I'm increasingly seeing AI generated codebases around me.

The codebase is usually broken in very fundamental ways. These are the mistakes someone would make if they lacked a fundamental understanding of how things work, and how they connect together.

The solution is simple - study. And study well.

Classics

• Designing Data Intensive Applications by Martin Kleppmann
  • THE book for introduction to data systems.
  • Provides a great tour of the data storage landscape - systems, tradeoffs etc.
  • It doesn't have much depth. But it is a great book to find out what you don't know, and didn't know that you need to know.

PostgreSQL

I like postgres.

• The Art of PostgreSQL

  • Not really a classic, but still an incredible book
  • Like the name says, it leads to beautiful queries

• PostgreSQL 14 Internals

  • Goes through an overview, and immediately dives in
  • The knowledge is highly transferable, and leads to better engineering in general

Data modelling and governance

• The Enterprise Data Catalog by Ole Olesen-Bagneux (OReilly)
• The Data Warehouse Toolkit by Ralph Kimball
  • One of the classics. It is an essential reads. The world has moved on in some regard, but the book's knowledge still has staying power.

Database Engineering

• Data Structures for Data-Intensive Applications - Tradeoffs and Guidelines. By Manos, Stratos and Dennis.

Kafka

• Effective Kafka: A Hands-On Guide to Building Robust and Scalable Event-Driven Applications with Code Examples in Java
  • A good book to skim through, for kafka.

Python SWE

• Cosmic python
  • https://www.cosmicpython.com/book/preface.html
  • Python architecture patterns, DDD, EDA etc