Week 11 Worklog

Week 11 Objectives:

  • Shift focus to data platform, analytics, and query workloads.
  • Build understanding of BI and advanced PostgreSQL use cases.

Tasks to be carried out this week:

DayTaskStart DateCompletion DateReference MaterialStatus
1AWS: Data Lake Fundamentals06/26/202606/26/2026Learned data lake principles on AWSCompleted
2AWS: Build Data Lake06/27/202606/27/2026Practiced implementation with sample dataCompleted
3AWS: Analytics Overview & QuickSight06/28/202606/28/2026Explored analytics and BI workflowsCompleted
4AWS: Data Eng Immersion Day06/29/202606/29/2026Completed data engineering workshop scenariosCompleted
5AWS: Athena Analytics06/30/202606/30/2026Queried data with serverless analyticsCompleted
6AWS: Adv PostgreSQL Part 107/01/202607/01/2026Advanced PostgreSQL features and tuningCompleted
7AWS: Adv PostgreSQL Part 207/02/202607/02/2026Continued advanced database patternsCompleted

Daily Details

Day 1 - AWS: Data Lake Fundamentals

  • Studied the fundamentals of data lake architecture on AWS.

Day 1 screenshot

Day 2 - AWS: Build Data Lake

  • Built a data lake with sample data and an initial storage structure.

Day 2 screenshot

Day 3 - AWS: Analytics Overview & QuickSight

  • Explored analytics workflows and BI dashboards with QuickSight.

Day 3 screenshot

Day 4 - AWS: Data Eng Immersion Day

  • Worked through scenarios designed for data engineering practice.

Day 4 screenshot

Day 5 - AWS: Athena Analytics

  • Queried data with Athena for quick serverless analysis.

Day 5 screenshot

Day 6 - AWS: Adv PostgreSQL Part 1

  • Practiced advanced PostgreSQL features and basic tuning ideas.

Day 6 screenshot

Day 7 - AWS: Adv PostgreSQL Part 2

  • Continued the advanced PostgreSQL track and completed the final exercises.

Day 7 screenshot

Week 11 Achievements:

  • Established a stronger end-to-end view of data and analytics on AWS.
  • Connected Athena, QuickSight, and PostgreSQL usage patterns effectively.
  • Improved readiness for larger-scale data workload design.