Architecture of the Data pipeline and Web-Scale Analytics Platform using AWS Common Crawl + PySpark

• This is a ongoing project to build a data pipeline that ingests data from the source and stores into data warehouse
• An end-to-end big data pipeline that processes the AWS Open Data Common Crawl corpus using PySpark and Spark SQL to build a scalable data warehouse for advanced analytics
• More information from the diagram and concept can provide detail understanding of the project. This project structure will evolve over time as different factors are required to be conisidered.

Key Highlights

• Distributed ETL with PySpark on AWS
• Large-scale web data cleaning & transformation
• Spark SQL–based warehousing (Parquet / Delta / Redshift)
• Data quality validation & schema enforcement
• ML-ready datasets for segmentation, trend analysis, and predictive modeling
• Scalable architecture for deep learning & AI applications

Project Architecture

Project Template

├── .github/ │ ├── workflows/ │ ├── download_aws_data.yml ├── dags/ ├── docker/ │ ├── airflow.sh │ ├── docker-compose.yml │ ├── instructions.txt ├── docs/ ├── notebooks/ ├── scripts/ │ ├── download_aws_crawl_data/ ├── src │ ├── examples/ │ ├── my_app/ ├── .gitignore ├── LICENSE ├── README.md ├── requirements.txt