That was before one pioneering work documented the underground computer revolution that was about to change our world forever. With groundbreaking profiles of Bill Gates, Steve Wozniak, MIT's Tech Model Railroad Club, and more, Steven Levy's Hackers brilliantly captured a seminal moment when the risk-takers and explorers were poised to conquer twentieth-century America's last great frontier. And in the Internet age, the hacker ethic-first espoused here-is alive and well.

    What You Will Learn Design and implement a series of server-side JavaScript patterns so you understand why and when to apply them in different use case scenarios Understand the fundamental Node.js components and use them to their full potential Untangle your modules by organizing and connecting them coherently Reuse well-known solutions to circumvent common design and coding issues Deal with asynchronous code with comfort and ease Identify and prevent common problems, programming errors, and anti-patterns In Detail Node.js is a massively popular software platform that lets you use JavaScript to easily create scalable server-side applications. It allows you to create efficient code, enabling a more sustainable way of writing software made of only one language across the full stack, along with extreme levels of reusability, pragmatism, simplicity, and collaboration. Node.js is revolutionizing the web and the way people and companies create their software.In this book, we will take you on a journey across various ideas and components, and the challenges you would commonly encounter while designing and developing software using the Node.js platform. You will also discover the "Node.js way" of dealing with design and coding decisions.The book kicks off by exploring the fundamental principles and components that define the platform. It then shows you how to master asynchronous programming and how to design elegant and reusable components using well-known patterns and techniques. The book rounds off by teaching you the various approaches to scale, distribute, and integrate your Node.js application.

    Web-based companies live or die by the ability to scale their infrastructure to accommodate increasing demand. This book is a hands-on and practical guide to planning for such growth, with many techniques and considerations to help you plan, deploy, and manage web application infrastructure.The Art of Capacity Planning is written by the manager of data operations for the world-famous photo-sharing site Flickr.com, now owned by Yahoo! John Allspaw combines personal anecdotes from many phases of Flickr's growth with insights from his colleagues in many other industries to give you solid guidelines for measuring your growth, predicting trends, and making cost-effective preparations. Topics include:Evaluating tools for measurement and deployment Capacity analysis and prediction for storage, database, and application servers Designing architectures to easily add and measure capacity Handling sudden spikes Predicting exponential and explosive growth How cloud services such as EC2 can fit into a capacity strategy In this book, Allspaw draws on years of valuable experience, starting from the days when Flickr was relatively small and had to deal with the typical growth pains and cost/performance trade-offs of a typical company with a Web presence. The advice he offers in The Art of Capacity Planning will not only help you prepare for explosive growth, it will save you tons of grief.

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    Find out how the Swedish police combined XP, Scrum, and Kanban in a 60-person project. From start to finish, you'll see how to deliver a successful product using Lean principles. We start with an organization in desperate need of a new way of doing things and finish with a group of sixty, all working in sync to develop a scalable, complex system. You'll walk through the project step by step, from customer engagement, to the daily "cocktail party," version control, bug tracking, and release. In this honest look at what works--and what doesn't--you'll find out how to: Make quality everyone's business, not just the testers. Keep everyone moving in the same direction without micromanagement. Use simple and powerful metrics to aid in planning and process improvement. Balance between low-level feature focus and high-level system focus. You'll be ready to jump into the trenches and streamline your own development process.ContentsForewordPrefacePART I: HOW WE WORK1. About the Project1.1 Timeline 51.2 How We Sliced the Elephant 61.3 How We Involved the Customer 72. Structuring the Teams3. Attending the Daily Cocktail Party3.1 First Tier: Feature Team Daily Stand-up3.2 Second Tier: Sync Meetings per Specialty3.3 Third Tier: Project Sync Meeting4. The Project Board4.1 Our Cadences4.2 How We Handle Urgent Issues and Impediments5. Scaling the Kanban Boards6. Tracking the High-Level Goal7. Defining Ready and Done7.1 Ready for Development7.2 Ready for System Test7.3 How This Improved Collaboration 8. Handling Tech Stories8.1 Example 1: System Test Bottleneck8.2 Example 2: Day Before the Release8.3 Example 3: The 7-Meter Class9. Handling Bugs9.1 Continuous System Test9.2 Fix the Bugs Immediately9.3 Why We Limit the Number of Bugs in the Bug Tracker9.4 Visualizing Bugs9.5 Preventing Recurring Bugs10. Continuously Improving the Process10.1 Team Retrospectives10.2 Process Improvement Workshops10.3 Managing the Rate of Change11. Managing Work in Progress11.1 Using WIP Limits11.2 Why WIP Limits Apply Only to Features12. Capturing and Using Process Metrics12.1 Velocity (Features per Week)12.2 Why We Don’t Use Story Points12.3 Cycle Time (Weeks per Feature)12.4 Cumulative Flow12.5 Process Cycle Efficiency13. Planning the Sprint and Release13.1 Backlog Grooming13.2 Selecting the Top Ten Features13.3 Why We Moved Backlog Grooming Out of the Sprint Planning Meeting13.4 Planning the Release14. How We Do Version Control14.1 No Junk on the Trunk14.2 Team Branches14.3 System Test Branch15. Why We Use Only Physical Kanban Boards16. What We Learned16.1 Know Your Goal16.2 Experiment16.3 Embrace Failure16.4 Solve Real Problems16.5 Have Dedicated Change Agents16.6 Involve PeoplePART II: A CLOSER LOOK AT THE TECHNIQUES 17. Agile and Lean in a Nutshell17.1 Agile in a Nutshell17.2 Lean in a Nutshell17.3 Scrum in a Nutshell17.4 XP in a Nutshell17.5 Kanban in a Nutshell18. Reducing the Test Automation Backlog18.1 What to Do About It18.2 How to Improve Test Coverage a Little Bit Each Iteration18.3 Step 1: List Your Test Cases18.4 Step 2: Classify Each Test18.5 Step 3: Sort the List in Priority Order18.6 Step 4: Automate a Few Tests Each Iteration18.7 Does This Solve the Problem?19. Sizing the Backlog with Planning Poker19.1 Estimating Without Planning Poker19.2 Estimating with Planning Poker19.3 Special Cards20. Cause-Effect Diagrams20.1 Solve Problems, Not Symptoms20.2 The Lean Problem-Solving Approach: A3 Thinking20.3 How to Use Cause-Effect Diagrams20.4 Example 1: Long Release Cycle20.5 Example 2: Defects Released to Production20.6 Example 3: Lack of Pair Programming20.7 Example 4: Lots of Problems20.8 Practical Issues: How to Create and Maintain the Diagrams20.9 Pitfalls20.10 Why Use Cause-Effect Diagrams?21. Final WordsA1. Glossary: How We Avoid Buzzword BingoIndex

