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End-to-End AWS Data Engineering Project: Banking Fraud Detection Pipeline (S3, Glue, Lambda, Redshift, Step Functions)
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.
What You Will Build
You will develop a production-ready data pipeline using:
- Amazon S3 for data lake storage
- AWS Lambda for event-driven validation
- AWS Glue for ETL processing using PySpark
- Amazon Athena for querying data in the data lake
- Amazon Redshift for data warehousing and analytics
- AWS Step Functions for orchestration and automation
What Makes This Course Unique?
Unlike theoretical courses, this course focuses on:
Real-world banking fraud detection use case
End-to-end pipeline implementation
Hands-on coding with PySpark
Production-level architecture design
Industry best practices
Project Architecture
You will build a modern data pipeline:
Raw Data (CSV in S3)
→ Lambda Validation
→ Glue ETL Jobs (Bronze → Silver → Gold Layers)
→ Athena Queries
→ Redshift Analytics
→ Step Functions Orchestration
Skills You Will Gain
- Designing scalable data lake architectures
- Building ETL pipelines using PySpark in AWS Glue
- Implementing data cleansing and transformation techniques
- Creating fraud detection logic and scoring models
- Optimizing performance using partitioning strategies
- Writing real-world SQL queries for analytics
- Automating workflows using serverless architecture
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.
Start Your Data Engineering Journey Today!
If you want to learn AWS Data Engineering by building a real-world project, this course is perfect for you.
Enroll now and take your first step toward becoming a Data Engineering expert on AWS
Who this course is for:
- Aspiring Data Engineers who want to build real-world, production-level projects
- Beginners looking to start a career in AWS Data Engineering
- Working professionals who want to transition into Data Engineering roles
- AWS developers who want hands-on experience with services like AWS Glue, Amazon Redshift, and AWS Step Functions
- Data analysts who want to upgrade their skills to data pipeline development
- Anyone preparing for Data Engineering interviews with real-time project experience
- Professionals interested in building end-to-end ETL pipelines on AWS Cloud
- Students who prefer hands-on learning instead of only theoretical concepts
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