Books published by O'Reilly Media

With millions of new users and several new models, the Raspberry Pi ecosystem continues to expandalong with a lot of new questions about the Pis capabilities. The second edition of this popular cookbook provides more than 240 hands-on recipes for running this tiny low-cost computer with Linux, programming it with Python, and hooking up sensors, motors, and other hardwareincluding Arduino and the Internet of Things.Prolific hacker and author Simon Monk also teaches basic principles to help you use new technologies with Raspberry Pi as its ecosystem continues to develop. This cookbook is ideal for programmers and hobbyists familiar with the Pi through resources, including Getting Started with Raspberry Pi (OReilly). Python and other code examples from the book are available on GitHub.Set up your Raspberry Pi and connect to a networkWork with its Linux-based operating systemProgram Raspberry Pi with PythonGive your Pi "e;eyes"e; with computer visionControl hardware through the GPIO connectorUse Raspberry Pi to run different types of motorsWork with switches, keypads, and other digital inputsUse sensors to measure temperature, light, and distanceConnect to IoT devices in various waysCreate dynamic projects with Arduino

Is Kubernetes ready for stateful workloads? This open source system has become the primary platform for deploying and managing cloud native applications. But because it was originally designed for stateless workloads, working with data on Kubernetes has been challenging. If you want to avoid the inefficiencies and duplicative costs of having separate infrastructure for applications and data, this practical guide can help.Using Kubernetes as your platform, you'll learn open source technologies that are designed and built for the cloud. Authors Jeff Carpenter and Patrick McFadin provide case studies to help you explore new use cases and avoid the pitfalls others have faced. Youll get an insider's view of what's coming from innovators who are creating next-generation architectures and infrastructure.With this book, you will:Learn how to use basic Kubernetes resources to compose data infrastructureAutomate the deployment and operations of data infrastructure on Kubernetes using tools like Helm and operatorsEvaluate and select data infrastructure technologies for use in your applicationsIntegrate data infrastructure technologies into your overall stackExplore emerging technologies that will enhance your Kubernetes-based applications in the future

Ready to build quantum computing applications using Q# and the Microsoft Quantum Development Kit? This is the book for you. Q# is a domain-specific language for expressing quantum algorithms that combines familiar "e;classical"e; language constructs with quantum-specific ones. Ideal for any developer familiar with (or willing to learn) the basics of quantum computing and looking to get started with quantum programming, this pocket guide quickly helps you find syntax and usage information for unfamiliar aspects of Q#.You'll explore the quantum software development lifecycle from implementing the program to running it on quantum simulators to testing and debugging it. You'll learn to use the tools provided by Microsoft's Quantum Development Kit for each step of the process.You'll explore:Q# language details, including data types, statements, and operatorsGuidelines for organizing Q# code and invoking it from different environmentsInformation on simulators and tools in the Microsoft Quantum Development KitAdvice on testing and debugging tools and techniques for quantum programs

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

The amount of data being generated today is staggering and growing. Apache Spark has emerged as the de facto tool to analyze big data and is now a critical part of the data science toolbox. Updated for Spark 3.0, this practical guide brings together Spark, statistical methods, and real-world datasets to teach you how to approach analytics problems using PySpark, Spark's Python API, and other best practices in Spark programming.Data scientists Akash Tandon, Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills offer an introduction to the Spark ecosystem, then dive into patterns that apply common techniques-including classification, clustering, collaborative filtering, and anomaly detection, to fields such as genomics, security, and finance. This updated edition also covers NLP and image processing.If you have a basic understanding of machine learning and statistics and you program in Python, this book will get you started with large-scale data analysis.Familiarize yourself with Spark's programming model and ecosystemLearn general approaches in data scienceExamine complete implementations that analyze large public datasetsDiscover which machine learning tools make sense for particular problemsExplore code that can be adapted to many uses

Software architecture metrics are key to the maintainability and architectural quality of a software project and they can warn you about dangerous accumulations of architectural and technical debt early in the process. In this practical book, leading hands-on software architects share case studies to introduce metrics that every software architect should know.This isn't a book about theory. It's more about practice and implementation, about what has already been tried and worked. Detecting software architectural issues early is crucial for the success of your software: it helps mitigate the risk of poor performance and lowers the cost of repairing those issues. Written by practitioners for software architects and software developers eager to explore successful case studies, this guide will help you learn more about decision and measurement effectiveness.Through contributions from 10 prominent practitioners, this book shares key software architecture metrics to help you set the right KPIs and measure the results. You'll learn how to:Measure how well your software architecture is meeting your goalsChoose the right metrics to track (and skip the ones you don't need)Improve observability, testability, and deployabilityPrioritize software architecture projectsBuild insightful and relevant dashboards

The software development ecosystem is constantly changing, providing a constant stream of new tools, frameworks, techniques, and paradigms. Over the past few years, incremental developments in core engineering practices for software development have created the foundations for rethinking how architecture changes over time, along with ways to protect important architectural characteristics as it evolves. This practical guide ties those parts together with a new way to think about architecture and time.

Get up to speed on Git for tracking, branching, merging, and managing code revisions. Through a series of step-by-step tutorials, this practical guide takes you quickly from Git fundamentals to advanced techniques, and provides friendly yet rigorous advice for navigating the many functions of this open source version control system.This thoroughly revised edition also includes tips for manipulating trees, extended coverage of the reflog and stash, and a complete introduction to the GitHub repository. Git lets you manage code development in a virtually endless variety of ways, once you understand how to harness the systems flexibility. This book shows you how.Learn how to use Git for several real-world development scenariosGain insight into Gits common-use cases, initial tasks, and basic functionsUse the system for both centralized and distributed version controlLearn how to manage merges, conflicts, patches, and diffsApply advanced techniques such as rebasing, hooks, and ways to handle submodulesInteract with Subversion (SVN) repositoriesincluding SVN to Git conversionsNavigate, use, and contribute to open source projects though GitHub

"Digital transformation has accelerated nearly tenfold in recent years as both a business and technology journey. Yet, most white papers and how-to guides still focus solely on the business side, rather than include methods for optimizing the technology behind it."--

Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does.Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability. Experienced machine learning engineers and data scientists will learn hands-on how these techniques work so that you'll be able to apply these tools more easily in your daily workflow.This essential book provides:A detailed look at some of the most useful and commonly used explainability techniques, highlighting pros and cons to help you choose the best tool for your needsTips and best practices for implementing these techniquesA guide to interacting with explainability and how to avoid common pitfallsThe knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systemsAdvice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text dataExample implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace

This streamlined intro to Microsoft Power BI covers all the foundational aspects and features you need to go from "zero to hero" with data and visualizations.

With technological advancements, fast markets, and higher complexity of systems, software engineers tend to skip the uncomfortable topic of software efficiency. However, tactical, observability-driven performance optimizations are vital for every product to save money and ensure business success.With this book, any engineer can learn how to approach software efficiency effectively, professionally, and without stress. Author Bartomiej Potka provides the tools and knowledge required to make your systems faster and less resource-hungry. Efficient Go guides you in achieving better day-to-day efficiency using Go. In addition, most content is language-agnostic, allowing you to bring small but effective habits to your programming or product management cycles.This book shows you how to: Clarify and negotiate efficiency goalsOptimize efficiency on various levelsUse common resources like CPU and memory effectivelyAssess efficiency using observability signals like metrics, logging, tracing, and (continuous) profiling via open source projects like Prometheus, Jaeger, and ParcaApply tools like go test, pprof, benchstat, and k6 to create reliable micro and macro benchmarksEfficiently use Go and its features like slices, generics, goroutines, allocation semantics, garbage collection, and more!

Every enterprise application creates data, including log messages, metrics, user activity, and outgoing messages. Learning how to move these items is almost as important as the data itself. If you're an application architect, developer, or production engineer new to Apache Pulsar, this practical guide shows you how to use this open source event streaming platform to handle real-time data feeds.Jowanza Joseph, staff software engineer at Finicity, explains how to deploy production Pulsar clusters, write reliable event streaming applications, and build scalable real-time data pipelines with this platform. Through detailed examples, you'll learn Pulsar's design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the load manager, and the storage layer.This book helps you:Understand how event streaming fits in the big data ecosystemExplore Pulsar producers, consumers, and readers for writing and reading eventsBuild scalable data pipelines by connecting Pulsar with external systemsSimplify event-streaming application building with Pulsar FunctionsManage Pulsar to perform monitoring, tuning, and maintenance tasksUse Pulsar's operational measurements to secure a production clusterProcess event streams using Flink and query event streams using Presto

Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models.Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will:Review classical time series applications and compare them with deep learning modelsExplore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learningImprove market risk models (VaR and ES) using ML techniques and including liquidity dimensionDevelop a credit risk analysis using clustering and Bayesian approachesCapture different aspects of liquidity risk with a Gaussian mixture model and Copula modelUse machine learning models for fraud detectionPredict stock price crash and identify its determinants using machine learning models

Get a comprehensive overview on how to set up and design an effective database with MySQL. This thoroughly updated edition covers MySQL's latest version, including its most important aspects. Whether you're deploying an environment, troubleshooting an issue, or engaging in disaster recovery, this practical guide provides the insights and tools necessary to take full advantage of this powerful RDBMS.Authors Vinicius Grippa and Sergey Kuzmichev from Percona show developers and DBAs methods for minimizing costs and maximizing availability and performance. You'll learn how to perform basic and advanced querying, monitoring and troubleshooting, database management and security, backup and recovery, and tuning for improved efficiency. This edition includes new chapters on high availability, load balancing, and using MySQL in the cloud.Get started with MySQL and learn how to use it in productionDeploy MySQL databases on bare metal, on virtual machines, and in the cloudDesign database infrastructuresCode highly efficient queriesMonitor and troubleshoot MySQL databasesExecute efficient backup and restore operationsOptimize database costs in the cloudUnderstand database concepts, especially those pertaining to MySQL

