Software Systems

Written by an expert who has created systems ranging from DNA scanners to children's toys, this book is ideal for intermediate and experienced programmers, no matter what platform you use. This expanded second edition includes new chapters on IoT and networked sensors, motors and movement, debugging, data handling strategies, and more.

A decade ago, developers built information-sharing software by writing a lot of custom code in a single code base, then adding layers of caching. Now we build information systems: interdependent software and services, data platforms, and event streams. Many of our software-to-systems initiatives fail, though, and when they do, we double down on traditional linear approaches. But linear thinking cannot resolve systems challenges. We need to shift to systems thinking.

There are no easy decisions in software architecture. Instead, there are many hard parts—difficult problems or issues with no best practices—that force you to choose among various compromises. With this book, you'll learn how to think critically about the trade-offs involved with distributed architectures. Architecture veterans and practicing consultants Neal Ford, Mark Richards, Pramod Sadalage, and Zhamak Dehghani discuss strategies for choosing an appropriate architecture.

This fact sheet will assist with better management of risk from OSS use in OT products and increase resilience using available resources. While several resources and recommendations within this fact sheet are best suited for execution by the vendor or the critical infrastructure owner, collaboration across parties will result in less friction for operator workflows and promote a safer, more reliable system and provision of National Critical Functions.

Author Brendan Burns demonstrates how you can adapt existing software design patterns for designing and building reliable distributed applications. Systems engineers and application developers will learn how these long-established patterns provide a common language and framework for dramatically increasing the quality of your system. This fully updated second edition includes new chapters on AI inference, AI training, and building robust systems for the real world.

Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering, and clearly explains how to apply the best practices from software engineering to data science.

It is a fact that you'll be bombarded with system design interview questions which have become part and parcel of all the software engineering hiring processes. Your performance in these interviews will reflect upon your capability to work with complex systems and translate into the role and position that the interviewing organization is interviewing you for. This audiobook is a comprehensive guide to master all the concepts about SDIs.

It's challenging to assess and understand how to learn a new skill, mainly when the subject appears vast. There can be so much data available that it is difficult to know where to begin. Even worse, you start learning and soon find there are so many definitions, commands, and complexities not clarified. This book is helpful even if you have never used Linux before but want to master it.

Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases.