{"id":25456432,"url":"https://github.com/hamza88-coder/cars_data_engineer_project","last_synced_at":"2026-01-24T18:32:45.605Z","repository":{"id":277320222,"uuid":"932047808","full_name":"Hamza88-coder/cars_data_engineer_project","owner":"Hamza88-coder","description":null,"archived":false,"fork":false,"pushed_at":"2025-02-16T15:11:17.000Z","size":5908,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-18T01:40:23.045Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Hamza88-coder.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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}},"created_at":"2025-02-13T09:26:41.000Z","updated_at":"2025-02-16T22:27:28.000Z","dependencies_parsed_at":"2025-02-18T01:40:25.439Z","dependency_job_id":"f6d38c9f-81d2-4139-8642-64e3e16ad10b","html_url":"https://github.com/Hamza88-coder/cars_data_engineer_project","commit_stats":null,"previous_names":["hamza88-coder/cars_data_enginneer_project","hamza88-coder/cars_data_engineer_project"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hamza88-coder%2Fcars_data_engineer_project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hamza88-coder%2Fcars_data_engineer_project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hamza88-coder%2Fcars_data_engineer_project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hamza88-coder%2Fcars_data_engineer_project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Hamza88-coder","download_url":"https://codeload.github.com/Hamza88-coder/cars_data_engineer_project/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254582907,"owners_count":22095518,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":[],"created_at":"2025-02-18T01:38:34.167Z","updated_at":"2026-01-24T18:32:45.576Z","avatar_url":"https://github.com/Hamza88-coder.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Azure Data Engineering Project\n\nThis project demonstrates the design and implementation of a robust **ETL pipeline** and **data transformation** workflow using **Azure** and **Databricks**. It involves the creation of various resources in **Azure** to build an efficient, automated data processing system for large-scale datasets.\n\n## Project Overview\n\nThis project utilizes multiple Azure services, including **Azure Data Factory**, **Azure Databricks**, **Azure SQL**, and **Azure Data Lake**, to manage and transform data. It follows the **Medallion Architecture** and aims to provide a dynamic, real-time ETL pipeline to efficiently process data from various sources.\n\n### Architecture Overview\n\nThe architecture for this project follows a structured data flow using the **Medallion Architecture** model. The system supports:\n- **Data Ingestion** using Azure Data Factory\n- **Data Transformation** with PySpark in Azure Databricks\n- **Data Storage** in Azure Data Lake and Azure SQL\n- **Real-time Data Processing** using ETL pipelines in Azure Data Factory\n\n![Project Architecture](architecture/azure_arch.gif)\n\n*Above is the architecture diagram showcasing the flow and interactions between components.*\n\n## Key Features\n\n- **Data Ingestion Pipeline:** Import data from various sources via Azure Data Factory.\n- **Data Transformation:** Perform transformations on large datasets using PySpark in Azure Databricks.\n- **Dynamic ETL Pipelines:** Automated and dynamic ETL pipelines to manage and process data.\n- **Data Modeling:** Implemented **Star Schema** with **Fact Tables** and **Surrogate Keys**.\n- **Slowly Changing Dimensions:** Implemented logic to manage slowly changing dimensions in data transformation.\n\n## Setup Instructions\n\nTo run this project on your own Azure account, follow these steps:\n\n### 1. **Set Up Azure Resources**\n   - Create the necessary Azure resources: **Data Lake**, **SQL Server**, **Data Factory**, and **Databricks**.\n\n### 2. **Clone the Repository**\n   Clone this repository to your local machine:\n   ```bash\n   git clone https://github.com/Hamza88-coder/cars_data_enginneer_project.git\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhamza88-coder%2Fcars_data_engineer_project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhamza88-coder%2Fcars_data_engineer_project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhamza88-coder%2Fcars_data_engineer_project/lists"}