Bücher veröffentlicht von O'Reilly Media

p>If you thought data structures and algorithms were all just theory, you're missing out on what they can do for your Python code. Learn to use Big O notation to make your code run faster by orders of magnitude. Choose from data structures such as hash tables, trees, and graphs to increase your code's efficiency exponentially. With simple language and clear diagrams, this book makes this complex topic accessible, no matter your background. Every chapter features practice exercises to give you the hands-on information you need to master data structures and algorithms for your day-to-day work. Algorithms and data structures are much more than abstract concepts. Mastering them enables you to write code that runs faster and more efficiently, which is particularly important for today's web and mobile apps. Take a practical approach to data structures and algorithms, with techniques and real-world scenarios that you can use in your daily production code. The Python edition uses Python exclusively for all code examples, exercise, and solutions. Use Big O notation to measure and articulate the efficiency of your code, and modify your algorithm to make it faster. Find out how your choice of arrays, linked lists, and hash tables can dramatically affect the code you write. Use recursion to solve tricky problems and create algorithms that run exponentially faster than the alternatives. Dig into advanced data structures such as binary trees and graphs to help scale specialized applications such as social networks and mapping software. You'll even encounter a single keyword that can give your code a turbo boost. Practice your new skills with exercises in every chapter, along with detailed solutions. Use these techniques today to make your Python code faster and more scalable.

"C# is undeniably one of the most versatile programming languages available to engineers today. With this comprehensive guide, you'll learn just how powerful the combination of C# and .NET can be. Author Ian Griffiths guides you through C# 12.0 and .NET 8 fundamentals and techniques for building cloud, web, and desktop applications. Designed for experienced programmers, this book provides many code examples to help you work with the nuts and bolts of C#, such as generics, LINQ, and asynchronous programming features. You'll get up to speed on .NET 8 and the latest C# 11.0 and 12.0 additions, including generic math, new polymorphism options, enhanced pattern matching, and new features designed to improve productivity. This book helps you: Understand how .NET has changed in recent releases and learn what it means for application development; Select the appropriate C# language features for any task; Learn when to use the new features and when to stick with older ones; Examine the range of functionality in .NET's class libraries; Apply these class libraries to practical programming tasks; Explore numerous small additions to .NET that improve expressiveness"--

In today's data-driven world, understanding statistical models is crucial for effective analysis and decision making. Whether you're a beginner or an experienced user, this book equips you with the foundational knowledge to grasp and implement statistical models within Tableau. Gain the confidence to speak fluently about the models you employ, driving adoption of your insights and analysis across your organization. As AI continues to revolutionize industries, possessing the skills to leverage statistical models is no longer optional--it's a necessity. Stay ahead of the curve and harness the full potential of your data by mastering the ability to interpret and utilize the insights generated by these models. Whether you're a data enthusiast, analyst, or business professional, this book empowers you to navigate the ever-evolving landscape of data analytics with confidence and proficiency. Start your journey toward data mastery today. In this book, you will learn: The basics of foundational statistical modeling with Tableau How to prove your analysis is statistically significant How to calculate and interpret confidence intervals Best practices for incorporating statistics into data visualizations How to connect external analytics resources from Tableau using R and Python

TypeScript is a typed superset of JavaScript with the potential to solve many of the headaches for which JavaScript is famous. But TypeScript has a learning curve of its own, and understanding how to use it effectively can take time. This book guides you through 62 specific ways to improve your use of TypeScript.Author Dan Vanderkam, a principal software engineer at Sidewalk Labs, shows you how to apply these ideas, following the format popularized by Effective C++ and Effective Java (both from Addison-Wesley). Youll advance from a beginning or intermediate user familiar with the basics to an advanced user who knows how to use the language well.Effective TypeScript is divided into eight chapters:Getting to Know TypeScriptTypeScripts Type SystemType InferenceType DesignWorking with anyTypes Declarations and @typesWriting and Running Your CodeMigrating to TypeScript

