This project demonstrates how to build a data engineering pipeline using AWS services to analyze YouTube data. The pipeline includes data ingestion, transformation, visualization, and automation. The goal is to provide insights into YouTube metrics and trends through interactive dashboards.
- Data Ingestion: Collect data from various YouTube sources using AWS services.
- Data Transformation: ETL processes to clean and preprocess the data.
- Data Visualization: Interactive dashboards and visualizations created with AWS QuickSight.
- Automation: Scheduled pipelines for continuous data updates and analysis.
- Data Ingestion: Upload your data sources to the specified S3 bucket.
- Data Transformation: The ETL process will be executed to clean and preprocess the data. Monitor AWS Glue jobs for status and logs.
- Data Visualization: Access AWS QuickSight to view and interact with the dashboards.
- AWS Athena: Query service for analyzing data.
- AWS Lambda: Serverless compute for data processing tasks.
- AWS Glue: ETL service for data preparation.
- AWS S3: Storage service for data storage.
- AWS QuickSight: Data visualization service for creating dashboards.