Week 5 Worklog

Week 5 Objectives:

  • Containerization: Master Docker, Docker Compose, and deploy containerized applications on AWS ECS.
  • Data Lake Architecture: Build a complete Data Lake with S3, AWS Glue (Catalog), and Athena (Analytics).
  • NoSQL Deep Dive: Hands-on deep dive with Amazon DynamoDB (Partition/Sort Key, GSI/LSI).
  • DB Comparison: Detailed comparison of architecture and performance between Amazon RDS and Amazon Aurora.
  • Analytics Optimization: Optimize costs and performance for big data queries.

Tasks to be carried out this week:

Day Task Start Date Completion Date Reference Material
1 - Explore docker, docker-compose
- Compare Amazon RDS vs Amazon Aurora
06/10/2025 06/10/2025 https://github.com/tuanvu250/AWS-FCJ/tree/main/bonus/docker
https://docs.docker.com/get-started/
https://000054.awsstudygroup.com/vi/
2 - Practice Deploying Applications on Docker with AWS
- Practice Deploying Applications on Amazon Elastic Container Service
- Practice DataLake on AWS
  + Collect and store data
  + Create Data Catalog (Amazon Glue)
  + Analyze and visualize (Amazon Athena)
07/10/2025 07/10/2025 https://000015.awsstudygroup.com/vi/
https://000016.awsstudygroup.com/vi/
https://000005.awsstudygroup.com/vi/
https://000035.awsstudygroup.com/vi/
3 - Practice Amazon DynamoDB Immersion Day 08/10/2025 08/10/2025 https://000039.awsstudygroup.com/vi/
4 - Practice Amazon DynamoDB Immersion Day 09/10/2025 09/10/2025 https://000039.awsstudygroup.com/vi/
5 - Practice Analyzing Cost and Performance with AWS Glue and Amazon Athena
- Practice Working with Amazon DynamoDB
10/10/2025 10/10/2025 https://000040.awsstudygroup.com/vi/
https://000060.awsstudygroup.com/vi/
6 - Practice Building Datalake with your own data 11/10/2025 11/10/2025 https://000070.awsstudygroup.com/vi/

Week 5 Achievements:

  • Container Ops: Successfully packaged and managed Docker images on ECR and operated ECS clusters.
  • Data Lake Live: Serverless data collection, storage, and analysis system is operational.
  • DynamoDB Pro: Desinged efficient NoSQL tables, understood data models and performance metrics.
  • DB Architecture: Clear analysis of pros/cons of RDS vs Aurora for decision making.
  • Cost Effective: Applied partition and query optimization techniques to reduce data analysis costs.