"How do some organizations maintain 24-7 internet-scale operations? How can organizations integrate security while continuously deploying new features? How do organizations increase security within their DevOps processes? This practical guide helps you answer those questions and more. Author Steve Suehring provides unique content to help practitioners and leadership successfully implement DevOps and DevSecOps. Learning DevSecOps emphasizes prerequisites that lead to success through best practices and then takes you through some of the tools and software used by successful DevSecOps-enabled organizations. You'll learn how DevOps and DevSecOps can eliminate the walls that stand between development, operations, and security so that you can tackle the needs of other teams early in the development lifecycle. This book helps you: Learn why DevSecOps is about culture and processes, with tools to support the processes; Understand why DevSecOps practices are key elements to deploying software in a 24-7 environment; Deploy software using a DevSecOps toolchain and create scripts to assist; Integrate processes from other teams earlier in the software development lifecycle; Help team members learn the processes important for successful software development"--

The walls were closing in on Bruce and Maggie Tate. Isolation forced on them by the pandemic, combined with America's growing political factionalism, threatened their bonds with community and family. Something had to change. Maggie's surprising answer: buy a boat, learn to pilot it, and embark on the Great Loop. For nine months Bruce and Maggi

Bringing a deep-learning project into production at scale is quite challenging. To successfully scale your project, a foundational understanding of full stack deep learning, including the knowledge that lies at the intersection of hardware, software, data, and algorithms, is required. This book illustrates complex concepts of full stack deep learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project. A scaling effort is only beneficial when it's effective and efficient. To that end, this guide explains the intricate concepts and techniques that will help you scale effectively and efficiently. You'll gain a thorough understanding of: How data flows through the deep-learning network and the role the computation graphs play in building your model How accelerated computing speeds up your training and how best you can utilize the resources at your disposal How to train your model using distributed training paradigms, i.e., data, model, and pipeline parallelism How to leverage PyTorch ecosystems in conjunction with NVIDIA libraries and Triton to scale your model training Debugging, monitoring, and investigating the undesirable bottlenecks that slow down your model training How to expedite the training lifecycle and streamline your feedback loop to iterate model development A set of data tricks and techniques and how to apply them to scale your training model How to select the right tools and techniques for your deep-learning project Options for managing the compute infrastructure when running at scale

Data modeling is the single most overlooked feature in Power BI Desktop, yet it's what sets Power BI apart from other tools on the market. This practical book serves as your fast-forward button for data modeling with Power BI, Analysis Services tabular, and SQL databases. It serves as a starting point for data modeling, as well as a handy refresher. Author Markus Ehrenmueller-Jensen, founder of Savory Data, shows you the basic concepts of Power BI's semantic model with hands-on examples in DAX, Power Query, and T-SQL. If you're looking to build a data warehouse layer, chapters with T-SQL examples will get you started. You'll begin with simple steps and gradually solve more complex problems. This book shows you how to: Normalize and denormalize with DAX, Power Query, and T-SQL Apply best practices for calculations, flags and indicators, time and date, role-playing dimensions and slowly changing dimensions Solve challenges such as binning, budget, localized models, composite models, and key value with DAX, Power Query, and T-SQL Discover and tackle performance issues by applying solutions in DAX, Power Query, and T-SQL Work with tables, relations, set operations, normal forms, dimensional modeling, and ETL

"Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have solid benefits, but they're also surrounded by a lot of hyperbole and confusion. This practical book provides a guided tour of these architectures to help data professionals understand the pros and cons of each. James Serra, big data and data warehousing solution architect at Microsoft, examines common data architecture concepts, including how data warehouses have had to evolve to work with data lake features. You'll learn what data lakehouses can help you achieve, and how to distinguish data mesh hype from reality. Best of all, you'll be able to determine the most appropriate data architecture for your needs. With this book, you'll: gain a working understanding of several data architectures; learn the strengths and weakness of each approach; distinguish data architecture theory from the reality; pick the best architecture for your use case; understand the differences between data warehouses and data lakes; learn common data architecture concepts to help you build better solutions; explore the historical evolution and characteristics of data architectures; learn essentials of running an architecture design session, team organization, and project success factors."--

