This project focuses on analyzing Spotify data to identify infection risk factors. It leverages AWS services for data ingestion, transformation, storage, and analysis, and utilizes Power BI for data visualization.

1. Data Acquisition
2. Data Ingestion
3. Data Transformation
4. Data Cataloging
5. Data Querying
6. Data Warehousing
7. Model Development
8. Model Evaluation
9. Data Visualization
10. Automation and Scheduling
11. Security and Compliance
12. Scaling and Optimization
13. Deployment
14. Testing and Quality Assurance
15. Documentation
16. Conclusion and Future Work
17. License and Copyright
18. Acknowledgments

• Collect historical Spotify data from various sources.
• Store the raw data in an AWS S3 bucket.

• Use AWS Glue Crawlers to automatically discover and catalog metadata about the raw data in S3.
• Create a Glue Data Catalog to manage the metadata.

• Develop AWS Glue ETL (Extract, Transform, Load) jobs to clean, transform, and enrich the data.
• Convert the raw data into a format suitable for analysis.
• Handle missing values, data types, and schema changes.

• Utilize AWS Glue Data Catalog to track data lineage and transformations.

• Use AWS Athena to query data stored in S3 using standard SQL.
• Create views and materialized views for frequently used queries.

• Load the processed data into AWS Redshift, a powerful data warehousing solution.
• Design an optimized Redshift schema for analytical queries.

• Develop machine learning models for Spotify infection risk prediction.
• Utilize Jupyter Notebooks on AWS SageMaker for model development and training.

• Assess model performance using appropriate metrics (e.g., accuracy, precision, recall).
• Fine-tune models for better accuracy.

• Connect Power BI to AWS Redshift for real-time data visualization.
• Create interactive dashboards and reports to visualize infection risk factors and trends.

• Set up AWS Lambda functions or Step Functions to automate ETL jobs, model training, and data updates.
• Schedule regular data updates and model retraining.

• Implement AWS IAM (Identity and Access Management) to control access to AWS resources.
• Ensure data encryption and compliance with security best practices.

• Monitor Redshift performance and scale resources as needed.
• Optimize ETL processes for efficiency and cost-effectiveness.

• Deploy the machine learning model as an API using AWS Lambda or AWS SageMaker endpoints for real-time predictions.

• Implement unit tests and integration tests for ETL pipelines and APIs.
• Ensure data quality and reliability.

• Document the project, including data sources, ETL processes, model details, and API endpoints.
• Include clear instructions for setting up and running the project.

• Summarize project outcomes and findings.
• Discuss potential future enhancements or research directions.

• Specify the project's license and copyright information.

• Give credit to any external libraries, datasets, or contributors.