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The pipeline showcases **data engineering capabilities**, including raw data extraction, transformation into a **star schema** data warehouse model, and loading for analytical purposes. It uses modern data engineering tools and practices to deliver a robust, scalable, and maintainable solution.\n## Key Features\n- **Data Warehouse Design**: Implements a **star schema** for optimized querying and analytics, featuring a **fact table** and multiple **dimension tables**.\n- **Modular ELT Workflow**:\n  - **Extract**: Data ingestion from the e-commerce dataset.\n  - **Load**: Data stored in **Snowflake**, a modern cloud-based data warehouse.\n  - **Transform**: Data transformation using **dbt** to clean, model, and create a star schema.\n- Orchestration with **Apache Airflow**: Automates the pipeline execution in a **Dockerized environment**.\n- **Dockerized Setup**: Ensures portability and reproducibility across systems.\n## Tools and Technologies\n- **Python**: Core programming language for the project.\n- **Apache Airflow**: Orchestrates the ELT pipeline.\n- **dbt (Data Build Tool)**: Handles SQL-based transformations.\n- **Snowflake**: Cloud-based data warehouse for data storage and analytics.\n- **Docker**: Containerizes the environment for consistent development and deployment\n## Star Schema Design\nThe **star schema** organizes the data warehouse for efficient analytical querying.\n### Fact Table\n- `fact_sales`: Contains transactional data, including order details, payments, and shipping information.\n### Dimension Tables\n1. `dim_customers`: Customer-related attributes such as location and demographics.\n2. `dim_sellers`: Seller-related information, including locations.\n3. `dim_products`: Product details, including categories and dimensions.\n4. `dim_geolocation`: Geographical information for customers and sellers.\n5. `dim_time`: Temporal data for time-series analysis.\n6. `dim_reviews`: Customer reviews and ratings.\n## Learning Outcomes\nThis project highlights:\n- Expertise in modern **ELT workflows**.\n- Design and implementation of a **star schema** for data warehouses.\n- Integration of **dbt**, **Airflow**, and **Snowflake** in a cohesive pipeline.\n- Dockerized development for portability.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frizkipragustono%2Fecommerce_elt_project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frizkipragustono%2Fecommerce_elt_project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frizkipragustono%2Fecommerce_elt_project/lists"}