What you’ll learn
Build a complete end-to-end AWS Data Engineering pipeline from scratch using real-world banking data
Design and implement a multi-layer architecture (Raw → Bronze → Silver → Gold)
Ingest and process data from Amazon S3 using scalable data lake principles
Develop serverless data workflows using AWS Lambda for event-driven processing
Create and optimize ETL pipelines using AWS Glue with PySpark
Apply advanced data transformations including cleansing, standardization, and feature engineering
Implement partitioning strategies to improve performance and reduce query cost
Build fraud detection logic using real-time business rules and scoring techniques
Query large datasets efficiently using Amazon Athena
Load and analyze data in Amazon Redshift for analytics and reporting
Orchestrate end-to-end workflows using AWS Step Functions
Automate pipelines using event-driven architecture (S3 → Lambda → Step Functions)
Write real-world SQL queries for fraud analysis and business insights
Understand data engineering best practices used in production environments
Build a portfolio-ready project to crack Data Engineering interviews
AWS Data Engineering Project: Banking Fraud Detection Pipeline
Are you ready to build a real-world AWS Data Engineering project that can boost your career and make your resume stand out?
In this hands-on course, you will design and implement a complete end-to-end data pipeline for detecting fraudulent banking transactions using modern AWS services.
Why This Course?
This project is designed to help you:
Crack Data Engineering interviews
Build a strong portfolio project
Gain real-time industry experience
Transition into high-paying AWS roles
Hands-On Learning Approach
This is a project-based course, where you will:
Write real code
Build real pipelines
Solve real problems
By the end of this course, you will have a complete AWS Data Engineering project that you can showcase in interviews and on your resume.