# 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**


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