Bücher veröffentlicht von O'Reilly Media

Imagine what you could do if scalability wasn't a problem. With this hands-on guide, you'll learn how the Cassandra database management system handles hundreds of terabytes of data while remaining highly available across multiple data centers. This revised third edition--updated for Cassandra 4.0 and new developments in the Cassandra ecosystem, including deployments in Kubernetes with K8ssandra--provides technical details and practical examples to help you put this database to work in a production environment.Authors Jeff Carpenter and Eben Hewitt demonstrate the advantages of Cassandra's nonrelational design, with special attention to data modeling. Developers, DBAs, and application architects looking to solve a database scaling issue or future-proof an application will learn how to harness Cassandra's speed and flexibility.Understand Cassandra's distributed and decentralized structureUse the Cassandra Query Language (CQL) and cqlsh (the CQL shell)Create a working data model and compare it with an equivalent relational modelDesign and develop applications using client driversExplore cluster topology and learn how nodes exchange dataMaintain a high level of performance in your clusterDeploy Cassandra onsite, in the cloud, or with Docker and KubernetesIntegrate Cassandra with Spark, Kafka, Elasticsearch, Solr, and Lucene

Testing is a critical discipline for any organization looking to deliver high-quality software. This practical book provides software developers and QA engineers with a comprehensive one-stop guide to testing skills in 10 different categories. You'll learn appropriate strategies, concepts, and practical implementation knowledge you can apply from both a development and testing perspective for web and mobile applications.Author Gayathri Mohan offers examples of more than 40 tools you can use immediately. You'll acquire the skills to conduct exploratory testing, test automation, cross-functional testing, data testing, mobile testing, and visual testing, as well as tests for performance, security, and accessibility. You'll learn to integrate them in continuous integration pipelines to gain faster feedback. Once you dive into this guide, you'll be able to tackle challenging development workflows with a focus on quality.With this book, you will:Learn how to employ various testing types to yield maximum quality in your projectsExplore new testing methods by following the book's strategies and conceptsLearn how to apply these tools at work by following detailed examplesImprove your skills and job prospects by gaining a broad exposure to testing best practices

Kubernetes is the operating system of the cloud-native world, providing a reliable and scalable platform for running containerized workloads. This book shows developers and operations staff how to apply industry-standard DevOps practices to Kubernetes in a cloud-native context.

The cloud is becoming the de facto home for companies ranging from enterprises to startups. Moving to the cloud means moving your applications from monolith to microservices. But once you do, running and maintaining these services brings its own level of complexity. The answer? Modularity, deployability, observability, and self-healing capacity through cloud native development.With this practical book, Nishant Singh and Michael Kehoe show you how to build a true cloud native infrastructure using Microsoft Azure or another cloud computing solution by following guidelines from the Cloud Native Computing Foundation (CNCF). DevOps and site reliability engineers will learn how adapting applications to cloud native early in the design phase helps you fully utilize the elasticity and distributed nature of the cloud.This book helps you explore:Why go cloud native?How to use infrastructure as codeWhat it takes to containerize an applicationWhy and how Kubernetes is the "e;grand orchestrator"e;How to create a Kubernetes cluster on AzureHow observability complements monitoringHow to use service discovery and a service mesh to find new territoriesHow networking and policy management serve as gatekeepersHow distributed databases and storage work

The world around us is full of data that holds unique insights and valuable stories, and this book will help you uncover them. Whether you already work with data or want to learn more about its possibilities, the examples and techniques in this practical book will help you more easily clean, evaluate, and analyze data so that you can generate meaningful insights and compelling visualizations.Complementing foundational concepts with expert advice, author Susan E. McGregor provides the resources you need to extract, evaluate, and analyze a wide variety of data sources and formats, along with the tools to communicate your findings effectively. This book delivers a methodical, jargon-free way for data practitioners at any level, from true novices to seasoned professionals, to harness the power of data.Use Python 3.8+ to read, write, and transform data from a variety of sourcesUnderstand and use programming basics in Python to wrangle data at scaleOrganize, document, and structure your code using best practicesCollect data from structured data files, web pages, and APIsPerform basic statistical analyses to make meaning from datasetsVisualize and present data in clear and compelling ways

