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It covers the full lifecycle of a data project, from initial planning and data extraction to automated deployment and final visualization.\r\n1. Project Architecture\r\nThis project follows an automated ETL (Extract, Transform, Load) pipeline. Data is extracted from an API, cleaned and processed, and then loaded into a database. The entire process is containerized and automated with CI/CD.\r\n\r\nThe key components are:\r\n\r\nData Source: A public API.\r\n\r\nETL Pipeline: Python scripts run inside a Docker container.\r\n\r\nDatabase: A SQLite database to store the final, cleaned data.\r\n\r\nDashboards: Visualizations created in Power BI and Tableau.\r\n\r\n2. Technical Stack\r\nThis project exercises a wide range of skills and tools, including:\r\n\r\nProject Management: Jira/Agile\r\n\r\nVersion Control: Git, GitHub\r\n\r\nData Extraction \u0026 Processing: Python, Pandas, APIs\r\n\r\nData Storage: Excel, SQL (SQLite)\r\n\r\nAutomation \u0026 DevOps: Docker, CI/CD (GitHub Actions)\r\n\r\nBusiness Intelligence: Power BI, Tableau\r\n\r\nQuality Assurance: Testing (Pytest)\r\n\r\nDocumentation: Markdown\r\n\r\n3. Setup Instructions\r\nTo run this project, you will need the following tools installed:\r\n\r\nPython 3.9 or higher\r\n\r\nDocker Desktop\r\n\r\nGit\r\n\r\nStep-by-Step Setup:\r\n\r\nClone the repository: git clone [Your-GitHub-Repository-URL]\r\n\r\nNavigate to the project directory: cd [Your-Project-Folder]\r\n\r\nBuild the Docker image: docker build -t etl-pipeline-image .\r\n\r\nRun the ETL pipeline: docker run --rm -v \"$(pwd)/data:/app/data\" etl-pipeline-image\r\n\r\nThis command will execute the ETL pipeline, creating a data folder with raw.json, transformed.csv, and project_database.db files.\r\n\r\n4. Key Findings \u0026 Insights\r\n(This section is a placeholder for you to fill in based on your analysis)\r\n\r\nInsight 1: Briefly describe a key finding from your Power BI dashboard. For example, \"The data reveals that there is a high number of posts associated with a single user, suggesting that the data is not evenly distributed.\"\r\n\r\nInsight 2: Summarize another key finding from your Tableau dashboard. For example, \"The post titles are often short, which indicates the data is well-suited for text analysis and natural language processing in future work.\"\r\n\r\n5. Conclusion\r\nThis project demonstrates the complete end-to-end process of building a data analytics pipeline. The use of modern DevOps practices ensures the project is not only functional but also scalable and repeatable.\r\n\r\n6. Acknowledgments\r\nAPI: Data for this project was extracted from the JSONPlaceholder API.\r\n\r\nGuidance: This project was built with step-by-step guidance.\r\n\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flakshmi-aluru-dev%2Fdata-analytics-capstone-1","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flakshmi-aluru-dev%2Fdata-analytics-capstone-1","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flakshmi-aluru-dev%2Fdata-analytics-capstone-1/lists"}