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Eliminate the unavoidable complexity of object-oriented designs. The innovative data-oriented programming paradigm makes your systems less complex by making it simpler to access and manipulate data. In Data-Oriented Programming you will learn how to: Separate code from data Represent data with generic data structures Manipulate data with general-purpose functions Manage state without mutating data Control concurrency in highly scalable systems Write data-oriented unit tests Specify the shape of your data Benefit from polymorphism without objects Debug programs without a debugger Data-Oriented Programming is a one-of-a-kind guide that introduces the data-oriented paradigm. This groundbreaking approach represents data with generic immutable data structures. It simplifies state management, eases concurrency, and does away with the common problems you’ll find in object-oriented code. The book presents powerful new ideas through conversations, code snippets, and diagrams that help you quickly grok what’s great about DOP. Best of all, the paradigm is language-agnostic—you’ll learn to write DOP code that can be implemented in JavaScript, Ruby, Python, Clojure, and also in traditional OO languages like Java or C#. Forewords by Michael T. Nygard and Ryan Singer. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Code that combines behavior and data, as is common in object-oriented designs, can introduce almost unmanageable complexity for state management. The Data-oriented programming (DOP) paradigm simplifies state management by holding application data in immutable generic data structures and then performing calculations using non-mutating general-purpose functions. Your applications are free of state-related bugs and your code is easier to understand and maintain. About the book Data-Oriented Programming teaches you to design software using the groundbreaking data-oriented paradigm. You’ll put DOP into action to design data models for business entities and implement a library management system that manages state without data mutation. The numerous diagrams, intuitive mind maps, and a unique conversational approach all help you get your head around these exciting new ideas. Every chapter has a lightbulb moment that will change the way you think about programming. What's inside Separate code from data Represent data with generic data structures Manage state without mutating data Control concurrency in highly scalable systems Write data-oriented unit tests Specify the shape of your data About the reader For programmers who have experience with a high-level programming language like JavaScript, Java, Python, C#, Clojure, or Ruby. About the author Yehonathan Sharvit has over twenty years of experience as a software engineer. He blogs, speaks at conferences, and leads Data-Oriented Programming workshops around the world. Table of Contents PART 1 FLEXIBILITY 1 Complexity of object-oriented programming 2 Separation between code and data 3 Basic data manipulation 4 State management 5 Basic concurrency control 6 Unit tests PART 2 SCALABILITY 7 Basic data validation 8 Advanced concurrency control 9 Persistent data structures 10 Database operations 11 Web services PART 3 MAINTAINABILITY 12 Advanced data validation 13 Polymorphism 14 Advanced data manipulation 15 Debugging

Build a data platform to the industry-leading standards set by Microsoft’s own infrastructure. Summary In Data Engineering on Azure you will learn how to: Pick the right Azure services for different data scenarios Manage data inventory Implement production quality data modeling, analytics, and machine learning workloads Handle data governance Using DevOps to increase reliability Ingesting, storing, and distributing data Apply best practices for compliance and access control Data Engineering on Azure reveals the data management patterns and techniques that support Microsoft’s own massive data infrastructure. Author Vlad Riscutia, a data engineer at Microsoft, teaches you to bring an engineering rigor to your data platform and ensure that your data prototypes function just as well under the pressures of production. You'll implement common data modeling patterns, stand up cloud-native data platforms on Azure, and get to grips with DevOps for both analytics and machine learning. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify. About the book In Data Engineering on Azure you’ll learn the skills you need to build and maintain big data platforms in massive enterprises. This invaluable guide includes clear, practical guidance for setting up infrastructure, orchestration, workloads, and governance. As you go, you’ll set up efficient machine learning pipelines, and then master time-saving automation and DevOps solutions. The Azure-based examples are easy to reproduce on other cloud platforms. What's inside Data inventory and data governance Assure data quality, compliance, and distribution Build automated pipelines to increase reliability Ingest, store, and distribute data Production-quality data modeling, analytics, and machine learning About the reader For data engineers familiar with cloud computing and DevOps. About the author Vlad Riscutia is a software architect at Microsoft. Table of Contents 1 Introduction PART 1 INFRASTRUCTURE 2 Storage 3 DevOps 4 Orchestration PART 2 WORKLOADS 5 Processing 6 Analytics 7 Machine learning PART 3 GOVERNANCE 8 Metadata 9 Data quality 10 Compliance 11 Distributing data

