I’m sure not everyone came here to hear tech book recommendations, but I will add another vote for Designing Data-Intensive Applications by Martin Kleppman. It’s one of the best tech reference books I’ve ever read. It manages to explain SO much while requiring so little prior knowledge from readers.
Another book that is relatively new that I loved was Designing Machine Learning Systems by Chip Huyen. I worked in productionizing ML systems for 3 years and this book equips you with exactly what you need to do so. It does a great job of explaining the whole ML modeling pipeline and some of the commonly overlooked nuances that can cause your models to fail spectacularly in production. I will be referencing this book for years to come.
I'd suggest searching HN for other reviews of the book since it comes up pretty frequently in these threads, but personally I liked that it covered a lot of ground quickly and fairly deeply, and the content remained engaging for me throughout. Some specific things I liked about the book were its coverage of SQL isolation levels and how they work, different ways to coordinate separate systems when you want atomicity (like 2pc and queue-based synchronization), failure modes to keep in mind when dealing with distributed systems (like unbounded network delay and split-brain situations), and references to a whole bunch of production-ready software that solve distributed computing challenges in various ways depending on the trade-offs you're looking for.
Overall I felt better equipped to make intelligent decisions about systems design after reading it.
Another book that is relatively new that I loved was Designing Machine Learning Systems by Chip Huyen. I worked in productionizing ML systems for 3 years and this book equips you with exactly what you need to do so. It does a great job of explaining the whole ML modeling pipeline and some of the commonly overlooked nuances that can cause your models to fail spectacularly in production. I will be referencing this book for years to come.