Week 6 Worklog

Week 6 Objectives

  • Understand core database concepts: tables, schemas, primary keys, foreign keys, indexes, partitioning, query execution plans, database logs, and buffer/cache behavior.
  • Learn the differences between relational and non-relational databases, and where each fits.
  • Distinguish between OLTP and OLAP workloads and how design choices impact performance.
  • Explore AWS database services: Amazon RDS, Amazon Aurora, Amazon ElastiCache, and Amazon Redshift, including their ideal use cases and architectural differences.

Tasks to be carried out this week

DayTaskOn-site?Date
1- Study database fundamentals
- Understand tables, rows, and schema design
- Learn primary key vs foreign key and why normalization exists		10/13/2025
2- Explore indexes, how they work, and why they speed up queries
- Study partitioning, query planners, and execution plans
- Learn about transaction logs, WAL, and buffer/cache behavior		10/14/2025
3- Compare relational vs non-relational databases
- Understand CAP considerations and consistency trade-offs
- Learn where document, key-value, graph, and wide-column stores fit		10/15/2025
4- Study OLTP (transactional) vs OLAP (analytical) systems
- Understand how data modeling differs for each
- Learn why analytical systems require columnar storage		10/16/2025
5- Introduction to Amazon RDS and managed relational engines
- Explore snapshots, Multi-AZ, backups, storage autoscaling		10/17/2025
6- Deep dive into Amazon Aurora, its distributed storage layer
- Learn high-performance read scaling and failover design - Explore Amazon ElastiCache (Redis/Memcached) for caching, sessions, and low-latency workloads
- Study Amazon Redshift for OLAP and analytics queries at scale		10/18/2025

Week 6 Achievements

  • Mastered foundational database concepts:

    • Understood how primary/foreign keys enforce relationships.
    • Learned how indexes affect performance and why poorly designed ones make databases cry.
    • Analyzed query execution plans and saw how optimizers decide access paths.
    • Understood logs, WAL, and buffer management as part of durability and performance.

  • Differentiated database types and use cases:

    • Evaluated relational models for strong consistency and structured data.
    • Explored non-relational models for flexible schemas and distributed scaling.
    • Understood when to choose OLTP vs OLAP systems and why mixing them is usually a disaster.

  • Learned AWS managed database offerings:

    • Used Amazon RDS for simplified relational engine management with backups and Multi-AZ.
    • Understood why Aurora achieves higher throughput using a shared storage layer.
    • Explored ElastiCache as an in-memory system for acceleration and state management.
    • Used Amazon Redshift as a columnar OLAP warehouse designed for complex analytical queries.