Until now. This practical guide provides the first comprehensive overview of software architecture's many aspects. You'll examine architectural characteristics, architectural patterns, component determination, diagramming and presenting architecture, evolutionary architecture, and many other topics.Authors Neal Ford and Mark Richards help you learn through examples in a variety of popular programming languages, such as Java, C#, JavaScript, and others. You'll focus on architecture principles with examples that apply across all technology stacks.

    And how to move all of this data becomes nearly as important as the data itself. If you� re an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds.Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you� ll learn Kafka� s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer.Understand publish-subscribe messaging and how it fits in the big data ecosystem.Explore Kafka producers and consumers for writing and reading messagesUnderstand Kafka patterns and use-case requirements to ensure reliable data deliveryGet best practices for building data pipelines and applications with KafkaManage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasksLearn the most critical metrics among Kafka� s operational measurementsExplore how Kafka� s stream delivery capabilities make it a perfect source for stream processing systems

    It is the definitive reference guide, now in a second edition. Although the first edition was written in 1978, it continues to be a worldwide best-seller. This second edition brings the classic original up to date to include the ANSI standard. From the Preface: We have tried to retain the brevity of the first edition. C is not a big language, and it is not well served by a big book. We have improved the exposition of critical features, such as pointers, that are central to C programming. We have refined the original examples, and have added new examples in several chapters. For instance, the treatment of complicated declarations is augmented by programs that convert declarations into words and vice versa. As before, all examples have been tested directly from the text, which is in machine-readable form. As we said in the first preface to the first edition, C "wears well as one's experience with it grows." With a decade more experience, we still feel that way. We hope that this book will help you to learn C and use it well.

    The book focuses on techniques of lasting value and explains them precisely in terms of a simple abstract machine. The book presents all major programming paradigms in a uniform framework that shows their deep relationships and how and where to use them together.After an introduction to programming concepts, the book presents both well-known and lesser-known computation models ("programming paradigms"). Each model has its own set of techniques and each is included on the basis of its usefulness in practice. The general models include declarative programming, declarative concurrency, message-passing concurrency, explicit state, object-oriented programming, shared-state concurrency, and relational programming. Specialized models include graphical user interface programming, distributed programming, and constraint programming. Each model is based on its kernel language—a simple core language that consists of a small number of programmer- significant elements. The kernel languages are introduced progressively, adding concepts one by one, thus showing the deep relationships between different models. The kernel languages are defined precisely in terms of a simple abstract machine. Because a wide variety of languages and programming paradigms can be modeled by a small set of closely related kernel languages, this approach allows programmer and student to grasp the underlying unity of programming. The book has many program fragments and exercises, all of which can be run on the Mozart Programming System, an Open Source software package that features an interactive incremental development environment.

    And he has boiled it down to one key factor: walkability. The very idea of a modern metropolis evokes visions of bustling sidewalks, vital mass transit, and a vibrant, pedestrian-friendly urban core. But in the typical American city, the car is still king, and downtown is a place that's easy to drive to but often not worth arriving at. Making walkability happen is relatively easy and cheap; seeing exactly what needs to be done is the trick. In this essential new book, Speck reveals the invisible workings of the city, how simple decisions have cascading effects, and how we can all make the right choices for our communities. Bursting with sharp observations and real-world examples, giving key insight into what urban planners actually do and how places can and do change, Walkable City lays out a practical, necessary, and eminently achievable vision of how to make our normal American cities great again.

    It’s helpful for learning Agile and Scrum, and also for a basic preparation for the PSM I exam (Professional Scrum Master level 1). It can also act as a simple reference for Scrum practitioners.

    The aim of The Node Beginner Book is to get you started with developing applications for Node.js, teaching you everything you need to know about advanced JavaScript along the way on 59 pages.

    Big data is on the tip of everyone's tongue. Everyone understands its power and importance, but many fail to grasp the actionable steps and resources required to utilise it effectively. This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective. From technology, media and retail, to sport teams, government agencies and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety and so much more. Organised for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved and the processes put it place to make it practical, as well as the technical details, challenges and lessons learned from each unique scenario. Learn how predictive analytics helps Amazon, Target, John Deere and Apple understand their customers Discover how big data is behind the success of Walmart, LinkedIn, Microsoft and more Learn how big data is changing medicine, law enforcement, hospitality, fashion, science and banking Develop your own big data strategy by accessing additional reading materials at the end of each chapter

