An organized reading list for illustrating the patterns behind scalable, reliable, and performant large-scale systems. Concepts are explained in the articles of prominent engineers and credible references. Case studies are taken from battle-tested systems that serve millions to billions of users.
Understand your problems: scalability problem (fast for a single user but slow under heavy load) or performance problem (slow for a single user) by reviewing some design principles and checking how scalability and performance problems are solved at tech companies. The section of intelligence are created for those who work with data and machine learning at big (data) and deep (learning) scale.
Look at some interview notes and real-world architectures with completed diagrams to get a comprehensive view before designing your system on whiteboard. You can check some talks of engineers from tech giants to know how they build, scale, and optimize their systems. There are some selected books for you (most of them are free)! Good luck :four_leaf_clover:
The goal of scaling team is not growing team size but increasing team output and value. You can find out how tech companies reach that goal in various aspects: hiring, management, organization, culture, and communication in the organization section.