Grokking Simplicity is a friendly, practical guide that will change the way you approach software design and development. Summary Distributed across servers, difficult to test, and resistant to modification—modern software is complex. Grokking Simplicity is a friendly, practical guide that will change the way you approach software design and development. It introduces a unique approach to functional programming that explains why certain features of software are prone to complexity, and teaches you the functional techniques you can use to simplify these systems so that they’re easier to test and debug. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Developers rightly fear the unintended complexity that infects most code. This book shows you how to write software that keeps complexity close to its inherent minimum. As you write software you should distinguish between code that alters your system’s state, and code that does not. Once you learn to make that distinction, you can refactor much of your state-altering “actions” into stateless “calculations.” Your software will be simpler. About the book The book also teaches you to solve the complex timing bugs that inevitably creep into asynchronous and multithreaded code. In ad­vanced sections of the book you learn how composable abstractions help avoid repeating code and open up new levels of expressivity. What's inside Patterns for simpler code Powerful time modeling approaches to simplify asynchronous code How higher-order functions can make code reusable and composable About the reader For intermediate and advanced developers building complex software. Exercises, illustrations, self-assessments, and hands-on examples lock in each new idea. About the author Eric Normand is an expert software developer who has been an influential teacher of functional programming since 2007. Table of Contents 1 Welcome to Grokking Simplicity 2 Functional thinking in action PART 1 - ACTIONS, CALCULATIONS, AND DATA 3 Distinguishing actions, calculations, and data 4 Extracting calculations from actions 5 Improving the design of actions 6 Staying immutable in a mutable language 7 Staying immutable with untrusted code 8 Stratified design, part 1 9 Stratified design, part 2 PART 2 - FIRST-CLASS ABSTRACTIONS 10 First-class functions, part 1 11 First-class functions, part 2 12 Functional iteration 13 Chaining functional tools 14 Functional tools for nested data 15 Isolating timelines 16 Sharing resources between timelines 17 Coordinating timelines 18 Reactive and onion architectures 19 The functional journey ahead

Summary Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive. 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. This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful. What's Inside Introduction to big data systems Real-time processing of web-scale data Tools like Hadoop, Cassandra, and Storm Extensions to traditional database skills About the Authors Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems. James Warren is an analytics architect with a background in machine learning and scientific computing. Table of Contents A new paradigm for Big Data PART 1 BATCH LAYER Data model for Big Data Data model for Big Data: Illustration Data storage on the batch layer Data storage on the batch layer: Illustration Batch layer Batch layer: Illustration An example batch layer: Architecture and algorithms An example batch layer: Implementation PART 2 SERVING LAYER Serving layer Serving layer: Illustration PART 3 SPEED LAYER Realtime views Realtime views: Illustration Queuing and stream processing Queuing and stream processing: Illustration Micro-batch stream processing Micro-batch stream processing: Illustration Lambda Architecture in depth

Eliminate the unavoidable complexity of object-oriented designs. The innovative data-oriented programming paradigm makes your systems less complex by making it simpler to access and manipulate data. In Data-Oriented Programming you will learn how to: Separate code from data Represent data with generic data structures Manipulate data with general-purpose functions Manage state without mutating data Control concurrency in highly scalable systems Write data-oriented unit tests Specify the shape of your data Benefit from polymorphism without objects Debug programs without a debugger Data-Oriented Programming is a one-of-a-kind guide that introduces the data-oriented paradigm. This groundbreaking approach represents data with generic immutable data structures. It simplifies state management, eases concurrency, and does away with the common problems you’ll find in object-oriented code. The book presents powerful new ideas through conversations, code snippets, and diagrams that help you quickly grok what’s great about DOP. Best of all, the paradigm is language-agnostic—you’ll learn to write DOP code that can be implemented in JavaScript, Ruby, Python, Clojure, and also in traditional OO languages like Java or C#. Forewords by Michael T. Nygard and Ryan Singer. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Code that combines behavior and data, as is common in object-oriented designs, can introduce almost unmanageable complexity for state management. The Data-oriented programming (DOP) paradigm simplifies state management by holding application data in immutable generic data structures and then performing calculations using non-mutating general-purpose functions. Your applications are free of state-related bugs and your code is easier to understand and maintain. About the book Data-Oriented Programming teaches you to design software using the groundbreaking data-oriented paradigm. You’ll put DOP into action to design data models for business entities and implement a library management system that manages state without data mutation. The numerous diagrams, intuitive mind maps, and a unique conversational approach all help you get your head around these exciting new ideas. Every chapter has a lightbulb moment that will change the way you think about programming. What's inside Separate code from data Represent data with generic data structures Manage state without mutating data Control concurrency in highly scalable systems Write data-oriented unit tests Specify the shape of your data About the reader For programmers who have experience with a high-level programming language like JavaScript, Java, Python, C#, Clojure, or Ruby. About the author Yehonathan Sharvit has over twenty years of experience as a software engineer. He blogs, speaks at conferences, and leads Data-Oriented Programming workshops around the world. Table of Contents PART 1 FLEXIBILITY 1 Complexity of object-oriented programming 2 Separation between code and data 3 Basic data manipulation 4 State management 5 Basic concurrency control 6 Unit tests PART 2 SCALABILITY 7 Basic data validation 8 Advanced concurrency control 9 Persistent data structures 10 Database operations 11 Web services PART 3 MAINTAINABILITY 12 Advanced data validation 13 Polymorphism 14 Advanced data manipulation 15 Debugging

There’s a lot more to the blockchain than mining Bitcoin. This secure system for registering and verifying ownership and identity is perfect for supply chain logistics, health records, and other sensitive data management tasks. Blockchain in Action unlocks the full potential of this revolutionary technology, showing you how to build your own decentralized apps for secure applications including digital democracy, private auctions, and electronic record management. Summary There’s a lot more to the blockchain than mining Bitcoin. This secure system for registering and verifying ownership and identity is perfect for supply chain logistics, health records, and other sensitive data management tasks. Blockchain in Action unlocks the full potential of this revolutionary technology, showing you how to build your own decentralized apps for secure applications including digital democracy, private auctions, and electronic record management. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Blockchain is more than just the tech behind Bitcoin—much more! Combining impenetrable security, decentralized transactions, and independently verifiable supply chains, blockchain applications have transformed currency, digital identity, and logistics. Platforms such as Ethereum and Hyperledger make it easy to get started by using familiar programming languages. About the book Blockchain in Action teaches you how to design and build blockchain-based decentralized apps, and is written in a clear, jargon-free style. First, you’ll get an overview of how blockchain works. Next, you’ll code your first smart contract using Ethereum and Solidity, adding a web interface, trust validation, and other features until your app is ready for deployment. The only thing you need to get started is standard hardware and open source software. What's inside Blockchain compared with other distributed systems Development in Solidity Identity, privacy, and security On-chain and off-chain data and operations About the reader For programmers who know JavaScript. About the author Bina Ramamurthy has thirty years of experience teaching distributed systems, data science, peer-to-peer networking, and blockchain. Table of Contents PART 1 - GETTING STARTED WITH BLOCKCHAIN PROGRAMMING 1 Blockchain basics 2 Smart contracts 3 Techniques for trust and integrity 4 From smart contracts to Dapps PART 2 - TECHNIQUES FOR END-TO-END DAPP DEVELOPMENT 5 Security and privacy 6 On-chain and off-chain data 7 Web3 and a channel Dapp 8 Going public with Infura PART 3 - A ROADMAP AND THE ROAD AHEAD 9 Tokenization of assets 10 Testing smart contracts 11 A roadmap to Dapp development 12 Blockchain: The Road ahead

