| Day | Task | Start Date | Completion Date | Reference Material |
|---|---|---|---|---|
| 2 | - LMR: Build and Deploy a Global Serverless Application with Amazon DynamoDB Practice: + Module 1: Deploy the backend resources + Module 2: Explore Global Tables + Module 3: Interact with the Globalflix Interface - Global Tables Discussion Topics - LEDA: Build a Serverless Event Driven Architecture with DynamoDB - Practice: + Lab 1: Connect the pipeline + Lab 2: Ensure fault tolerance and exactly once processing + Cleanup resource | 11/10/2025 | 11/10/2025 | https://000039.awsstudygroup.com/ |
| 3 | - LGME: Modeling Game Player Data with Amazon DynamoDB - Practice: + Plan data model + Core usage: user profiles and games + Find open games + Join and close games + View past games + Summary & Cleanup - LDC: Design Challenges | 11/11/2025 | 11/11/2025 | https://000039.awsstudygroup.com/ |
| 4 | - Cost and performance analysis with AWS Glue and Amazon Athena - Practice: + Preparing the database + Building a database + Database Check + Data in the Table + Cost + Tagging and Cost Allocation + Usage + Cleanup resource | 11/12/2025 | 11/12/2025 | https://000040.awsstudygroup.com/ |
| 5 | - Work with Amazon DynamoDB - Practice: + Manage using AWS Management Console + Use AWS CloudShell + Configure AWS CLI + Getting started with Python and DynamoDB - Clean up resource | 11/13/2025 | 11/13/2025 | https://000060.awsstudygroup.com/ |
| 6 | - Building a Datalake with Your Data - Practice: + Preparing Data + Data Ingestion with AWS Glue + Query with Athena + Visualization with QuickSight + Resource Cleanup | 11/14/2025 | 11/14/2025 | https://000070.awsstudygroup.com/ |
Successfully built and deployed a global serverless application using DynamoDB Global Tables, gaining hands-on experience with multi-Region architecture and event-driven pipelines with fault tolerance and exactly-once processing.
Strengthened DynamoDB data-modeling skills through game player data modeling, covering user profiles, game sessions, open/closed games, and historical queries.
Gained deeper experience with AWS Glue & Athena by performing database preparation, ETL operations, querying, cost analysis, tagging, and cost allocation for analytics workloads.
Improved DynamoDB operational skills using Management Console, CloudShell, AWS CLI, and Python SDK (boto3).
Built a full data lake pipeline end-to-end: data preparation, Glue ingestion, Athena query optimization, and QuickSight visualization.