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This project simulates a real-world logistics scenario, processing 50,000+ trip records to analyze fuel efficiency across a commercial fleet.\n\n\n## 🏗️ Data Architecture \u0026 Modeling\n\nThe project is structured following the **Medallion Architecture** to ensure data traceability and quality:\n\n**1. BRONZE Layer (Staging)**\n* `stg_trips`: Technical cleansing, date normalization, and sensor error filtering.\n* `stg_vehicles`: Standardization of truck fleet metadata.\n* `stg_drivers`: Processing of driver master records.\n\n**2. SILVER Layer (Intermediate)**\n* `silver_fleet_performance`: Integration table joining telemetry (trips) with dimensions (drivers and vehicles). Includes fuel efficiency business logic and outlier handling.\n\n**3. GOLD Layer (Marts)**\n* `gold_fleet_stats`: Final reporting table for business stakeholders. Contains aggregated metrics and performance rankings by model and driver.\n\n\n## 👷🏻‍♂️ Transformation Summary\n\n| Layer | Input | Output | Key Operations |\n| :--- | :--- | :--- | :--- |\n| **Bronze** | Raw Data | `stg_` | `SAFE.PARSE_DATE`, casting, and initial validation. |\n| **Silver** | Staging | `silver_` | Massive `LEFT JOIN` and `L/100km` calculation. |\n| **Gold** | Silver | `gold_` | `GROUP BY` and performance ranking aggregation. |\n\n\n## 🧪 Data Quality \u0026 Testing\nRobustness is guaranteed through dbt tests:\n- **Generic Tests:** `not_null` and `unique` on primary keys.\n- **Business Tests:** `dbt_utils.accepted_range` to ensure fuel consumption and distances fall within realistic physical bounds (e.g., 0 to 200 L/100km).\n\n\n## 🛠️ Tech Stack\n- **Data Transformation:** dbt (Data Build Tool)\n- **Warehouse:** Google BigQuery\n- **Environment:** Conda\n- **Data Generation:** Python (Pandas/Numpy)\n- **Visualization:** Looker Studio\n\n\n## 📦 Dependencies \u0026 Packages\nThis project utilizes the following **dbt packages** to extend functionality:\n* **dbt-utils:** Used for advanced data quality testing (`accepted_range`) and cross-database macros.\n\n\n## 🚀 How to Run\n1. Clone the repo.\n2. Setup your `profiles.yml` for BigQuery.\n3. Install dependencies: `dbt deps`.\n4. Run the pipeline: `dbt run`.\n5. Execute tests: `dbt test`.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frobertofernandezmartinez%2Flogistics-fleet-dbt","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frobertofernandezmartinez%2Flogistics-fleet-dbt","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frobertofernandezmartinez%2Flogistics-fleet-dbt/lists"}