Hands-on Labs & Final Challenge

Scenario:

SecureCart’s engineering team must implement a high-performance data ingestion and transformation solution.

Final Study Group Challenges:

✅ Scenario 1: Implement a Scalable Data Ingestion Pipeline Using Amazon Kinesis ✅ Scenario 2: Transform CSV Data into Parquet Format Using AWS Glue ✅ Scenario 3: Optimize Data Transfer Using AWS Storage Gateway & Snowball ✅ Scenario 4: Build a Data Lake Using AWS Lake Formation ✅ Scenario 5: Visualize Business Performance Using Amazon QuickSight

🔹 Outcome: Learners demonstrate AWS best practices for scalable and efficient data processing.

---

📚 Recommended Study Resources

✅ AWS Well-Architected Framework – Data Analytics & Performance Pillars ✅ AWS Glue, EMR, and Lambda for ETL & Data Processing ✅ Amazon Kinesis & AWS DataSync for Data Ingestion ✅ AWS Lake Formation for Secure Data Lakes ✅ Amazon Athena, QuickSight & Redshift Spectrum for Data Analytics