The next generation of containers is here. Learn Podman directly from its creator, discover its exceptional security features, and start managing rootless containers that integrate easily into your systems. In Podman in Action you will learn how to: Build and run containers in rootless mode Develop and manage pods Use SystemD to oversee a container’s lifecycle Work with the Podman service via Python Keep your containers confined using Podman security features Manage containerized applications on edge devices Podman in Action shows you how to deploy containerized applications on Linux, Windows, and MacOS systems using Podman. Written by Daniel Walsh, who leads the Red Hat Podman team, this book teaches you how to securely manage the entire application lifecycle without human intervention. You’ll quickly get to grips with Podman’s unique advantages over Docker, and learn how easy it is to migrate your Docker-based infrastructure. It also demonstrates how, with Podman, you can easily convert containerized applications into Kubernetes-based microservices. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology It’s time to upgrade your container engine! The Podman container manager delivers flexible image layer control, seamless Kubernetes compatibility, and rootless containers that can be created, run, and managed by users without admin rights. Plus, its OCI-compliant support for the Docker API lets you shift existing containers to Podman without breaking your scripts or changing the way you work. About the book Podman in Action introduces the Podman container manager. The easy-to-follow explanations and examples give you a clear view of what containers are, how they work, and how to manage them using Podman’s powerful features. You’ll get a deep look at the Linux components Podman uses and even learn more about Docker along the way. You’ll especially appreciate author Dan Walsh’s unique insights into container security. What's inside Develop and manage pods Key security concepts including SELinux and SECCOMP Use systemd to oversee a container’s lifecycle Keep your containers confined using Podman security Manage containerized applications on edge devices Install and run Podman on MacOS and Windows About the reader For developers or system administrators experienced with Linux and Docker. About the author Daniel Walsh is a senior distinguished engineer at Red Hat, and leads the team that created Podman. Table of Contents PART 1 FOUNDATIONS 1 Podman: A next-generation container engine 2 Command line 3 Volumes 4 Pods PART 2 DESIGN 5 Customization and configuration files 6 Rootless containers PART 3 ADVANCED TOPICS 7 Integration with systemd 8 Working with Kubernetes 9 Podman as a service PART 4 CONTAINER SECURITY 10 Security container isolation 11 Additional security considerations

Polish your web development skills by working on real-world projects for different use-cases of full-stack web development Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Understand how the different aspects of a MERN application come together through a series of practical projects Set up frontend and backend projects that can be integrated and maintained together Enhance your proficiency in building scalable and sustainable React projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionRecent trends show deeper integration between the two, opening numerous possibilities for building real-world web applications, through server-side technologies like Node.js, Express, and MongoDB. Written by the author of Learning Redux and Learn React Hooks, and CEO of TouchLay, Modern Full-Stack React Projects will guide you through the entire process of advancing from a frontend developer to a full-stack developer. Starting with how to set up robust projects that can be maintained for a long time, you'll then progress toward developing a backend system and integrating it with the frontend. Throughout the book, you'll learn how to build, test, and deploy a blog application and a chat application. You'll also explore MongoDB, Express, React, Node.js (MERN) stack, best practices for frontend and backend development, different full-stack architectures, unit and end-to-end testing, and deployment of full-stack web applications. Once you get to grips with the essential concepts, you'll progress to learn how to use Next.js, an enterprise-grade full-stack web framework for React. By the end, you'll be well-versed in the MERN stack and all set to create performant and scalable full-stack web applications. *Email sign-up and proof of purchase requiredWhat you will learn Implement a backend using Express and MongoDB, and unit-test it with Jest Deploy full-stack web apps using Docker, set up CI/CD and end-to-end tests using Playwright Add authentication using JSON Web Tokens (JWT) Create a GraphQL backend and integrate it with a frontend using Apollo Client Build a chat app based on event-driven architecture using Socket.IO Facilitate Search Engine Optimization (SEO) and implement server-side rendering Use Next.js, an enterprise-ready full-stack framework, with React Server Components and Server Actions Who this book is for This book is for frontend developers, JavaScript engineers, and React developers who possess a solid understanding of JavaScript and React concepts. Specifically designed for those with limited experience in the development, creation, integration, and deployment of backend and full-stack systems, this book will equip you with the necessary skills to create high-quality web applications.

