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.
- Data Acquisition
- Data Ingestion
- Data Transformation
- Data Cataloging
- Data Querying
- Data Warehousing
- Model Development
- Model Evaluation
- Data Visualization
- Automation and Scheduling
- Security and Compliance
- Scaling and Optimization
- Deployment
- Testing and Quality Assurance
- Documentation
- Conclusion and Future Work
- License and Copyright
- 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.