If you use Linux in your day-to-day work, then Linux Pocket Guide is the perfect on-the-job reference. This thoroughly updated 20th anniversary edition explains more than 200 Linux commands, including new commands for file handling, package management, version control, file format conversions, and more. In this concise guide, author Daniel Barrett provides the most useful Linux commands grouped by functionality. Whether you're a novice or an experienced user, this practical book is an ideal reference for the most important Linux commands. You'll learn: Essential concepts--commands, shells, users, and the filesystem File commands-creating, organizing, manipulating, and processing files of all kinds Sysadmin basics-superusers, processes, user management, and software installation Filesystem maintenance-disks, RAID, logical volumes, backups, and more Networking commands-working with hosts, network connections, email, and the web Getting stuff done-everything from math to version control to graphics and audio

"Entity resolution is a key analytic technique that enables you to identify multiple data records that refer to the same real-world entity. With this hands-on guide, product managers, data analysts, and data scientists will learn how to add value to data by cleansing, analyzing, and resolving datasets using open source Python libraries and cloud APIs. Author Michael Shearer shows you how to scale up your data matching processes and improve the accuracy of your reconciliations. You'll be able to remove duplicate entries within a single source and join disparate data sources together when common keys aren't available. Using real-world data examples, this book helps you gain practical understanding to accelerate the delivery of real business value. This book covers: challenges in deduplicating and joining datasets; extracting, cleansing, and preparing datasets for matching; text matching algorithms to identify equivalent entities; techniques for deduplicating and joining datasets at scale; matching datasets containing persons and organizations; optimizing and tuning data matching algorithms; entity resolution using cloud APIs; matching using privacy-enhancing technologies. With entity resolution, you'll build rich and comprehensive data assets that reveal relationships for marketing and risk management purposes, key to harnessing the full potential of machine learning and AI."--

Get the details, examples, and best practices you need to build generative AI applications, services, and solutions using the power of Azure OpenAI Service. With this comprehensive guide, Microsoft AI specialist Adrián González Sánchez examines the integration and utilization of Azure OpenAI Service--using powerful generative AI models such as GPT-4 and GPT-4o--within the Microsoft Azure cloud computing platform. To guide you through the technical details of using Azure OpenAI Service, this book shows you how to set up the necessary Azure resources, prepare end-to-end architectures, work with APIs, manage costs and usage, handle data privacy and security, and optimize performance. You'll learn various use cases where Azure OpenAI Service models can be applied, and get valuable insights from some of the most relevant AI and cloud experts. Ideal for software and cloud developers, product managers, architects, and engineers, as well as cloud-enabled data scientists, this book will help you: Learn how to implement cloud native applications with Azure OpenAI Service Deploy, customize, and integrate Azure OpenAI Service with your applications Customize large language models and orchestrate knowledge with company-owned data Use advanced roadmaps to plan your generative AI project Estimate cost and plan generative AI implementations for adopter companies

If you haven't modernized your data cleaning and reporting processes in Microsoft Excel, you're missing out on big productivity gains. And if you're looking to conduct rigorous data analysis, more can be done in Excel than you think. This practical book serves as an introduction to the modern Excel suite of features along with other powerful tools for analytics. George Mount of Stringfest Analytics shows business analysts, data analysts, and business intelligence specialists how to make bigger gains right from your spreadsheets by using Excel's latest features. You'll learn how to build repeatable data cleaning workflows with Power Query, and design relational data models straight from your workbook with Power Pivot. You'll also explore other exciting new features for analytics, such as dynamic array functions, AI-powered insights, and Python integration. Learn how to build reports and analyses that were previously difficult or impossible to do in Excel. This book shows you how to: Build repeatable data cleaning processes for Excel with Power Query Create relational data models and analysis measures with Power Pivot Pull data quickly with dynamic arrays Use AI to uncover patterns and trends from inside Excel Integrate Python functionality with Excel for automated analysis and reporting

Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorithms based on machine learning and reinforcement learning. Sofien Kaabar--financial author, trading consultant, and institutional market strategist--introduces deep learning strategies that combine technical and quantitative analyses. By fusing deep learning concepts with technical analysis, this unique book presents outside-the-box ideas in the world of financial trading. This A-Z guide also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization. Understand and create machine learning and deep learning models Explore the details behind reinforcement learning and see how it's used in time series Understand how to interpret performance evaluation metrics Examine technical analysis and learn how it works in financial markets Create technical indicators in Python and combine them with ML models for optimization Evaluate the models' profitability and predictability to understand their limitations and potential

Advance your Power BI skills by adding AI to your repertoire at a practice level. With this practical book, business-oriented software engineers and developers will learn the terminologies, practices, and strategy necessary to successfully incorporate AI into your business intelligence estate.

Filled with tips, tricks, and techniques, this easy-to-use book is the perfect resource for intermediate to advanced users of Excel. You'll find complete recipes for more than a dozen topics covering formulas, PivotTables, charts, Power Query, and more. Each recipe poses a particular problem and outlines a solution that you can put to use right away--without having to comb through tutorial pages. Whether you're a data analyst, project manager, or financial analyst, author Dawn Griffiths directs you straight to the answers you need. Ideal as a quick reference, Excel Cookbook is also perfect for learning how to work in a more efficient way, leading to greater productivity on the job. With this book, you'll jump in and get answers to your questions--fast. This cookbook shows you how to: Get the most out of Excel's features Address complex data problems in the best way possible Collect, manage, and analyze data from a variety of sources Use functions and formulas with ease--including dynamic array and lambda formulas Analyze data with PivotTables, Power Pivot, and more Import and transform data with Power Query Write custom functions and automate Excel with VBA

NGINX is one of the most widely used web servers available today, in part because of its capabilities as a load balancer and reverse proxy server for HTTP and other network protocols. This cookbook provides easy-to-follow examples to real-world problems in application delivery. The practical recipes will help you set up and use either the open source or commercial offering to solve problems in various use cases.For professionals who understand modern web architectures, such as n-tier or microservice designs, and common web protocols including TCP and HTTP, these recipes provide proven solutions for security, software load balancing, and monitoring and maintaining NGINXs application delivery platform. Youll also explore advanced features of both NGINX and NGINX Plus, the free and licensed versions of this server.Youll find recipes for:High-performance load balancing with HTTP, TCP, and UDPSecuring access through encrypted traffic, secure links, HTTP authentication subrequests, and moreDeploying NGINX to Google Cloud, AWS, and Azure cloud computing servicesSetting up and configuring NGINX ControllerInstalling and configuring the NGINX Plus App Protect moduleEnabling WAF through Controller ADC

With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology.

Traditional data architecture patterns are severely limited. To use these patterns, you have to ETL data into each tool--a cost-prohibitive process for making warehouse features available to all of your data. The lack of flexibility with these patterns requires you to lock into a set of priority tools and formats, which creates data silos and data drift. This practical book shows you a better way. Apache Iceberg provides the capabilities, performance, scalability, and savings that fulfill the promise of an open data lakehouse. By following the lessons in this book, you'll be able to achieve interactive, batch, machine learning, and streaming analytics with this high-performance open source format. Authors Tomer Shiran, Jason Hughes, and Alex Merced from Dremio show you how to get started with Iceberg. With this book, you'll learn: The architecture of Apache Iceberg tables What happens under the hood when you perform operations on Iceberg tables How to further optimize Iceberg tables for maximum performance How to use Iceberg with popular data engines such as Apache Spark, Apache Flink, and Dremio Discover why Apache Iceberg is a foundational technology for implementing an open data lakehouse.