With the explosion of data, computing power, and cloud data warehouses, SQL has become an even more indispensable tool for the savvy analyst or data scientist. This practical book reveals new and hidden ways to improve your SQL skills, solve problems, and make the most of SQL as part of your workflow.You'll learn how to use both common and exotic SQL functions such as joins, window functions, subqueries, and regular expressions in new, innovative ways--as well as how to combine SQL techniques to accomplish your goals faster, with understandable code. If you work with SQL databases, this is a must-have reference.Learn the key steps for preparing your data for analysisPerform time series analysis using SQL's date and time manipulationsUse cohort analysis to investigate how groups change over timeUse SQL's powerful functions and operators for text analysisDetect outliers in your data and replace them with alternate valuesEstablish causality using experiment analysis, also known as A/B testing

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

Reactive systems and event-driven architecture are becoming indispensable to application design, and companies are taking note. Reactive systems ensure that applications are responsive, resilient, and elastic no matter what failures or errors may be occurring, while event-driven architecture offers a flexible and composable option for distributed systems. This practical book helps Java developers bring these approaches together using Quarkus 2.x, the Kubernetes-native Java framework.Clement Escoffier and Ken Finnigan show you how to take advantage of event-driven and reactive principles to build robust distributed systems, reducing latency and increasing throughput, particularly in microservices and serverless applications. You'll also get a foundation in Quarkus to help you create true Kubernetes-native applications for the cloud.Understand the fundamentals of reactive systems and event-driven architectureLearn how to use Quarkus to build reactive applicationsCombine Quarkus with Apache Kafka or AMQP to build reactive systemsDevelop microservices that utilize messages with Quarkus for use in event-driven architecturesLearn how to integrate external messaging systems, such as Apache Kafka, with QuarkusBuild applications with Quarkus using reactive systems and reactive programming concepts

We're at an inflection point in data, where our data management solutions no longer match the complexity of organizations, the proliferation of data sources, and the scope of our aspirations to get value from data with AI and analytics. In this practical book, author Zhamak Dehghani introduces data mesh, a decentralized sociotechnical paradigm drawn from modern distributed architecture that provides a new approach to sourcing, sharing, accessing, and managing analytical data at scale.Dehghani guides practitioners, architects, technical leaders, and decision makers on their journey from traditional big data architecture to a distributed and multidimensional approach to analytical data management. Data mesh treats data as a product, considers domains as a primary concern, applies platform thinking to create self-serve data infrastructure, and introduces a federated computational model of data governance.Get a complete introduction to data mesh principles and its constituentsDesign a data mesh architectureGuide a data mesh strategy and executionNavigate organizational design to a decentralized data ownership modelMove beyond traditional data warehouses and lakes to a distributed data mesh

This practical guide provides over 70 self-contained recipes to help you creatively solve common AWS challenges you'll encounter on your cloud journey. If you're comfortable with rudimentary scripting and general cloud concepts, this cookbook provides what you need to address foundational tasks and create high-level capabilities.Authors John Culkin and Mike Zazon share real-world examples that incorporate best practices. Each recipe includes a diagram to visualize the components. Code is provided so that you can safely execute in an AWS account to ensure solutions work as described. From there, you can customize the code to help construct an application or fix an existing problem. Each recipe also includes a discussion to provide context, explain the approach, and challenge you to explore the possibilities further.Go beyond theory and learn the details you need to successfully build on AWS. The recipes help you:Redact personal identifiable information (PII) from text using Amazon ComprehendAutomate password rotation for Amazon RDS databasesUse VPC Reachability Analyzer to verify and troubleshoot network pathsLock down Amazon Simple Storage Service (S3) bucketsAnalyze AWS Identity and Access Management policiesAutoscale a containerized service

Get quick hands-on experience with Google Cloud. This cookbook provides a variety of self-contained recipes that show you how to use Google Cloud services for your enterprise application. Whether you're looking for practical ways to apply microservices, AI, analytics, security, or networking solutions, these recipes take you step-by-step through the process and provide discussions that explain how and why the recipes work.Ideal for system engineers and administrators, developers, network and database administrators, and data analysts, this cookbook helps you get started with Google Cloud regardless of your level of experience. Google veterans Rui Costa and Drew Hodun also cover advanced-level Google Cloud services for those who have appreciable experience with the platform.Learn how to get started with Google CloudUnderstand the depth of services Google Cloud providesGain hands-on experience using practical examples and labsExplore topics that include BigQuery, Cloud Run, and KubernetesBuild and run mobile and web applications on Google CloudExamine ways to build your cloud applications for scaleBuild a minimum viable product (MVP) app to use in productionLearn data platform and pipeline skills