    Research psychologist Sonja Lyubomirsky's pioneering concept of the 40% solution shows you how Drawing on her own groundbreaking research with thousands of men and women, research psychologist and University of California professor of psychology Sonja Lyubomirsky has pioneered a detailed yet easy-to-follow plan to increase happiness in our day-to-day lives-in the short term and over the long term. The How of Happiness is a different kind of happiness book, one that offers a comprehensive guide to understanding what happiness is, and isn't, and what can be done to bring us all closer to the happy life we envision for ourselves. Using more than a dozen uniquely formulated happiness-increasing strategies, The How of Happiness offers a new and potentially life- changing way to understand our innate potential for joy and happiness as well as our ability to sustain it in our lives. Beginning with a short diagnostic quiz that helps readers to first quantify and then to understand what she describes as their "happiness set point," Lyubomirsky reveals that this set point determines just 50 percent of happiness while a mere 10 percent can be attributed to differences in life circumstances or situations. This leaves a startling, and startlingly underdeveloped, 40 percent of our capacity for happiness within our power to change. Lyubomirsky's "happiness strategies" introduce readers to the concept of intentional activities, mindful actions that they can use to achieve a happier life. These include exercises in practicing optimism when imagining the future, instruction in how best to savor life's pleasures in the here and now, and a thoroughgoing explanation of the importance of staying active to being happy. Helping readers find the right fit between the goals they set and the activities she suggests, Lyubomirsky also helps readers understand the many obstacles to happiness as well as how to harness individual strengths to overcome them. Always emphasizing how much of our happiness is within our control, Lyubomirsky addresses the "scientific how" of her happiness research, demystifying the many myths that unnecessarily complicate its pursuit. Unlike those of many self-help books, all her recommendations are supported by scientific research. The How of Happiness is both a powerful contribution to the field of positive psychology and a gift to all those who have questioned their own well- being and sought to take their happiness into their own hands.

    With extensive treatment of the heart of epidemiology-from study designs to descriptive epidemiology to quantitative measures-this reader-friendly text is accessible and interesting to a wide range of beginning students in all health-related disciplines. A unique focus is given to real-world applications of epidemiology and the development of skills that students can apply in subsequent course work and in the field. The text is also accompanied by a complete package of instructor and student resources available through a companion Web site.

    It’s a choose-your-own-adventure guide to the world of rapid learning.#1 New York Times bestselling author (and lifelong non-cook) Tim Ferriss takes you from Manhattan to Okinawa, and from Silicon Valley to Calcutta, unearthing the secrets of the world’s fastest learners and greatest chefs. Ferriss uses cooking to explain “meta-learning,” a step-by-step process that can be used to master anything, whether searing steak or shooting 3-pointers in basketball. That is the real “recipe” of The 4-Hour Chef.You'll train inside the kitchen for everything outside the kitchen. Featuring tips and tricks from chess prodigies, world-renowned chefs, pro athletes, master sommeliers, super models, and everyone in between, this “cookbook for people who don’t buy cookbooks” is a guide to mastering cooking and life.The 4-Hour Chef is a five-stop journey through the art and science of learning:1. META-LEARNING. Before you learn to cook, you must learn to learn. META charts the path to doubling your learning potential.2. THE DOMESTIC. DOM is where you learn the building blocks of cooking. These are the ABCs (techniques) that can take you from Dr, Seuss to Shakespeare.3. THE WILD. Becoming a master student requires self-sufficiency in all things. WILD teaches you to hunt, forage, and survive.4. THE SCIENTIST. SCI is the mad scientist and modernist painter wrapped into one. This is where you rediscover whimsy and wonder.5. THE PROFESSIONAL. Swaraj, a term usually associated with Mahatma Gandhi, can be translated as “self-rule.” In PRO, we’ll look at how the best in the world become the best in the world, and how you can chart your own path far beyond this book.

    Here are the landmark ideas that have established the Harvard Business Review as required reading for ambitious business people in organizations around the globe. From the seminal article "Leading Change" by John Kotter to Paul Strebel on why employees so often resist change, Harvard Business Review on Change is the most comprehensive resource available for embracing corporate change--and using it to your company's greatest advantage. Articles include: Leading Change: Why Transformation Efforts Fail by John P. Kotter; Building Your Company's Vision by James C. Collins and Jerry I. Porras; Managing Change: The Art of Balancing by Jeanie Daniel Duck; The Reinvention Roller Coaster: Risking the Present for a Powerful Future by Tracy Goss, Richard T. Pascale, and Anthony G. Athos; Changing the Mind of the Corporation by Roger Martin; Why Do Employees Resist Change? by Paul Strebel; Reshaping an Industry: Lockheed Martin's Survival Story by Norman R. Augustine; and Successful Change Programs Begin with Results by Robert H. Schaffer and Harvey A. Thomson.

    The calculus is not a hard subject and I prove this through an easy to read and obvious approach spanning only 100 pages. I have written this book with the following type of student in mind; the non-traditional student returning to college after a long break, a notoriously weak student in math who just needs to get past calculus to obtain a degree, and the garage tinkerer who wishes to understand a little more about the technical subjects. This book is meant to address the many fundamental thought-blocks that keep the average 'mathaphobe' (or just an interested person who doesn't have the time to enroll in a course) from excelling in mathematics in a clear and concise manner. It is my sincerest hope that this book helps you with your needs.Show more Show less

    Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks.Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data--or anything in between--this guide makes it easier to understand and implement machine learning seamlessly.Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!

    This book is the most complete and up-to-date resource on Java from programming guru, Herb Schildt -- a must-have desk reference for every Java programmer.