    This makes the book random-access: Items are easy to browse and study in whatever order the reader needs. I will be recommending "Effective Python" to students as an admirably compact source of mainstream advice on a very broad range of topics for the intermediate Python programmer. " Brandon Rhodes, software engineer at Dropbox and chair of PyCon 2016-2017" It s easy to start coding with Python, which is why the language is so popular. However, Python s unique strengths, charms, and expressiveness can be hard to grasp, and there are hidden pitfalls that can easily trip you up. " Effective Python " will help you master a truly Pythonic approach to programming, harnessing Python s full power to write exceptionally robust and well-performing code. Using the concise, scenario-driven style pioneered in Scott Meyers best-selling "Effective C++, " Brett Slatkin brings together 59 Python best practices, tips, and shortcuts, and explains them with realistic code examples. Drawing on years of experience building Python infrastructure at Google, Slatkin uncovers little-known quirks and idioms that powerfully impact code behavior and performance. You ll learn the best way to accomplish key tasks, so you can write code that s easier to understand, maintain, and improve. Key features includeActionable guidelines for all major areas of Python 3.x and 2.x development, with detailed explanations and examples Best practices for writing functions that clarify intention, promote reuse, and avoid bugs Coverage of how to accurately express behaviors with classes and objects Guidance on how to avoid pitfalls with metaclasses and dynamic attributes More efficient approaches to concurrency and parallelism Better techniques and idioms for using Python s built-in modules Tools and best practices for collaborative development Solutions for debugging, testing, and optimization in order to improve quality and performance "

    Ideal for processing large datasets, the Apache Hadoop framework is an open source implementation of the MapReduce algorithm on which Google built its empire. This comprehensive resource demonstrates how to use Hadoop to build reliable, scalable, distributed systems: programmers will find details for analyzing large datasets, and administrators will learn how to set up and run Hadoop clusters. Complete with case studies that illustrate how Hadoop solves specific problems, this book helps you:Use the Hadoop Distributed File System (HDFS) for storing large datasets, and run distributed computations over those datasets using MapReduce Become familiar with Hadoop's data and I/O building blocks for compression, data integrity, serialization, and persistence Discover common pitfalls and advanced features for writing real-world MapReduce programs Design, build, and administer a dedicated Hadoop cluster, or run Hadoop in the cloud Use Pig, a high-level query language for large-scale data processing Take advantage of HBase, Hadoop's database for structured and semi-structured data Learn ZooKeeper, a toolkit of coordination primitives for building distributed systems If you have lots of data -- whether it's gigabytes or petabytes -- Hadoop is the perfect solution. Hadoop: The Definitive Guide is the most thorough book available on the subject. "Now you have the opportunity to learn about Hadoop from a master-not only of the technology, but also of common sense and plain talk." -- Doug Cutting, Hadoop Founder, Yahoo!