Observability is critical for building, changing, and understanding the software that powers complex modern systems. Teams that adopt observability are much better equipped to ship code swiftly and confidently, identify outliers and aberrant behaviors, and understand the experience of each and every user. This practical book explains the value of observable systems and shows you how to practice observability-driven development.Authors Charity Majors, Liz Fong-Jones, and George Miranda from Honeycomb explain what constitutes good observability, show you how to improve upon what you??re doing today, and provide practical dos and don'ts for migrating from legacy tooling, such as metrics monitoring and log management. You??ll also learn the impact observability has on organizational culture (and vice versa).You'll explore:How the concept of observability applies to managing software systemsThe value of practicing observability when delivering and managing complex cloud native applications and systemsThe impact observability has across the entire software development lifecycleHow and why different functional teams use observability with service-level objectives (SLOs)How to instrument your code to help future engineers understand the code you wrote todayHow to produce quality code for context-aware system debugging and maintenanceHow data-rich analytics can help you debug elusive issues quickly

How can you realize MySQL's full power? With High Performance MySQL, you'll learn advanced techniques for everything from setting service-level objectives to designing schemas, indexes, and queries to tuning your server, operating system, and hardware to achieve your platform's full potential. This guide also teaches database administrators safe and practical ways to scale applications through replication, load balancing, high availability, and failover.Updated to reflect recent advances in cloud- and self-hosted MySQL, InnoDB performance, and new features and tools, this revised edition helps you design a relational data platform that will scale with your business. You'll learn best practices for database security along with hard-earned lessons in both performance and database stability.Dive into MySQL's architecture, including key facts about its storage enginesLearn how server configuration works with your hardware and deployment choicesMake query performance part of your software delivery processExamine enhancements to MySQL's replication and high availabilityCompare different MySQL offerings in managed cloud environmentsExplore MySQL's full stack optimization from application-side configuration to server tuningTurn traditional database management tasks into automated processes

Ready to learn Java? This book combines puzzles, strong visuals, mysteries, and soul-searching interviews with famous Java objects to engage you in many different ways. It's fast, it's fun, and it's effective. And despite its playful appearance, Head First Java is serious stuff: a complete introduction to object-oriented programming and Java.

Popularly known as "the Llama," Learning Perl is the book most programmers rely on to get started with this versatile language. The bestselling Perl tutorial since it was first published in 1993, this sixth edition includes recent changes to the language up to Perl 5.24.

Even if you're familiar with C# syntax, knowing how to combine various language features is a critical skill when you're building applications. This cookbook is packed full of recipes to help you solve issues for C# programming tasks you're likely to encounter. You'll learn tried-and-true techniques to help you achieve greater productivity and improve the quality of your code.Author and independent consultant Joe Mayo shares some of the most important practices you'll need to be successful as a C# developer. Each section of this cookbook describes some useful facet of the C# programming language. These recipes--the result of many years of experience--are proven concepts for solving real-world problems with C#.Recipes in this book will help you:Set up your project, manage object lifetime, and establish patternsImprove code quality through maintainability, error prevention, and correct syntaxUse LINQ to Objects for in-memory data manipulation and queryingUnderstand the differences between dynamic programming and reflectionApply several async programming features you may not be aware ofWork with data using newer libraries and algorithmsLearn different ways to use new C# features, such as pattern matching and records

Getting started with Arduino is a snap. To use the introductory examples in this guide, all you need is an Arduino Uno or Leonardo, along with a USB cable and an LED. The easy-to-use, free Arduino development environment runs on Mac, Windows, and Linux.

This book introduces the reader to amateur radio and prepares them for the FCC Technician license exam. It focuses on electronics and wireless technologies through projects that provide some hands-on hardware construction and operation.

In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. If you're familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started.

Features information on vim, the vi clone. This guide helps you learn text editing basics and advanced tools for both editors, such as multi-window editing, how to write both interactive macros and scripts to extend the editor, and power tools for programmers. It also includes an introduction to text editing with vi.

With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance.Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and moreUse automated machine learning to implement a specific subset of use cases with SageMaker AutopilotDive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deploymentTie everything together into a repeatable machine learning operations pipelineExplore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache KafkaLearn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more

In today??s IT architectures, microservices and serverless functions play increasingly important roles in process automation. But how do you create meaningful, comprehensive, and connected business solutions when the individual components are decoupled and independent by design? Targeted at developers and architects, this book presents a framework through examples, practical advice, and use cases to help you design and automate complex processes.As systems are more distributed, asynchronous, and reactive, process automation requires state handling to deal with long-running interactions. Author Bernd Ruecker demonstrates how to leverage process automation technology like workflow engines to orchestrate software, humans, decisions, or bots.Learn how modern process automation compares to business process management, service-oriented architecture, batch processing, event streaming, and data pipeline solutionsUnderstand how to use workflow engines and executable process models with BPMNUnderstand the difference between orchestration and choreography and how to balance both

Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcementand enable a machine to learn by itself.Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learnnumerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML.Learn what RL is and how the algorithms help solve problemsBecome grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learningDive deep into a range of value and policy gradient methodsApply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learningUnderstand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and moreGet practical examples through the accompanying website

More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact.This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout.This book helps you:Fulfill data science value by reducing friction throughout ML pipelines and workflowsRefine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracyDesign the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainableOperationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized

Working with AI is complicated and expensive for many developers. That's why cloud providers have stepped in to make it easier, offering free (or affordable) state-of-the-art models and training tools to get you started. With this book, you'll learn how to use Google's AI-powered cloud services to do everything from creating a chatbot to analyzing text, images, and video.Author Micheal Lanham demonstrates methods for building and training models step-by-step and shows you how to expand your models to accomplish increasingly complex tasks. If you have a good grasp of math and the Python language, you'll quickly get up to speed with Google Cloud Platform, whether you want to build an AI assistant or a simple business AI application.Learn key concepts for data science, machine learning, and deep learningExplore tools like Video AI and AutoML TablesBuild a simple language processor using deep learning systemsPerform image recognition using CNNs, transfer learning, and GANsUse Google's Dialogflow to create chatbots and conversational AIAnalyze video with automatic video indexing, face detection, and TensorFlow HubBuild a complete working AI agent application

With this practical book, Kief Morris of ThoughtWorks shows you how to effectively use principles, practices, and patterns pioneered by infrastructure and development teams to manage cloud age infrastructure.

Problem solving with JavaScript is a lot trickier now that its use has expanded considerably in size, scope, and complexity. This cookbook has your back, with recipes for common tasks across the JavaScript world, whether youre working in the browser, the server, or a mobile environment. Each recipe includes reusable code and practical advice for tackling JavaScript objects, Node, Ajax, JSON, data persistence, graphical and media applications, complex frameworks, modular JavaScript, APIs, and many related technologies.Aimed at people who have some experience with JavaScript, the first part covers traditional uses of JavaScript, along with new ideas and improved functionality. The second part dives into the server, mobile development, and a plethora of leading-edge tools. Youll save timeand learn more about JavaScript in the process.Topics include:Classic JavaScript:Arrays, functions, and the JavaScript ObjectAccessing the user interfaceTesting and accessibilityCreating and using JavaScript librariesClient-server communication with AjaxRich, interactive web effectsJavaScript, All Blown Up:New ECMAScript standard objectsUsing Node on the serverModularizing and managing JavaScriptComplex JavaScript frameworksAdvanced client-server communicationsVisualizations and client-server graphicsMobile application development

Organizations today often struggle to balance business requirements with ever-increasing volumes of data. Additionally, the demand for leveraging large-scale, real-time data is growing rapidly among the most competitive digital industries. Conventional system architectures may not be up to the task. With this practical guide, youll learn how to leverage large-scale data usage across the business units in your organization using the principles of event-driven microservices.Author Adam Bellemare takes you through the process of building an event-driven microservice-powered organization. Youll reconsider how data is produced, accessed, and propagated across your organization. Learn powerful yet simple patterns for unlocking the value of this data. Incorporate event-driven design and architectural principles into your own systems. And completely rethink how your organization delivers value by unlocking near-real-time access to data at scale.Youll learn:How to leverage event-driven architectures to deliver exceptional business valueThe role of microservices in supporting event-driven designsArchitectural patterns to ensure success both within and between teams in your organizationApplication patterns for developing powerful event-driven microservicesComponents and tooling required to get your microservice ecosystem off the ground

