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This book helps software engineers understand the practical workings behind buzzwords like NoSQL, Big Data, and Scalability. It delves into the trade-offs and fundamental ideas of diverse data storage and processing technologies, enabling informed decisions for building reliable, scalable, and maintainable data-intensive applications.
As software engineers, we need to build applications that are reliable, scalable and maintainable in the long run. We need to understand the range of available tools and their trade-offs. For that, we have to dig deeper than buzzwords.This book will help you navigate the diverse and fast-changing landscape of technologies for storing and processing data. We compare a broad variety of tools and approaches, so that you can see the strengths and weaknesses of each, and decide what’s best for your application.
How this book is different
As software engineers, we need to build applications that are reliable, scalable and maintainable in the long run. We need to understand the range of available tools and their trade-offs. For that, we have to dig deeper than buzzwords.This book will help you navigate the diverse and fast-changing landscape of technologies for storing and processing data. We compare a broad variety of tools and approaches, so that you can see the strengths and weaknesses of each, and decide what’s best for your application.
How this book is different
- Compare several designs: This book compares the fundamental ideas behind a broad variety of systems. It doesn’t go into the details of how to configure and use one particular tool. But it does explain the trade-offs and fundamental limitations that systems face, so that you can make informed decisions.
- Both theory and practice: We discuss many good ideas from academic research, but we always tie them back to reality. Just because it looks good on a whiteboard doesn’t mean it actually works in practice. We care about ideas that have been proven under intensive workloads, at big companies and at startups.
- Deeper understanding: We go under the hood of the systems you already use, teasing apart how they work internally. The aim is to help you think about data systems in new ways — not just how they work, but why they were designed that way. Your own software will be better as a result.