If programming is magic then web scraping is surely a form of wizardry. By writing a simple automated program, you can query web servers, request data, and parse it to extract the information you need. The expanded edition of this practical book not only introduces you web scraping, but also serves as a comprehensive guide to scraping almost every type of data from the modern web.Part I focuses on web scraping mechanics: using Python to request information from a web server, performing basic handling of the servers response, and interacting with sites in an automated fashion. Part II explores a variety of more specific tools and applications to fit any web scraping scenario youre likely to encounter.Parse complicated HTML pagesDevelop crawlers with the Scrapy frameworkLearn methods to store data you scrapeRead and extract data from documentsClean and normalize badly formatted dataRead and write natural languagesCrawl through forms and loginsScrape JavaScript and crawl through APIsUse and write image-to-text softwareAvoid scraping traps and bot blockersUse scrapers to test your website

Explore the theory and practice of designing and writing serverless applications using examples from the Knative project. With this practical guide, mid-level to senior application developers and team managers will learn when and why to target serverless platforms when developing microservices or applications. Along the way, you'll also discover warning signs that suggest why serverless might cause you more trouble than joy. Drawing on author Evan Anderson's 15 years of experience developing and maintaining applications in the cloud--and more than six years of experience with serverless platforms at scale--this book acts as your guide into the high-velocity world of serverless application development. You'll come to appreciate why Knative is the most widely adopted open source serverless platform available. With this book, you will: Learn what serverless is, how it works, and why teams are adopting it Understand the benefits of Knative for cloud native development teams Learn how to build a serverless application on Knative Explore the challenges serverless introduces for debugging and the tools that can help improve it Learn why event-driven architecture and serverless compute are complementary but distinct Understand when a serverless approach might not be the right system design

"The adoption of serverless is on the rise, but until now, little guidance has been available for development teams that want to apply this technology on AWS. This definitive guide is packed with architectural, security, and data best practices and patterns for architects and engineers who want to build reliable enterprise-scale serverless solutions. Sheen Brisals, an AWS Serverless Hero, and Luke Hedger, an AWS Community Builder, outline the serverless adoption requirements for an enterprise, examine the development tools your team needs, and explain in depth the nuances of testing event-driven and distributed serverless services. You'll gain practical guidance for keeping up with change and learn how to build serverless solutions with sustainability in mind. Examine the serverless technology ecosystem and AWS services needed to develop serverless applications; learn the approach and preparation required for a successful serverless adoption in an enterprise; learn serverless architectures and implementation patterns; design, develop, and test distributed serverless microservices on AWS cloud; apply security best practices while building serverless solutions; identify and adapt the implementation patterns for your particular use case; incorporate the necessary measures for observable serverless applications; implement sustainable serverless applications in the cloud "--

Trillions of lines of code help us in our lives, companies, and organizations. But just a single software cybersecurity vulnerability can stop entire companies from doing business and cause billions of dollars in revenue loss and business recovery. Securing the creation and deployment of software, also known as software supply chain security, goes well beyond the software development process. This practical book gives you a comprehensive look at security risks and identifies the practical controls you need to incorporate into your end-to-end software supply chain. Author Cassie Crossley demonstrates how and why everyone involved in the supply chain needs to participate if your organization is to improve the security posture of its software, firmware, and hardware. With this book, you'll learn how to: Pinpoint the cybersecurity risks in each part of your organization's software supply chain Find the cybersecurity frameworks and resources that can improve security Identify the roles that participate in the supply chain--including IT, development, operations, manufacturing, and procurement Design initiatives and controls for each part of the supply chain using existing frameworks and references Evaluate third-party risk in your supply chain

All cloud architects need to know how to build data platforms that enable businesses to make data-driven decisions and deliver enterprise-wide intelligence in a fast and efficient way. This handbook shows you how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, and multicloud tools like Snowflake and Databricks. Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle from ingestion to activation in a cloud environment using real-world enterprise architectures. You'll learn how to transform, secure, and modernize familiar solutions like data warehouses and data lakes, and you'll be able to leverage recent AI/ML patterns to get accurate and quicker insights to drive competitive advantage. You'll learn how to: Design a modern and secure cloud native or hybrid data analytics and machine learning platform Accelerate data-led innovation by consolidating enterprise data in a governed, scalable, and resilient data platform Democratize access to enterprise data and govern how business teams extract insights and build AI/ML capabilities Enable your business to make decisions in real time using streaming pipelines Build an MLOps platform to move to a predictive and prescriptive analytics approach

