Examples of Apache Flink® v2.1 applications showcasing the DataStream API, Table API in Java and Python, and Flink SQL, featuring AWS, GitHub, Terraform, Streamlit, and Apache Iceberg.

Automation framework to catalog AWS data sources using Glue

Smart City Realtime Data Engineering Project

Tool to migrate Delta Lake tables to Apache Iceberg using AWS Glue and S3

This project repo 📺 offers a robust solution meticulously crafted to efficiently manage, process, and analyze YouTube video data leveraging the power of AWS services. Whether you're diving into structured statistics or exploring the nuances of trending key metrics, this pipeline is engineered to handle it all with finesse.

It is a project build using ETL(Extract, Transform, Load) pipeline using Spotify API on AWS.

Engaging, interactive visualizations crafted with Streamlit, seamlessly powered by Apache Flink in batch mode to reveal deep insights from data.

Prototype of AWS data lake reference implementation written in Python and Spark: https://aws.amazon.com/solutions/implementations/data-lake-solution/

Creating an audit table for a DynamoDB table using CloudTrail, Kinesis Data Stream, Lambda, S3, Glue and Athena and CloudFormation

Working with Glue Data Catalog and Running the Glue Crawler On Demand

Unveiling job market trends with Scrapy and AWS

This project demonstrates how to use Terraform to enable Tableflow in Kafka to generate and store the Iceberg Table files in an AWS S3 bucket. Then, configure Snowflake to read the Iceberg Tables using AWS Glue Data Catalog and the AWS S3 bucket where Tableflow produces the Iceberg files.

Example using the Iceberg register_table command with AWS Glue and Glue Data Catalog

Developed an ETL pipeline for real-time ingestion of stock market data from the stock-market-data-manage.onrender.com API. Engineered the system to store data in Parquet format for optimized query processing and incorporated data quality checks to ensure accuracy prior to visualization.

This project creates a scalable data pipeline to analyze YouTube data from Kaggle using AWS services: S3, Glue, Lambda, Athena, and QuickSight. It processes raw JSON and CSV files into cleansed, partitioned datasets, integrates them with ETL workflows, and catalogs data for querying. Final insights are visualized in QuickSight dashboards.

This project showcases a complete data engineering pipeline on AWS, following best practices in data ingestion, transformation, and analytics — ready for real-world production use or integration with BI tools such as QuickSight or Power BI.

This project uses Terraform and GitHub Actions to build and validate a data infrastructure on AWS. The CI pipeline automates code verification for provisioning an S3 bucket and a Glue catalog, establishing a solid, version-controlled foundation for data engineering projects.

AWS Glue ETL Pipeline automates data extraction, transformation, and loading using AWS Glue and S3. It ingests raw data from an S3 source bucket, processes it via Glue ETL jobs, and stores the transformed data in a destination bucket. This solution enables efficient serverless data processing.