Build a cloud-based data pipeline that collects, processes, and analyzes real-time sales data using AWS services such as Kinesis, S3, Glue, and Redshift. The project focuses on data ingestion, ETL processing, and dashboard-ready analytics to help businesses monitor sales performance, customer behavior, and transaction trends in real time using scalable AWS architecture.

Develop an end-to-end ETL solution that extracts data from multiple sources, transforms it using AWS Glue, and loads it into Amazon Redshift for reporting and analytics. This AWS Data Engineer project helps learners understand data warehousing concepts, data transformation techniques, query optimization, and large-scale cloud data management workflows.

Create a Big Data processing application using Apache Spark on AWS EMR to analyze large datasets efficiently. The project involves handling structured and unstructured data, performing distributed data processing, and generating insights through Spark transformations and analytics. Learners gain practical exposure to scalable Big Data solutions used in modern data engineering environments.