The next generation of containers is here. Learn Podman directly from its creator, discover its exceptional security features, and start managing rootless containers that integrate easily into your systems. In Podman in Action you will learn how to: Build and run containers in rootless mode Develop and manage pods Use SystemD to oversee a container’s lifecycle Work with the Podman service via Python Keep your containers confined using Podman security features Manage containerized applications on edge devices Podman in Action shows you how to deploy containerized applications on Linux, Windows, and MacOS systems using Podman. Written by Daniel Walsh, who leads the Red Hat Podman team, this book teaches you how to securely manage the entire application lifecycle without human intervention. You’ll quickly get to grips with Podman’s unique advantages over Docker, and learn how easy it is to migrate your Docker-based infrastructure. It also demonstrates how, with Podman, you can easily convert containerized applications into Kubernetes-based microservices. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology It’s time to upgrade your container engine! The Podman container manager delivers flexible image layer control, seamless Kubernetes compatibility, and rootless containers that can be created, run, and managed by users without admin rights. Plus, its OCI-compliant support for the Docker API lets you shift existing containers to Podman without breaking your scripts or changing the way you work. About the book Podman in Action introduces the Podman container manager. The easy-to-follow explanations and examples give you a clear view of what containers are, how they work, and how to manage them using Podman’s powerful features. You’ll get a deep look at the Linux components Podman uses and even learn more about Docker along the way. You’ll especially appreciate author Dan Walsh’s unique insights into container security. What's inside Develop and manage pods Key security concepts including SELinux and SECCOMP Use systemd to oversee a container’s lifecycle Keep your containers confined using Podman security Manage containerized applications on edge devices Install and run Podman on MacOS and Windows About the reader For developers or system administrators experienced with Linux and Docker. About the author Daniel Walsh is a senior distinguished engineer at Red Hat, and leads the team that created Podman. Table of Contents PART 1 FOUNDATIONS 1 Podman: A next-generation container engine 2 Command line 3 Volumes 4 Pods PART 2 DESIGN 5 Customization and configuration files 6 Rootless containers PART 3 ADVANCED TOPICS 7 Integration with systemd 8 Working with Kubernetes 9 Podman as a service PART 4 CONTAINER SECURITY 10 Security container isolation 11 Additional security considerations

Summary Google Cloud Platform in Action teaches you to build and launch applications that scale, leveraging the many services on GCP to move faster than ever. You'll learn how to choose exactly the services that best suit your needs, and you'll be able to build applications that run on Google Cloud Platform and start more quickly, suffer fewer disasters, and require less maintenance. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Thousands of developers worldwide trust Google Cloud Platform, and for good reason. With GCP, you can host your applications on the same infrastructure that powers Search, Maps, and the other Google tools you use daily. You get rock-solid reliability, an incredible array of prebuilt services, and a cost-effective, pay-only-for-what-you-use model. This book gets you started. About the Book Google Cloud Platform in Action teaches you how to deploy scalable cloud applications on GCP. Author and Google software engineer JJ Geewax is your guide as you try everything from hosting a simple WordPress web app to commanding cloud-based AI services for computer vision and natural language processing. Along the way, you'll discover how to maximize cloud-based data storage, roll out serverless applications with Cloud Functions, and manage containers with Kubernetes. Broad, deep, and complete, this authoritative book has everything you need. What's inside The many varieties of cloud storage and computing How to make cost-effective choices Hands-on code examples Cloud-based machine learning About the Reader Written for intermediate developers. No prior cloud or GCP experience required. About the Author JJ Geewax is a software