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.

A practical summary of DDIA chapter 8 covering partial failures, unreliable networks, clock assumptions, and reliability patterns for production systems.

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

An easy-to-understand walkthrough of the hardest chapter in Designing Data-Intensive Applications — how distributed systems agree on what's true.

A friendly walkthrough of batch processing in Designing Data-Intensive Applications — Unix pipes, MapReduce, and the rise of Apache Spark.

Notes on stream processing in Designing Data-Intensive Applications — event streams, message brokers, CDC, event sourcing, time semantics, stream joins, and fault tolerance.

Notes on the final DDIA chapter — integrating batch and stream processing, unbundling databases, end-to-end correctness, and the ethics of data-intensive systems.