Week 10 Worklog

Week 10 Objectives:

  • Master data visualization with Amazon QuickSight.
  • Deep dive into Data Engineering on AWS.
  • Build serverless data lakes and real-time data processing pipelines.

Tasks to be carried out this week:

Day Task Start Date Completion Date References
2 - Learn Amazon QuickSight for data visualization
- Master Data Engineering on AWS
- Understand serverless data lake architecture
- Practice:
  + Getting Started with QuickSight:
   - Understand QuickSight concepts (Data source, Dataset, Analysis, Visual, Dashboard)
   - Prepare data for analysis
   - Build interactive dashboards
   - Implement dashboard improvements and interactivity
  + Data Engineering Immersion Day:
   - Implement real-time clickstream anomaly detection with Apache Flink
   - Ingest data using AWS DMS
   - Transform data with AWS Glue
   - Query and visualize data with Athena and QuickSight
   - Automate Data Lake workflows
   - Prepare data with Glue DataBrew		 11/10/2025 11/10/2025 Getting Started with Quick Sight
Dashboard visualization

Data Engineering Immersion Day
Serverless data lake
3 - Master Amazon Athena for interactive analytics
- Deep dive into AWS RDS PostgreSQL management
- Understand serverless SQL querying and database optimization
- Practice:
  + Amazon Athena Workshop:
   - Understand Athena architecture and serverless analytics
   - Run interactive SQL queries on S3 data
   - Implement Athena for Apache Spark
   - Configure Athena Federation for external data sources
   - Optimize query performance and costs
  + AWS RDS PostgreSQL Foundation:
   - Perform database upgrades and maintenance
   - Implement performance monitoring and optimization
   - Configure backup and recovery strategies
   - Implement database scalability and read replicas
   - Manage parameter groups for tuning
   - Configure High Availability (Multi-AZ) deployments		 11/11/2025 11/11/2025 Amazon Athena Workshop
Serverless interactive analytics

AWS RDS PostgreSQL Foundation
Database management
4 - Learn AWS RDS PostgreSQL for application development
- Master Amazon SageMaker for Machine Learning
- Understand ML workflow from Feature Engineering to Deployment
- Practice:
  + AWS RDS PostgreSQL for Developers:
   - Connect to RDS PostgreSQL from applications
   - Implement database connection code (Python/Node.js)
   - Deploy Node.js application with RDS backend
   - Manage database credentials and security
  + SageMaker Immersion Day:
   - Understand SageMaker architecture and capabilities
   - Perform Feature Engineering on datasets
   - Train, Tune, and Deploy XGBoost models
   - Implement Lift-and-Shift for ML workloads
   - Monitor and debug ML models		 11/12/2025 11/12/2025 AWS RDS PostgreSQL for Developers
Application integration

SageMaker Immersion Day
Machine Learning workflow
5 - New lab will be coming soon 11/13/2025 11/13/2025
6 - New lab will be coming soon 11/14/2025 11/14/2025

Week 10 Achievements:

  • Amazon QuickSight Visualization Mastery:

    • Mastered QuickSight core concepts and architecture
    • Successfully connected to various data sources
    • Created and managed datasets for analysis
    • Built interactive and insightful dashboards
    • Implemented visual types and dashboard interactivity
    • Applied best practices for data visualization

  • Amazon Athena Analytics Mastery:

    • Mastered serverless interactive analytics with Athena
    • Successfully ran complex SQL queries on S3 data lakes
    • Implemented Athena for Apache Spark workloads
    • Configured Athena Federation to query external data sources
    • Optimized query performance and managed costs effectively
    • Applied best practices for serverless data analysis

  • RDS PostgreSQL Development Mastery:

    • Mastered application development with RDS PostgreSQL
    • Successfully connected applications to managed databases
    • Deployed scalable Node.js applications with RDS
    • Implemented secure database connection patterns

  • Amazon SageMaker ML Expertise:

    • Mastered end-to-end ML workflow on SageMaker
    • Successfully performed Feature Engineering
    • Trained, tuned, and deployed XGBoost models
    • Implemented model deployment best practices

  • New worklog will be coming soon