engineer at Google, focusing on Google Cloud Platform and API design. Table of Contents PART 1 - GETTING STARTED What is "cloud"? Trying it out: deploying WordPress on Google Cloud The cloud data center PART 2 - STORAGE Cloud SQL: managed relational storage Cloud Datastore: document storage Cloud Spanner: large-scale SQL Cloud Bigtable: large-scale structured data Cloud Storage: object storage PART 3 - COMPUTING Compute Engine: virtual machines Kubernetes Engine: managed Kubernetes clusters App Engine: fully managed applications Cloud Functions: serverless applications Cloud DNS: managed DNS hosting PART 4 - MACHINE LEARNING Cloud Vision: image recognition Cloud Natural Language: text analysis Cloud Speech: audio-to-text conversion Cloud Translation: multilanguage machine translation Cloud Machine Learning Engine: managed machine learning PART 5 - DATA PROCESSING AND ANALYTICS BigQuery: highly scalable data warehouse Cloud Dataflow: large-scale data processing Cloud Pub/Sub: managed event publishing

How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition. Summary Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy's expert instruction and illustration of real-world projects, you'll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology How much has computer vision advanced? One ride in a Tesla is the only answer you'll need. Deep learning techniques have led to exciting breakthroughs in facial recognition, interactive simulations, and medical imaging, but nothing beats seeing a car respond to real-world stimuli while speeding down the highway. About the book How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition. What's inside Image classification and object detection Advanced deep learning architectures Transfer learning and generative adversarial networks DeepDream and neural style transfer Visual embeddings and image search About the reader For intermediate Python programmers. About the author Mohamed Elgendy is the VP of Engineering at Rakuten. A seasoned AI expert, he has previously built and managed AI products at Amazon and Twilio. Table of Contents PART 1 - DEEP LEARNING FOUNDATION 1 Welcome to computer vision 2 Deep learning and neural networks 3 Convolutional neural networks 4 Structuring DL projects and hyperparameter tuning PART 2 - IMAGE CLASSIFICATION AND DETECTION 5 Advanced CNN architectures 6 Transfer learning 7 Object detection with R-CNN, SSD, and YOLO PART 3 - GENERATIVE MODELS AND VISUAL EMBEDDINGS 8 Generative adversarial networks (GANs) 9 DeepDream and neural style transfer 10 Visual embeddings

API Design Patterns lays out a set of design principles for building internal and public-facing APIs. A collection of best practices and design standards for web and internal APIs. In API Design Patterns you will learn: Guiding principles for API patterns Fundamentals of resource layout and naming Handling data types for any programming language Standard methods that ensure predictability Field masks for targeted partial updates Authentication and validation methods for secure APIs Collective operations for moving, managing, and deleting data Advanced patterns for special interactions and data transformations API Design Patterns reveals best practices for building stable, user-friendly APIs. These design patterns can be applied to solve common API problems and flexibly altered to fit your specific needs. Hands-on examples and relevant use-cases illustrate patterns for API fundamentals, advanced functionalities, and even uncommon scenarios. Foreword by Jon Skeet. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology APIs are contracts that define how applications, services, and components communicate. API design patterns provide a shared set of best practices, specifications and standards that ensure APIs are reliable and simple for other developers to use. This book collects and explains the most important patterns from both the API design community and the experts at Google. About the book API Design Patterns lays out a set of design principles for building internal and public-facing APIs. Google API expert JJ Geewax presents patterns that ensure your APIs are consistent, scalable, and flexible. You’ll improve the design of the most common APIs, plus discover techniques for tricky edge cases. Precise illustrations, relevant examples, and detailed scenarios make every pattern clear and easy to understand. What's inside Guiding principles for API patterns Fundamentals of resource