Designing Data-Intensive Applications

Notes on Chapter 2 of Designing Data-Intensive Applications — relational vs document models, query languages, and graph databases.

How databases store and retrieve data — from log-structured engines (LSM-trees) to page-oriented engines (B-trees), plus indexing strategies, OLTP vs OLAP, and column-oriented storage.

Livestream walkthrough of DDIA Chapter 3 — visual explanations of row vs column storage, B-trees vs LSM trees, and why OLTP and OLAP databases are engineered differently.

Summary of Kleppmann's chapter on data encoding formats, schema evolution, and compatibility in data-intensive systems.

Notes from a live stream walking through DDIA Chapter 5 (Replication): single-leader setup, primary and read replicas, scaling reads, and high availability across availability zones.

Summary of Kleppmann's chapter on replication in data systems: why replicate, single-leader, multi-leader, and leaderless replication, plus consistency trade-offs.

Video walkthrough of DDIA Chapter 6 on partitioning (sharding): diagrams and explanations of key-range vs hash partitioning, secondary indexes, rebalancing, and request routing.

Summary of Kleppmann's chapter on partitioning (sharding): splitting data across nodes, partition strategies, secondary indexes, rebalancing, and request routing.

Live stream walkthrough of DDIA Chapter 7: why transactions matter, ACID, isolation levels and naming pitfalls, Postgres MVCC and vacuum, and MySQL/InnoDB undo logs.

Summary of Kleppmann's chapter on transactions: ACID, isolation anomalies, snapshot isolation, two-phase locking, and serializable snapshot isolation.

In-progress notes on DDIA Chapter 8: partial failures, unreliable networks, time assumptions, and defensive patterns for distributed systems.