Task Statement 3.5: Determine High-Performing Data Ingestion and Transformation Solutions

This study group focuses on designing scalable and high-performing data ingestion and transformation solutions using AWS services for real-time and batch processing.

We will follow SecureCart, an e-commerce platform, as they optimize data ingestion, transformation, and visualization to support real-time analytics, data lakes, and machine learning workloads.

---

📅 Study Group Agenda

Week	Topic	Key AWS Services
Week 1	Data Ingestion Strategies & Patterns	Amazon Kinesis, AWS DataSync, AWS Storage Gateway
Week 2	Data Transformation & ETL Pipelines	AWS Glue, AWS Lambda, AWS EMR
Week 3	Secure & Scalable Data Transfer	AWS Transfer Family, AWS Snowball, AWS Direct Connect
Week 4	Building & Managing Data Lakes	AWS Lake Formation, Amazon S3, AWS Glue Catalog
Week 5	Data Visualization & Analytics	Amazon Athena, AWS QuickSight, Redshift Spectrum
Week 6	Hands-on Labs & Final Challenge	Implementing a High-Performance AWS Data Pipeline

Final Study Group Summary

Week	Focus Area	Outcome
Week 1	Data Ingestion Strategies	Implement Streaming & Batch Ingestion
Week 2	Data Transformation & ETL	Process & Optimize Data for Analytics
Week 3	Secure Data Transfer	Securely Transfer & Migrate Large Data Sets
Week 4	Data Lake Architecture	Build & Manage Scalable Data Lakes
Week 5	Data Analytics & Visualization	Create Real-Time Dashboards & Insights
Week 6	Hands-on Scenarios	Implement a Scalable AWS Data Pipeline

---

🚀 Next Steps

Would you like: ✅ Terraform automation scripts for AWS data pipelines? ✅ Scenario-based quizzes for each study group session? ✅ Instructor-led deep-dive discussions?