layout and naming Advanced patterns for special interactions and data transformations A detailed case-study on building an API and adding features About the reader For developers building web and internal APIs in any language. About the author JJ Geewax is a software engineer at Google, focusing on Google Cloud Platform, API design, and real-time payment systems. He is also the author of Manning’s Google Cloud Platform in Action. Table of Contents PART 1 INTRODUCTION 1 Introduction to APIs 2 Introduction to API design patterns PART 2 DESIGN PRINCIPLES 3 Naming 4 Resource scope and hierarchy 5 Data types and defaults PART 3 FUNDAMENTALS 6 Resource identification 7 Standard methods 8 Partial updates and retrievals 9 Custom methods 10 Long-running operations 11 Rerunnable jobs PART 4 RESOURCE RELATIONSHIPS 12 Singleton sub-resources 13 Cross references 14 Association resources 15 Add and remove custom methods 16 Polymorphism PART 5 COLLECTIVE OPERATIONS 17 Copy and move 18 Batch operations 19 Criteria-based deletion 20 Anonymous writes 21 Pagination 22 Filtering 23 Importing and exporting PART 6 SAFETY AND SECURITY 24 Versioning and compatibility 25 Soft deletion 26 Request deduplication 27 Request validation 28 Resource revisions 29 Request retrial 30 Request authentication

API Design Patterns lays out a set of design principles for building internal and public-facing APIs. A collection of best practices and design standards for web and internal APIs. In API Design Patterns you will learn: Guiding principles for API patterns Fundamentals of resource layout and naming Handling data types for any programming language Standard methods that ensure predictability Field masks for targeted partial updates Authentication and validation methods for secure APIs Collective operations for moving, managing, and deleting data Advanced patterns for special interactions and data transformations API Design Patterns reveals best practices for building stable, user-friendly APIs. These design patterns can be applied to solve common API problems and flexibly altered to fit your specific needs. Hands-on examples and relevant use-cases illustrate patterns for API fundamentals, advanced functionalities, and even uncommon scenarios. Foreword by Jon Skeet. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology APIs are contracts that define how applications, services, and components communicate. API design patterns provide a shared set of best practices, specifications and standards that ensure APIs are reliable and simple for other developers to use. This book collects and explains the most important patterns from both the API design community and the experts at Google. About the book API Design Patterns lays out a set of design principles for building internal and public-facing APIs. Google API expert JJ Geewax presents patterns that ensure your APIs are consistent, scalable, and flexible. You’ll improve the design of the most common APIs, plus discover techniques for tricky edge cases. Precise illustrations, relevant examples, and detailed scenarios make every pattern clear and easy to understand. What's inside Guiding principles for API patterns Fundamentals of resource layout and naming Advanced patterns for special interactions and data transformations A detailed case-study on building an API and adding features About the reader For developers building web and internal APIs in any language. About the author JJ Geewax is a software engineer at Google, focusing on Google Cloud Platform, API design, and real-time payment systems. He is also the author of Manning’s Google Cloud Platform in Action. Table of Contents PART 1 INTRODUCTION 1 Introduction to APIs 2 Introduction to API design patterns PART 2 DESIGN PRINCIPLES 3 Naming 4 Resource scope and hierarchy 5 Data types and defaults PART 3 FUNDAMENTALS 6 Resource identification 7 Standard methods 8 Partial updates and retrievals 9 Custom methods 10 Long-running operations 11 Rerunnable jobs PART 4 RESOURCE RELATIONSHIPS 12 Singleton sub-resources 13 Cross references 14 Association resources 15 Add and remove custom methods 16 Polymorphism PART 5 COLLECTIVE OPERATIONS 17 Copy and move 18 Batch operations 19 Criteria-based deletion 20 Anonymous writes 21 Pagination 22 Filtering 23 Importing and exporting PART 6 SAFETY AND SECURITY 24 Versioning and compatibility 25 Soft deletion 26 Request deduplication 27 Request validation 28 Resource revisions 29 Request retrial 30 Request authentication