{"id":33559835,"url":"https://github.com/augo-amos/dbt-bigquery-analytics","last_synced_at":"2026-05-23T16:45:13.833Z","repository":{"id":325702072,"uuid":"1101878856","full_name":"augo-amos/dbt-bigquery-analytics","owner":"augo-amos","description":"A modern data analytics platform built on Google BigQuery that transforms raw e-commerce data into actionable business intelligence.","archived":false,"fork":false,"pushed_at":"2025-11-22T21:33:05.000Z","size":8,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-05-23T16:44:54.112Z","etag":null,"topics":["analytics","bigquery","dbt","gcp"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/augo-amos.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-11-22T12:10:02.000Z","updated_at":"2025-11-22T21:45:38.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/augo-amos/dbt-bigquery-analytics","commit_stats":null,"previous_names":["augo-amos/dbt-gcp-project"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/augo-amos/dbt-bigquery-analytics","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/augo-amos%2Fdbt-bigquery-analytics","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/augo-amos%2Fdbt-bigquery-analytics/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/augo-amos%2Fdbt-bigquery-analytics/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/augo-amos%2Fdbt-bigquery-analytics/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/augo-amos","download_url":"https://codeload.github.com/augo-amos/dbt-bigquery-analytics/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/augo-amos%2Fdbt-bigquery-analytics/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33404268,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-23T04:15:53.637Z","status":"ssl_error","status_checked_at":"2026-05-23T04:15:53.242Z","response_time":53,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analytics","bigquery","dbt","gcp"],"created_at":"2025-11-27T23:00:22.467Z","updated_at":"2026-05-23T16:45:13.815Z","avatar_url":"https://github.com/augo-amos.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# E-Commerce Analytics on BigQuery\n\nA modern data analytics platform built on Google BigQuery that transforms raw e-commerce data into actionable business intelligence.\n\n## Overview\n\nThis 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.\n\n## Data Architecture\n\n### Core Tables\n- **`customers`**: 100+ customer records with demographic and segmentation data\n- **`products`**: 30+ products across electronics, furniture, and accessories\n- **`orders`**: 150+ transactions with order details and status tracking\n- **`order_items`**: Line-item details connecting orders to products\n\n### Key Features\n- **Customer Lifetime Value (CLV)**\n- **RFM Segmentation** (Recency, Frequency, Monetary)\n- **Product Performance Analytics**\n- **Sales Trend Analysis**\n- **Marketing Campaign Targeting**\n\n## Technical Stack\n\n- **Data Warehouse**: Google BigQuery\n- **Data Modeling**: SQL-based transformations\n- **Analytics**: Custom SQL queries and macros\n- **Data Formats**: JSONL \u0026 CSV for data loading\n\n## Business Insights\n\n### Customer Segmentation\n- **VIP Customers**: $1000+ lifetime spend\n- **Premium**: $500-999 spend\n- **Regular**: $100-499 spend\n- **Activity Tiers**: Active, Warming, Cold, Dormant\n\n### Key Metrics\n- Customer acquisition trends\n- Average order value (AOV)\n- Monthly recurring revenue (MRR)\n- Product category performance\n- Customer retention rates\n\n## Project Structure\n\n```\nmodels/\n├── staging/\n│   ├── stg_customers.sql\n│   └── stg_orders.sql\n├── marts/\n│   ├── customer_segments.sql\n│   ├── product_performance.sql\n│   └── sales_analytics.sql\n└── macros/\n    └── customer_segmentation.sql\n```\n\n## Quick Start\n\n1. **Load Data to BigQuery**\n   ```sql\n   -- Create dataset and load customer, product, order tables\n   bq load --source_format=CSV your_dataset.customers customers.csv\n   ```\n\n2. **Run Core Analytics**\n   ```sql\n   -- Customer segmentation query\n   SELECT * FROM `your-project.analytics.customer_segments`\n   WHERE customer_tier = 'VIP';\n   ```\n\n3. **Generate Reports**\n   ```sql\n   -- Monthly sales performance\n   SELECT * FROM `your-project.analytics.monthly_sales_trends`;\n   ```\n\n## Sample Queries\n\n### Top Performing Products\n```sql\nSELECT \n  product_name,\n  category,\n  SUM(quantity) as units_sold,\n  SUM(line_total) as revenue\nFROM `your-project.analytics.order_items` oi\nJOIN `your-project.analytics.products` p USING (product_id)\nGROUP BY 1, 2\nORDER BY revenue DESC;\n```\n\n### Customer Retention Analysis\n```sql\nSELECT\n  customer_tier,\n  COUNT(*) as customer_count,\n  AVG(total_spent) as avg_lifetime_value\nFROM `your-project.analytics.customer_segments`\nGROUP BY 1;\n```\n\n## Use Cases\n\n- **Marketing**: Targeted campaign segmentation\n- **Sales**: Customer prioritization and outreach\n- **Product**: Inventory and category optimization\n- **Executive**: Business performance dashboards\n\n## Performance\n\n- **Query Optimization**: Leveraging BigQuery's columnar storage\n- **Data Freshness**: Daily updates via scheduled queries\n- **Scalability**: Handles 100K+ records efficiently\n\n## Contributing\n\nThis project demonstrates modern cloud data warehousing patterns and can be extended with:\n- Real-time data streaming\n- Machine learning predictions\n- Advanced customer analytics\n- Multi-channel attribution","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faugo-amos%2Fdbt-bigquery-analytics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faugo-amos%2Fdbt-bigquery-analytics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faugo-amos%2Fdbt-bigquery-analytics/lists"}