An example CI/CD pipeline using GitHub Actions for doing continuous deployment of AWS Glue jobs built on PySpark and Jupyter Notebooks.

This project demonstrates building a scalable cloud-based data pipeline using AWS services like S3, Glue, Athena, and QuickSight. It processes, analyzes, and visualizes datasets, with the Spotify dataset as an example, enabling actionable insights. The architecture is adaptable for diverse industries, empowering data-driven decision-making.

End-to-end AWS data pipeline: S3 → Lambda → Glue → Athena. Includes IaC, schemas, and example queries.

This project showcases an end-to-end ETL pipeline using AWS services for processing and analyzing the Spotify dataset. The architecture includes S3 for data storage, AWS Glue for ETL processing, Athena for querying, and QuickSight for visualization. It serves as a example of building scalable, cloud based data pipelines for realworld analysis

A full‑featured AWS data engineering template designed to accelerate project setup with ready‑made patterns for S3 ingestion, Glue ETL, Lambda orchestration, and Redshift loading. Includes flow orchestration, IAM‑role authentication, modular ETL code, infrastructure scaffolding, and example end‑to‑end pipeline.

A step-by-step guide to getting started with AWS Glue — Amazon's fully managed extract, transform, and load (ETL) service. This tutorial walks through the core concepts, hands-on examples, and sample PySpark scripts you can use in your own ETL pipelines.

This project demonstrates building a scalable cloud-based data pipeline using AWS services like S3, Glue, Athena, and QuickSight. It processes, analyzes, and visualizes datasets, with the Spotify dataset as an example, enabling actionable insights. The architecture is adaptable for diverse industries, empowering data-driven decision-making.

This project demonstrates building a scalable cloud-based data pipeline using AWS services like S3, Glue, Athena, and QuickSight. It processes, analyzes, and visualizes datasets, with the Spotify dataset as an example, enabling actionable insights. The architecture is adaptable for diverse industries, empowering data-driven decision-making.