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4 books, 73 chapters. The ideas behind the system design and database questions, condensed so you can revise a chapter in one sitting.
The big ideas behind reliable, scalable and maintainable data systems: storage engines, replication, partitioning, transactions, consensus, and batch and stream processing.
How storage engines and distributed databases work under the hood: B-trees, LSM trees, recovery, replication, failure detection and consensus.
Building applications on foundation models: evaluation, prompt engineering, RAG and agents, finetuning, dataset engineering, inference optimization and system architecture.
How Google runs production systems: SLOs and error budgets, toil, monitoring and alerting, on-call and incident response, postmortems, load balancing, overload and cascading failure, consensus, cron, pipelines, data integrity, launches, and how to grow an SRE team.