- Size: 7.81 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# E-Commerce Analytics on BigQuery
A modern data analytics platform built on Google BigQuery that transforms raw e-commerce data into actionable business intelligence.
## Overview
This project implements a complete e-commerce analytics solution using Google BigQuery, featuring customer segmentation, product performance analysis, and sales tracking. The data model supports 100+ customers, 150+ orders, and comprehensive business reporting.
## Data Architecture
### Core Tables
- **`customers`**: 100+ customer records with demographic and segmentation data
- **`products`**: 30+ products across electronics, furniture, and accessories
- **`orders`**: 150+ transactions with order details and status tracking
- **`order_items`**: Line-item details connecting orders to products
### Key Features
- **Customer Lifetime Value (CLV)**
- **RFM Segmentation** (Recency, Frequency, Monetary)
- **Product Performance Analytics**
- **Sales Trend Analysis**
- **Marketing Campaign Targeting**
## Technical Stack
- **Data Warehouse**: Google BigQuery
- **Data Modeling**: SQL-based transformations
- **Analytics**: Custom SQL queries and macros
- **Data Formats**: JSONL & CSV for data loading
## Business Insights
### Customer Segmentation
- **VIP Customers**: $1000+ lifetime spend
- **Premium**: $500-999 spend
- **Regular**: $100-499 spend
- **Activity Tiers**: Active, Warming, Cold, Dormant
### Key Metrics
- Customer acquisition trends
- Average order value (AOV)
- Monthly recurring revenue (MRR)
- Product category performance
- Customer retention rates
## Project Structure
```
models/
├── staging/
│ ├── stg_customers.sql
│ └── stg_orders.sql
├── marts/
│ ├── customer_segments.sql
│ ├── product_performance.sql
│ └── sales_analytics.sql
└── macros/
└── customer_segmentation.sql
```
## Quick Start
1. **Load Data to BigQuery**
```sql
-- Create dataset and load customer, product, order tables
bq load --source_format=CSV your_dataset.customers customers.csv
```
2. **Run Core Analytics**
```sql
-- Customer segmentation query
SELECT * FROM `your-project.analytics.customer_segments`
WHERE customer_tier = 'VIP';
```
3. **Generate Reports**
```sql
-- Monthly sales performance
SELECT * FROM `your-project.analytics.monthly_sales_trends`;
```
## Sample Queries
### Top Performing Products
```sql
SELECT
product_name,
category,
SUM(quantity) as units_sold,
SUM(line_total) as revenue
FROM `your-project.analytics.order_items` oi
JOIN `your-project.analytics.products` p USING (product_id)
GROUP BY 1, 2
ORDER BY revenue DESC;
```
### Customer Retention Analysis
```sql
SELECT
customer_tier,
COUNT(*) as customer_count,
AVG(total_spent) as avg_lifetime_value
FROM `your-project.analytics.customer_segments`
GROUP BY 1;
```
## Use Cases
- **Marketing**: Targeted campaign segmentation
- **Sales**: Customer prioritization and outreach
- **Product**: Inventory and category optimization
- **Executive**: Business performance dashboards
## Performance
- **Query Optimization**: Leveraging BigQuery's columnar storage
- **Data Freshness**: Daily updates via scheduled queries
- **Scalability**: Handles 100K+ records efficiently
## Contributing
This project demonstrates modern cloud data warehousing patterns and can be extended with:
- Real-time data streaming
- Machine learning predictions
- Advanced customer analytics
- Multi-channel attribution