"Join the technological revolution that's taking the financial world by storm. Mastering Bitcoin is your guide through the seemingly complex world of Bitcoin, providing the knowledge you need to participate in the internet of money. Whether you're building the next killer app, investing in a startup, or simply curious about the technology, this revised and expanded third edition provides essential detail to get you started. Bitcoin, the first successful decentralized digital currency, has already spawned a multibillion-dollar global economy open to anyone with the knowledge and passion to participate. Mastering Bitcoin provides the knowledge. You supply the passion" --

Looking for quick answers for using C# 12? This tightly focused and practical guide tells you exactly what you need to know without long intros or bloated samples. Succinct and easy to browse, this pocket reference is an ideal quick source of information. If you know Java, C++, or an earlier C# version, this guide will help you get rapidly up to speed. All programs and code snippets are available as interactive samples in LINQPad. You can edit these samples and instantly see the results without needing to set up projects in Visual Studio.

Many UX designers are surprised to learn that much of the job isn't about drawing things. It's about knowing what to draw and how to convince people to build it. Whether you're a one-person design team making products from scratch or a C-level product leader managing many products and strategies, UX for Business is your missing guide to real-world business design. You'll not only learn how to think about design as a professional but also discover how design can move the needle for your entire company. Author Joel Marsh helps you understand stakeholders, business models, the process of designing valuable solutions, dangerous choices that can ruin a product, and how to gain the attention your work deserves. You'll also explore the principles of designing common types of digital products and services, from portfolio sites to social networks to ecosystems. With this book, you'll learn: How to design the right things by understanding value, diagnostics, and probability How to conduct UX research and analysis without the luxury of time or money The most important aspects of common digital business models Methods for getting things done under less-than-ideal circumstances How to avoid common pitfalls caused by inexperience

"Traditional secret-based credentials can't scale to meet the complexity and size of cloud and on-premises infrastructure. Today's applications are spread across a diverse range of clouds and colocation facilities, as well as on-prem data centers. Each layer of this modern stack has its own attack vectors and protocols to consider."--

Baseball is not the only sport to use "moneyball." American football teams, fantasy football players, fans, and gamblers are increasingly using data to gain an edge on the competition. Professional and college teams use data to help identify team needs and select players to fill those needs. Fantasy football players and fans use data to try to defeat their friends, while sports bettors use data in an attempt to defeat the sportsbooks. In this concise book, Eric Eager and Richard Erickson provide a clear introduction to using statistical models to analyze football data using both Python and R. Whether your goal is to qualify for an entry-level football analyst position, dominate your fantasy football league, or simply learn R and Python with fun example cases, this book is your starting place. Through case studies in both Python and R, you'll learn to: Obtain NFL data from Python and R packages and web scraping Visualize and explore data Apply regression models to play-by-play data Extend regression models to classification problems in football Apply data science to sports betting with individual player props Understand player athletic attributes using multivariate statistics

OpenTelemetry is a revolution in observability data. Instead of running multiple uncoordinated pipelines, OpenTelemetry provides users with a single integrated stream of data, providing multiple sources of high-quality telemetry data: tracing, metrics, logs, RUM, eBPF, and more. This practical guide shows you how to set up, operate, and troubleshoot the OpenTelemetry observability system. Authors Austin Parker, head of developer relations at Lightstep and OpenTelemetry Community Maintainer, and Ted Young, cofounder of the OpenTelemetry project, cover every OpenTelemetry component, as well as observability best practices for many popular cloud, platform, and data services such as Kubernetes and AWS Lambda. You'll learn how OpenTelemetry enables OSS libraries and services to provide their own native instrumentation--a first in the industry. Ideal for application developers, OSS maintainers, operators and infrastructure teams, and managers and team leaders, this book guides you through: The principles of modern observability All OpenTelemetry components--and how they fit together A practical approach to instrumenting platforms and applications Methods for installing, operating, and troubleshooting an OpenTelemetry-based observability solution Ways to roll out and maintain end-to-end observability across a large organization How to write and maintain consistent, high-quality instrumentation without a lot of work.