Threat modeling is one of the most essential--and most misunderstood--parts of the development lifecycle. Whether you're a security practitioner or a member of a development team, this book will help you gain a better understanding of how you can apply core threat modeling concepts to your practice to protect your systems against threats.Contrary to popular belief, threat modeling doesn't require advanced security knowledge to initiate or a Herculean effort to sustain. But it is critical for spotting and addressing potential concerns in a cost-effective way before the code's written--and before it's too late to find a solution. Authors Izar Tarandach and Matthew Coles walk you through various ways to approach and execute threat modeling in your organization.Explore fundamental properties and mechanisms for securing data and system functionalityUnderstand the relationship between security, privacy, and safetyIdentify key characteristics for assessing system securityGet an in-depth review of popular and specialized techniques for modeling and analyzing your systemsView the future of threat modeling and Agile development methodologies, including DevOps automationFind answers to frequently asked questions, including how to avoid common threat modeling pitfalls

Coding and testing are generally considered separate areas of expertise. In this practical book, Java expert Scott Oaks takes the approach that anyone who works with Java should be adept at understanding how code behaves in the Java Virtual Machineincluding the tunings likely to help performance. This updated second edition helps you gain in-depth knowledge of Java application performance using both the JVM and the Java platform.Developers and performance engineers alike will learn a variety of features, tools, and processes for improving the way the Java 8 and 11 LTS releases perform. While the emphasis is on production-supported releases and features, this book also features previews of exciting new technologies such as ahead-of-time compilation and experimental garbage collections.Understand how various Java platforms and compilers affect performanceLearn how Java garbage collection worksApply four principles to obtain best results from performance testingUse the JDK and other tools to learn how a Java application is performingMinimize the garbage collectors impact through tuning and programming practicesTackle performance issues in Java APIsImprove Java-driven database application performance

Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, youll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managersincluding experienced practitioners and novices alikewill learn the tools, best practices, and challenges involved in building a real-world ML application step by step.Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.This book will help you:Define your product goal and set up a machine learning problemBuild your first end-to-end pipeline quickly and acquire an initial datasetTrain and evaluate your ML models and address performance bottlenecksDeploy and monitor your models in a production environment

With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. Youll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way.Author Seth Weidman shows you how neural networks work using a first principles approach. Youll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, youll be set up for success on all future deep learning projects.This book provides:Extremely clear and thorough mental modelsaccompanied by working code examples and mathematical explanationsfor understanding neural networksMethods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented frameworkWorking implementations and clear-cut explanations of convolutional and recurrent neural networksImplementation of these neural network concepts using the popular PyTorch framework

Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase.Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly.Youll get the guidance you need to confidently:Find and wrangle time series dataUndertake exploratory time series data analysisStore temporal dataSimulate time series dataGenerate and select features for a time seriesMeasure errorForecast and classify time series with machine or deep learningEvaluate accuracy and performance

Much has changed in technology over the past decade. Data is hot, the cloud is ubiquitous, and many organizations need some form of automation. Throughout these transformations, Python has become one of the most popular languages in the world. This practical resource shows you how to use Python for everyday Linux systems administration tasks with todays most useful DevOps tools, including Docker, Kubernetes, and Terraform.Learning how to interact and automate with Linux is essential for millions of professionals. Python makes it much easier. With this book, youll learn how to develop software and solve problems using containers, as well as how to monitor, instrument, load-test, and operationalize your software. Looking for effective ways to "e;get stuff done"e; in Python? This is your guide.Python foundations, including a brief introduction to the languageHow to automate text, write command-line tools, and automate the filesystemLinux utilities, package management, build systems, monitoring and instrumentation, and automated testingCloud computing, infrastructure as code, Kubernetes, and serverlessMachine learning operations and data engineering from a DevOps perspectiveBuilding, deploying, and operationalizing a machine learning project

This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures.