Allgemein
IT-Service-Management in der Praxis mit ITIL® (7.Auflg.)
Was Sie für die Foundation-Zertifizierung über ITIL® wissen müssen Ein Überblick über ITIL® sowie ergänzende Standards und Methoden Wie Sie IT-Service-Management erfolgreich gestalten und verankern Zahlreiche Praxistipps und eine umfangreiche Fallstudie Neu in der 7. Auflage: Alle wichtigen Fakten zu ITIL® Version 5 und Bewertung der wichtigsten Änderungen Die IT hat sich zu einem zentralen Erfolgsfaktor für funktionierende Geschäftsprozesse entwickelt. Das verlangt von IT-Organisationen, immer schneller veränderten Anforderungen gerecht zu werden. IT-Verantwortliche können diese Aufgabe meistern, wenn sie auf modernes IT-Service-Management setzen. Hier wird Ihnen gezeigt, wie Sie IT-Service-Management praxisgerecht planen und realisieren. Sie erfahren, wie Sie ITIL® Ihren Zielen entsprechend mit ISO 20000, IT-Kennzahlen und COBIT® 2019 und agilen Methoden richtig kombinieren und einsetzen. Als standardisierte Notation für Prozesse wird BPMN 2.0 beleuchtet. Ein ausführliches Fallbeispiel veranschaulicht, wie Sie das alles in die Praxis umsetzen und auf diese Weise kontinuierlich die Qualität der Services und die Wirtschaftlichkeit der Leistungserbringung verbessern. »Das ist ein Buch sowohl für die Praxis (ITIL Projekte stehen bevor) als auch für Schulungs-Teilnehmer, die sich auf eine ITIL Foundation Prüfung vorbereiten wollen. Das Buch zeigt, wie IT-Service Management mit ITIL® in der Praxis geplant und realisiert werden und wie eine Verzahnung mit weiteren Good Practices Ihren Zielen entsprechend kombiniert werden kann.« it Service Management (itSMF Deutschland e. V.) zur 3. Auflage AUS DEM INHALT // IT-Service-Management / Serviceprinzipien / ITIL® 3 / ITIL® 4 / COBIT® 2019 / ITIL® Version 5 / ISO 20000 / BPMN 2.0 / Leistung und Qualität messen / Praxisbeispiel
Maschinelles Lernen
Entdecken Sie in diesem Buch, wie Sie maschinelles Lernen für die Entwicklung von Empfehlungs- und Entscheidungssystemen praktisch einsetzen – klar erklärt, anwendungsorientiert und direkt auf Ihre Projekte übertragbar. Sie erhalten eine umfangreiche Sammlung anschaulicher Beispiele aus den Bereichen Supervised und Unsupervised Learning, die Ihnen helfen, schnell die passenden Methoden für Ihre Anforderungen zu finden. Ob Sie etwa ein Fraud-Erkennungssystem entwickeln möchten oder nach dem optimalen Algorithmus samt Parameterwahl suchen – dieses Buch liefert konkrete Antworten. Ein weiterer Schwerpunkt liegt auf dem Design und der Umsetzung von Empfehlungssystemen. Schritt für Schritt erfahren Sie, wie zum Beispiel Filmempfehlungen mit gängigen Machine-Learning-Verfahren aufgebaut werden, welche Stärken und Grenzen die einzelnen Algorithmen haben und worauf es in der Praxis wirklich ankommt. Jedes Kapitel verbindet kompakte theoretische Grundlagen mit nachvollziehbaren Pilotimplementierungen und gut dokumentierten Python-Codebeispielen auf Basis von scikit-learn. Zum Abschluss lernen Sie alle Schritte kennen, die notwendig sind, um Machine-Learning-Lösungen erfolgreich in industrielle Prozesse zu integrieren – damit Ihre Modelle nicht nur funktionieren, sondern echten Mehrwert schaffen.
Mobile Systems
Explains how mobile systems are developed and used from start to finish. Provides a solid foundation and offers practical insights for both academic study and professional practice. Presents strategies and proven methods for developing successful and sustainable mobile systems. Introduces new developments, trends, and technologies that will shape the future of the mobile world. “Mobile Systems – Concept, Development, and Operation” is a comprehensive foundational work that delivers in-depth knowledge of mobile technologies, their development, and their practical application. It covers both technical fundamentals and advanced application areas, addressing the entire life cycle of mobile systems. Topics include user experience design, development strategies, application management, green IT, XR technologies, mobile security, and futureoriented subjects such as the mobile metaverse. The aim is to familiarize students of computer science, business informatics, and media informatics, as well as IT managers, with the characteristics, opportunities, and challenges of mobile ecosystems. Readers will learn how to apply mobile technologies in a targeted and sustainable way. The book prepares them to implement innovative mobile projects across various industries. WHAT‘S INSIDE // Mobile Systems: Components and Core Technologies Mobile Devices: Classes, Technology, and Infrastructure Mobile Development Frameworks: Native, Cross-Platform, and Hybrid Mobile User Experience (UX) Mobile Application Lifecycle Management (ALM) and Mobile Application Management (MAM) Mobile Security: Risks and Prevention Mobile Artificial Intelligence (AI) Mobile Business: Business Models and the Global Market Mobile XR and the Mobile Metaverse Green IT and Green Coding Technology Assessment and Sociocultural Implications
Pro State Management in JavaScript
Modern front-end teams face heavy toolchains and abstraction sprawl, while many developers lack a ‘first principles’ understanding of state engines. Pro State Management in JavaScript takes you under the hood, showing you how to build a simple browser-native state management library which tackles dependencies, observability, and inspectability, giving you a practical measure to compare the most popular state management engines of today. This book will teach you to harness the browser’s native strengths like events, the DOM, and closures to craft a minimalist, understandable state management library. Giving you a deep understanding so you can ship a working engine adaptable to real web-based applications. Split into four parts, the book starts by demystifying the event model, exploring topics like capture/target/bubble, DOM vs. custom events, delegation, and ergonomics of closures and module patterns. Part II builds the state engine step-by-step making trade-offs explicit along the way, including micro-benchmarks and surface performance tips. Part III rebuilds popular paradigms to sharpen your intuition. We reconstruct core ideas behind Redux, MobX, Zustand, Recoil and XState (statecharts and transitions) - then compare each to our event-driven engine in terms of clarity, ergonomics, and runtime costs. The final part provides you with projects relating to a searchable/paginated data-table, dashboard widgets with cross-component coordination, and an interactive tree list component. By the end of this book, you will know the “why” behind these libraries by building them in miniature, leaving you with a comparative mental model you can apply in any stack. You Will: Learn to design, implement, and ship a production-ready state engine in plain JavaScriptUnderstand and Explore Redux, MobX, Zustand, Recoil and XStateExplore dependencies, observability, and inspectability, by using browser-native state management libraries This Books is for: Intermediate to advanced level developers and front-end engineers who are looking to turbo charge their understanding of state management and engines with the rights tools, frameworks and thinkings models to implement successfully.
Digital AI Transformation
Artificial intelligence is no longer an emerging capability—it is reshaping how organizations compete, operate, and grow. Yet many organizations struggle to move beyond experimentation toward sustained, enterprise‑wide impact. Digital AI Transformation provides a clear, practical guide to navigating this shift with confidence, discipline, and purpose. This book introduces digital AI transformation as a strategic evolution, not a technology project. It traces the rise of AI as a defining force across business, society, and technology, while grounding that context in practical decision‑making. Readers build a strong foundation through accessible explanations of AI and machine learning concepts that matter at the enterprise level—without requiring a technical background. The book also explores the impact of AI across business functions such as Finance, HR, Marketing, and Operations. Distinctively, the book places equal emphasis on people and systems. Successful transformation depends not only on models and platforms, but on leadership, operating models, and a culture capable of continuous learning. The book explores how organizations can develop the skills, mindset, and governance needed to responsibly adopt AI at scale. Advanced sections address enterprise‑ready architectures, including AI landing zones and multi‑agent systems, demonstrating how coordinated AI capabilities can solve complex organizational and real‑world challenges. The closing chapters look ahead, identifying emerging trends and strategic inflection points while providing a practical roadmap to help organizations anticipate disruption and act ahead of competitors. What sets this book apart is its integrated perspective. It combines strategic foresight, hands‑on guidance, and ethical responsibility—connecting technology decisions to measurable business impact. The approach aligns enterprise AI strategy with execution, measurement, and continuous improvement, delivered in a clear and engaging executive voice. What You Will Learn Evaluate enterprise readiness and shape transformative AI strategies that redefine growthBuild agile, future‑ready organizations empowered by innovation, talent, and intelligent operating modelsUnderstand emerging trends shaping enterprise AI, from multi‑agent systems to industry‑wide disruptionLead with vision and conviction to convert AI opportunities into lasting enterprise value Who This Book is For Whether you are a C-suite executive, digital strategist, innovator, or a professional preparing for tomorrow’s careers, this book equips you with the insight and practical wisdom to navigate and excel in the AI-driven era.
Troubleshooting and Supporting Windows 11
Diagnose, troubleshoot and repair any type of problem on your PC from startup and file access to cloud services and the challenges created by hybrid work. This book contains everything you need to know to keep PC systems running optimally, and to repair problems quickly and efficiently. This book provides a deep dive into the Windows OS, detailing what everything is, and how it works. You will learn of the in-built, additional, and third-party tools and utilities you can use to create reliable, robust and secure PC systems. This updated version will guide you with the latest advancements in Windows 11 and using AI tools for troubleshooting and managing Windows security. This book starts with a deep dive into the Windows OS, detailing what everything is, and how it works. You will learn of the in-built, additional, and third-party tools and utilities you can use to create reliable, robust and secure PC systems. Further, you will learn how to configure Windows 11 so as to avoid problems occurring, and how to support every type of end user, working from home, or in any part of the globe, speaking any language, and taking into account other factors such as ability or personal barriers. You will discover the support tools and support ecosystem you can use to create and manage effective support tracking and remote access. You will discover how to get detailed events and reliability information, and how to manage updating channels. You will deep dive into Windows 11 operating system and folder structure and learn app and software troubleshooting, process and service troubleshooting, network and internet troubleshooting and hardware and peripherals troubleshooting. Finally, you will learn advanced troubleshooting techniques like security and encryption troubleshooting and using PowerShell scripting to repair problems. By the end, you will know how to troubleshoot complex problems and diagnose hardware problems in a PC. You will be able to troubleshoot and repair any type of problem on a Windows 11 PC. New to the book: Using AI to troubleshoot and repair problemsManaging Windows security featuresSupporting Windows 11 on older Windows 10 PCs What You Will Learn How to support home and hybrid workers using their own PCsUsing scripting and PowerShell to troubleshoot and repair systemsManaging networking and internet access to minimize downtimeManaging installation and troubleshooting for updates and patchesHow to use Artificial Intelligence tools to troubleshoot and repair problems Who This Book Is For IT Pros and system administrators who have to maintain small or large networks of connected PCs locally at their organization, or with hybrid workers.
Governance-First AI
As Generative AI shifts from early excitement to real-world implementation, organizations face a critical crossroads. Executives demand rapid returns on investment, yet deployment is frequently stalled by risk, compliance, and governance barriers, and the rise of unmanaged Shadow AI systems has elevated the stakes. Governance-First AI confronts this tension head‑on, offering a practical, execution-ready guide for leaders seeking to move beyond hype and into secure, scalable adoption. This book bridges the gap with a unified, governance‑first approach that enables enterprises to operationalise Generative AI confidently and responsibly. It confronts a landmark MIT NANDA finding — that 95% of enterprise generative AI initiatives deliver no measurable return — and traces the failure to its root: a systemic disconnect between solution architecture and enterprise governance.The authors present a cohesive framework for achieving Certified Operational Velocity, helping organisations evolve from experimental pilots to compliant, production-grade systems. At its core, the book translates stringent governance principles—including platform engineering and platform‑as‑a‑product thinking—into fifteen concrete architectural patterns. These blueprints address the full spectrum of enterprise AI challenges, from retrieval-augmented generation (RAG) deployment to context management, legacy system integration, and the auditable controls required for autonomous Agentic AI systems. Each chapter delivers step-by-step guidance for building secure, measurable, and future-proof AI capabilities. By introducing a unified governance-first architecture and pairing it with repeatable, production-ready use cases, Governance-First AI becomes the missing guide for unlocking AI’s true impact. It equips organizations to achieve transformative productivity gains—without compromising compliance—while preparing for the era of safe, autonomous AI. What you will learn: Implement a unified, governance‑first architectural framework to reliably scale Generative AI from POC experiments to fully compliant enterprise systems.Apply 15 production‑ready architectural blueprints to unlock significant value and achieve 70–80% efficiency gains across core enterprise use cases.Enforce essential technical governance controls—such as DLAC, retrieval‑time access checks, and immutable audit logs—to ensure complete auditability and legal defensibility.Design and deploy autonomous Agentic AI systems with strict safeguards, including policy‑as‑code enforcement, accountability layers, and robust human‑in‑the‑loop protocols. Who this book is for: The book is designed for C‑suite leaders—including CDOs, CTOs, CIOs, CISOs—along with Enterprise Architects and Directors of AI/ML Engineering who are responsible for scaling AI across the enterprise. It also serves Compliance Officers, Risk Managers, and senior ML engineers deploying LLMs in highly regulated sectors such as finance, pharma, manufacturing, and legal. Readers should have foundational knowledge of enterprise IT or cloud architecture and a basic understanding of Generative AI and LLMs.
Sicher im digitalen Alltag
Digitale Sicherheit muss nicht kompliziert sein. Phishing-Mails, gehackte Konten, falsche Bankmitarbeiter, Datenlecks, Identitätsdiebstahl und inzwischen sogar täuschend echte KI-Stimmen und Deepfakes gehören zum digitalen Alltag. Doch um sich wirksam zu schützen, muss man kein IT-Experte sein. "Sicher im digitalen Alltag" erklärt verständlich und ohne unnötiges Technik-Blabla, welche Schutzmaßnahmen wirklich wichtig sind. Du erfährst, wie du deine wichtigsten Konten absicherst, Passwörter sinnvoll verwaltest, Zwei-Faktor-Authentifizierung und Passkeys nutzt und typische Betrugsmaschen rechtzeitig erkennst. Konkrete Praxisfälle zeigen, was bei verdächtigen Nachrichten, übernommenen Konten, falschen Zahlungsaufforderungen oder einem verlorenen Smartphone zu tun ist. Checklisten, Sicherheitswerkstätten und einfache Notfallpläne helfen dabei, das Gelernte direkt im eigenen Alltag umzusetzen. Ein verständlicher Ratgeber für alle, die ihre digitale Sicherheit verbessern möchten - ohne Angst, Fachchinesisch und ständige Paranoia.
Controlling Implementation in SAP S/4HANA
Controlling in SAP S/4HANA represents a fundamental shift from the classic structures and logic of SAP ECC. Many familiar concepts have been modernized—or entirely reimagined—to meet current business demands and align with SAP’s evolving technological landscape. For many organizations, the transition to SAP S/4HANA creates a rare opportunity to redesign fragmented Controlling landscapes, harmonize processes, simplify architectures, and establish a future-ready foundation aligned with SAP Best Practices and SAP’s strategic roadmap. However, these opportunities can only be realized when Controlling is approached not merely as a configuration task, but as a strategic design domain directly linked to business value creation. Controlling Implementation in SAP S/4HANA: From Strategic Design to Business Value provides a structured framework for making key design decisions that shape long-term system architecture, enterprise steering capabilities, reporting transparency, and operational agility. The book explains the conceptual and functional changes introduced in SAP S/4HANA Controlling through direct comparisons with SAP ECC and demonstrates how strategic design choices influence transformation success far beyond technical go-live. Designed for project and workstream leads, solution architects, SAP consultants, and client-side stakeholders, this book helps readers make informed Controlling design decisions that support scalable system architecture and measurable business value. Readers Will Gain: A clear understanding of the strategic challenges and critical design topics in SAP S/4HANA Controlling A structured framework for evaluating key design decisions and their impact on architecture, governance, and process harmonizationPractical guidance for aligning Controlling solutions with SAP Best Practices and SAP’s long-term direction The ability to make informed, future-proof decisions that support scalable architecture and long-term business value This Book is for: SAP S/4HANA project and Controlling leadsSAP S/4HANA system architectsSAP S/4HANA CO consultantsClient-side project management teamsCorporate and business unit controllers on the client side
Introduction to AI Implementation for Managers
This book is a practical guide designed for leaders, managers, and teams who want to adopt AI, not as hype, but as a real, responsible business advantage. Instead of overwhelming readers with jargon and complex theories, it breaks AI down into clear concepts, actionable frameworks, and practical steps to help organizations move from curiosity to confident implementation. The book begins by explaining what AI truly is and isn’t, cutting through media noise and inflated expectations. It explores the real limitations and risks of AI that leaders must understand before jumping in. With that foundation in place, the core framework unfolds across three essential pillars: organizational structure, technical readiness, and human adoption. Readers discover how to build safe environments, align strategy with execution, and foster trust and motivation within teams. Real-world examples and everyday use cases illustrate how AI already shapes work, decision-making, and productivity, and how organizations can stay current without feeling overwhelmed by rapid change. The book concludes by preparing leaders for the future of AI-driven work, emphasizing thoughtful, human-centered leadership. With clear steps and a forward-looking mindset, this book empowers organizations to embrace AI confidently and responsibly. One practical step at a time. What you will learn: Learn a practical three-pillar framework (organizational, technical, and human) to build AI capability that scales responsiblyGain tools to align AI strategy with business goals, build safe technical environments, and foster trust and adoption across teamsExplore real AI use cases, workflows, and daily applications that drive productivity and decision-making in modern workplacesDevelop a future-ready mindset to lead confidently in an AI-driven world, with clear next steps and a human-centered perspective Who this book is for: This book is designed for business leaders, managers, and decision-makers who want to adopt AI confidently without needing deep technical skills. It is also suited for transformation teams, product managers, and operations leaders looking to build AI-ready organizations and drive practical, ethical, and scalable AI adoption across their companies.
Mastering the Microsoft Agent Framework
Design and build enterprise AI agents using the Microsoft Agent Framework with .NET and C#. The Microsoft Agent Framework is Microsoft's foundation for building AI agents on .NET. It unifies the ideas that started in Semantic Kernel and AutoGen and adds what a production system actually needs. This book teaches it from the ground up, one working agent per chapter, from your first conversational agent to agents that call each other and run in the cloud. You start simple: deploy a model, connect your first agent, and understand how models, providers, and credentials fit together. Then you add the capabilities that make an agent genuinely useful. You give it tools so it can call your code. You give it memory so it remembers, both within a conversation and after a restart. You ground it in your own documents so it answers from real sources, with citations. And you put a human in the loop, so risky actions happen only after someone approves them. From there, the book goes further. You connect your agent to outside tools and data through the Model Context Protocol, and package reusable capabilities as skills. You learn to coordinate several agents at once: running them in sequence, in parallel, handing off between them, or letting them collaborate in a group. Then you make agents production-ready, adding guardrails, tracing what they do with OpenTelemetry and Azure Application Insights, and measuring their quality with evaluation. Finally, you connect agents to each other and host them on Azure, and close with a chapter of hard-won best practices for building agents that last. Everything in the book runs. Every code listing is a real program that builds and executes, and a companion GitHub repository gives you the complete source for every chapter. The book pins one framework version throughout, so the code works exactly as written instead of drifting out of date. Each chapter follows the same practical rhythm: here's the problem, here's the code, here's it running, and here's what to watch for in production. What You Will Learn: Deploy a model and give your agent tools to call your own codeAdd memory, ground answers in your documents with retrieval, and require human approval for risky stepsExtend agents with the Model Context Protocol and reusable skillsCoordinate multiple agents: sequential, concurrent, handoff, group chat, and Magentic orchestrationAdd guardrails and trace agents with OpenTelemetry and Azure Application InsightsEvaluate agent quality, connect agents to each other, and host agents in the cloud with Azure Who This Book Is For: .NET developers who want to build real AI agents with C#. You should be comfortable with C# and have used a cloud service before. No machine learning background is needed, you deploy and call models, you don't train them.
The Database Safari
Pack your field kit—this is a guided safari through today’s database ecosystem. In the wild, one size rarely fits all: Modern systems often need specialized databases to deliver real‑world performance and scale. This book is a practical field guide to those less commonly used databases—vector, time‑series, graph, columnar/OLAP, geospatial, event‑stream, and more. It shows you how each engine works, where it excels, and how to choose the right engine for each workload. The book provides clear trade‑off tables, indexing strategies, query patterns, and use‑case recipes you can apply immediately. Starting with foundations in SQL and NoSQL, this book explores each “species” chapter by chapter, emphasizing hands‑on scenarios and pitfalls to avoid. It then brings everything together in polyglot/hybrid architectures, helping you combine engines confidently, whether you’re designing a similarity search with vector databases (ANN), building observability pipelines on time‑series data and event logs, modeling relationships in graph databases, or delivering analytics with columnar/OLAP and geospatial queries. The result is a concise, engineer‑first map of the database landscape that turns theory into pragmatic decision‑making. What You Will Learn Identify the right engine for the job across vector, time‑series, graph, columnar/OLAP, geospatial, and event‑stream databasesEvaluate trade‑offs in performance, scalability, consistency, storage layout, indexing, and operational complexityDesign polyglot architectures that combine multiple engines (e.g., vector + OLAP + event‑stream) for end‑to‑end solutionsApply use‑case patterns for similarity search (ANN), observability metrics, fraud/network analysis, geospatial routing, and analytical reportingUnderstand core internals that matter (index types, retention policies, log‑structured storage, spatial indexes, columnar execution) without vendor lock‑inAvoid common pitfalls in schema design, query planning, and cross‑engine data movementBuild a decision checklist and trade‑off tables to justify engine selection to stakeholders Who This Book Is For Software engineers; back-end developers; data engineers; system architects; site reliability engineers (SREs); technical leads with basic SQL/NoSQL experience who need practical guidance to select, compare, and combine specialized databases for real‑world applications
Praxiswissen Softwarearchitektur für KI-Systeme
Das Buch zum Lehrplan »Certified Professional for Software Architecture – Softwarearchitektur für KI-Systeme« Grundlagen moderner Softwarearchitekturen für KI-Systeme Entwicklung skalierbarer, wartbarer und erweiterbarer KI-Lösungen Praktische Ansätze zur Integration von KI in bestehende IT-Landschaften In diesem Buch lernen Sie die zentralen Architekturkonzepte, die für den Entwurf und die Umsetzung von KI-Systemen notwendig sind, kennen. Es richtet sich an Softwareentwickler, Softwarearchitekten und Data Scientists, die Machine-Learning- und Generative-KI-Lösungen entwickeln und in bestehende Systeme einbinden wollen. Die Autoren waren maßgeblich an der Entstehung des SWARC4AI-Lehrplans beteiligt. Sie zeigen Ihnen, wie Sie grundlegende Prinzipien der Softwarearchitektur auf KI-Anwendungen anwenden können – von der Analyse spezifischer Qualitätsanforderungen über den Umgang mit Daten- und Modelllebenszyklen bis zur strukturierten Integration von KI-Komponenten in bestehende Softwarelandschaften. Neben den theoretischen Grundlagen lernen Sie Architektur-Patterns kennen, die im KI-Kontext besonders relevant sind. Dazu gehören Muster für Datenpipelines, Trainings- und Bereitstellungsarchitekturen, Modellverwaltung sowie das Zusammenspiel klassischer Komponenten mit KI-basierten Bausteinen. Anhand praxisnaher Beispiele lernen Sie, KI-Systeme so zu gestalten, dass sie robust, erweiterbar und langfristig wartbar bleiben – zentrale Anforderungen in einer dynamischen technologischen Umgebung.
F*ckups
Alles, was schiefgehen kann, ist bereits irgendwo schiefgegangen. Sie haben die Nase voll von der schönen neuen IT-Welt voller Best Practices, Clean Code und Five Nines? Dann schauen Sie sich ganz entspannt an, was alles nicht funktioniert hat – und was sich daraus lernen lässt. Die Fallbeispiele dieser forensischen Untersuchung des Scheiterns decken auf, was wir schon längst vermutet haben: Computer sind Diven, die dummerweise immer genau das tun, was man ihnen sagt. Festplatten haben ein feines Gespür für Dramatik und geben am liebsten dann den Geist auf, wenn das letzte Backup viel zu lange her ist. Das Internet ist ein Kartenhaus, das auf einem wackeligen Fundament gutgemeinter Annahmen, viel Bürokratie und einer großen Portion Hoffnung steht. Dieses Buch ist für alle Entwicklerinnen, Admins, SREs und DevOps-Engineers, die wissen, dass auf die Nase fallen Teil der Arbeit ist, und die in Fehlern eine Chance sehen, etwas zu lernen. Warum Backups nie funktionieren, Ausfallsicherheit ein Mythos ist und DNS immer schuld ist. Redundanz und Resilienz sind schön und gut, aber letztendlich doch nur Aufforderungen an die Welt sich neue Formen des Scheiterns zu überlegen. Am Ende werden Sie verstehen, warum Murphy immer Recht behalten wird. 96 Lessons Learned, basierend auf über 500 Quellen 28 Konzepte und Ideen erklärt, ohne Kauderwelsch und Fachchinesisch.
Designing Self-Healing Systems with .NET
Software systems fail. The expensive part is not the failure itself but the recovery: manual restarts, replayed jobs, drained queues, and configuration changes performed under pressure at inconvenient hours. Most teams already know the corrective actions for their common incidents. What they lack is an architecture that can apply those actions without waiting for a human to intervene every time. This book brings recovery into the architecture. It treats self-healing as a design discipline and provides a reference architecture that organizes detection, decision, safety, execution, and verification into a coherent control loop. It then delivers a concrete reference implementation in .NET, mapping every architectural layer to explicit contracts and components that teams can study, adapt, and extend. The architecture is grounded in production experience on a large distributed platform that connected dozens of public administrations through document exchange flows. As external integrations multiplied, each new dependency brought failure modes that demanded their own detection logic and recovery strategies. The design described in these pages emerged from that growth, and the anecdotes, case studies, and decisions throughout the text reflect that origin. The treatment goes beyond cloud-native microservices. It covers monoliths, legacy systems, hybrid environments, batch processing, messaging, and queue-centered workloads. It addresses the operating model as well: governance, safety boundaries, adoption roadmap, anti-patterns, and organizational roles. No existing title combines a self-healing reference architecture with a .NET reference implementation, and I believe this book addresses a gap that practicing .NET architects will recognize. What You Will Learn: Design and implement a self-healing reference architecture in .NET, with explicit contracts for observation, detection, decision, safety, execution, and verification.Classify failure modes (stall, degradation, traffic absence, gray failure) and build composite detection rules that reduce false positives while catching real conditions.Design bounded recovery actions (restart, degraded mode, quarantine, replay, credential rotation) with safety guards, cooldowns, action budgets, and verification gates.Apply self-healing patterns across different deployment topologies: single deployable, API-worker pair, queue-centered tier, modular monolith, distributed services, and hybrid/legacy environments.Build the organizational operating model behind self-healing: governance, adoption roadmap, maturity assessment, incident integration, and team roles. Who This Book is For The primary audience is .NET software architects and senior developers who design and operate backend systems, distributed services, messaging platforms, and integration layers. These professionals deal with production reliability challenges and are looking for a structured approach to automated recovery beyond ad-hoc retry logic and restart scripts.
KI, Statistik und Anwendungen
In einer Zeit, in der Künstliche Intelligenz allgegenwärtig erscheint und gleichzeitig viel Halbwissen kursiert, bietet dieses kompakte Lehrbuch eine fundierte und verständliche Einführung in die Grundlagen der KI – mit einem besonderen Fokus auf Anwendungen und statistische Methoden. Ausgehend von einfachen Excel-Beispielen bis hin zu komplexeren Konzepten wie dem Perceptron-Paradigma, neuronalen Netzen und Clustering, führt der Autor Schritt für Schritt durch zentrale Themen der KI. Dabei bleibt er – wo möglich – im Rahmen der Schulmathematik und ergänzt notwendige Kenntnisse in R und Matrizenrechnung in übersichtlichen Anhängen. Zahlreiche Übungen fördern durch das eigenständige Lernen und Vertiefen das Verständnis. Dieses Buch richtet sich nicht nur an Studierende der Wirtschaftsinformatik und BWL, sondern auch an alle, die sich kritisch und fundiert mit KI auseinandersetzen möchten.
Medien und Musik
Medien und Musik gehören einfach zusammen. Radio und Fernsehen, Werbung und Videos, Smartphones und Streamingdienste sorgen dafür, dass Musik uns ständig begleitet: Von den 16 Stunden am Tag, die wir nicht schlafen, hören wir im Durchschnitt mindestens fünf Stunden Musik. Dieses Buch thematisiert die wichtigen Musikmedien und ihre Inhalte. Zugleich ist das Buch ein Streifzug durch die Entwicklung und die Geschichte von Musik in den Medien: Es geht auf wichtige Ereignisse, Erkenntnisse und Anekdoten ein und macht die Entwicklung und den Wandel medialer Musik von den Anfängen der Tonaufzeichnung bis zu den aktuellsten Phänomenen nachvollziehbar. Für die zweite Auflage haben die Autoren alle Kapitel grundlegend überarbeitet und aktualisiert. Dazu haben sie auch aktuellste Entwicklungen wie das Aufkommen von Künstlicher Intelligenz integriert.
Large-Scale Agile Frameworks
Warum sollten Sie sich mit Large-Scale Agile Frameworks auseinandersetzen? Agilität in Unternehmen und Organisationen gewinnt zunehmend an Bedeutung. Dieses Buch unterstützt Sie mit praxisnahen Lösungen und Werkzeugen zur übergreifenden Priorisierung von Anforderungen zur Software-Entwicklung. Ermöglichen Sie bestmögliche digitale Lösungen und etablieren einen optimalen Informationsfluss in Ihrer Organisation.Mit agilen Strukturen, Prozessen und Workflows - den Large-Scale Agile Frameworks - werden Organisationen anpassungsfähiger. Damit können sie schneller auf Veränderungen reagieren und werden wettbewerbsfähiger. Wissenschaftsorientiert werden Large-Scale Agile Frameworks im Kontext des Large-Scale Agile Development, agiler Methodik und Tools beleuchtet. Mit Domain Driven Design, dem Scaled Agile Framework (SAFe) und dem Spotify Engineering Model werden drei bekannte Vertreter vorgestellt. Dabei sind aktuelle Technologiethemen wie Cloud, IT Security und die organisatorischen Anforderungen im Hinblick auf Compliance und IT-Security reflektiert.
Azure DocumentDB in Practice
Azure DocumentDB in Practice is your complete guide to understanding and leveraging Microsoft’s unified document database platform, combining the power of Cosmos DB vCore with DocumentDB OSS capabilities. This book walks you through the full architecture of Azure DocumentDB, from its global foundations and MongoDB API compatibility to the vCore execution model that simplifies scaling, performance planning, and cost predictability. Moving beyond fundamentals, you’ll dive deep into performance engineering, data modeling, application development, and workload optimization tailored to Azure DocumentDB’s architecture. You will then learn to benchmark and test workloads, build resilient applications using MongoDB drivers, and implement scalable, high-availability designs. Dedicated chapters on migration strategies help you transition workloads from self-hosted MongoDB, Cosmos DB RU-based models, or other databases with minimal friction. Real-world patterns, industry use cases, and reference architectures give you the blueprints needed to solve complex problems at scale. You’ll learn how compute, storage, and networking layers work together, how to secure and govern your data with enterprise-grade identity and compliance features, and how to provision, size, and manage clusters for production workloads. With clear explanations, hands-on guidance, and practical best practices, this book equips you to confidently operate Azure DocumentDB in cloud-native, highly distributed environments. Whether you’re a developer, architect, or database engineer, Azure DocumentDB in Practice provides the technical depth and practical guidance required to design, build, and operate next-generation document database solutions and understand where the platform is headed next. What You Will Learn: Designing and deploying production grade clusters with the right sizing, networking, and securityHow to model data effectively using optimal schema, sharding, and indexing strategiesDiagnose and optimize performance using profiler, query plans, metrics, and best practicesBuild resilient applications using transactions, connection pooling, retry logic and Change streamsHow to run and operate clusters with backup/restore, monitoring, parameters Who This Book Is For: This book is meant for developers, architects, and database engineers.
Künstliche Intelligenz als digitaler Kollege
Künstliche Intelligenz kann Fragen beantworten, Texte entwerfen und Informationen zusammenfassen. Im beruflichen Alltag reicht eine gute Einzelantwort jedoch selten aus. Kundenaufträge verlangen klare Ziele, verlässliche Grundlagen, nachvollziehbare Arbeitsschritte und eine sorgfältige Prüfung der Ergebnisse.Dieses Buch zeigt dir, wie aus der gelegentlichen Nutzung von KI eine strukturierte Zusammenarbeit entsteht. Du lernst, Aufgaben verständlich zu beschreiben, den notwendigen Kontext bereitzustellen und verbindliche Qualitätskriterien festzulegen. Schritt für Schritt entwickelst du einen digitalen Kollegen, der dich bei wiederkehrenden Tätigkeiten unterstützt, ohne dir fachliche Entscheidungen und Verantwortung abzunehmen.Im Mittelpunkt stehen die Anforderungen von Freelancern, virtuellen Assistenzen und Consultants. An konkreten Beispielen erfährst du, wie sich Kundenaufträge vorbereiten, Dokumente auswerten, Inhalte entwickeln, Entscheidungen strukturieren und Ergebnisse kontrollieren lassen. Dabei geht es auch um sensible Daten, Quellenprüfung, Freigaben und den sinnvollen Einsatz von KI-Agenten.Ein 30-Tage-Plan begleitet dich beim Aufbau deines ersten eigenen KI-Arbeitsablaufs. Drei vollständig ausgearbeitete Praxisfälle und direkt einsetzbare Vorlagen helfen dir, das Gelernte auf deine Arbeit zu übertragen.Ein praxisnaher Leitfaden für alle, die KI nicht nur ausprobieren, sondern verlässlich in ihre beruflichen Abläufe integrieren möchten.
Your Digital Colleague
Artificial intelligence can draft text, summarise documents, organise information and develop ideas within seconds. But a polished answer is not yet a reliable professional result. Your Digital Colleague shows freelancers, virtual assistants and consultants how to move beyond occasional prompts and build structured, dependable workflows with AI. Step by step, you will learn how to define clear briefs, provide useful context, establish quality standards and onboard an AI tool for recurring professional tasks. The book explains how to create a shared digital workspace, prepare client work, improve communication and documents, and introduce AI agents without losing control over permissions, confidential information or important decisions. Practical examples demonstrate how AI can support research, project preparation, meeting follow-ups, client communication and the delivery of professional results. A 30-day plan, three complete workflows and a collection of reusable templates help you turn the principles into a working process of your own. Throughout the book, responsibility remains clearly with the human professional: AI provides structure, speed and practical support, while you assess the information, make the decisions and approve the final result. A hands-on guide for self-employed professionals and businesses that want to use artificial intelligence purposefully, securely and with standards they can explain to their clients.
Künstliche Intelligenz als digitaler Mitbewohner
Künstliche Intelligenz kann im privaten Alltag weit mehr sein als eine Anwendung, der man gelegentlich eine Frage stellt. Richtig eingesetzt wird sie zu einem digitalen Mitbewohner: Sie hilft dabei, Informationen zu ordnen, Termine im Blick zu behalten, Entscheidungen vorzubereiten und wiederkehrende Aufgaben leichter zu bewältigen.Dieses Buch zeigt Schritt für Schritt, wie eine verlässliche und zugleich entspannte Zusammenarbeit mit KI entstehen kann ohne technisches Vorwissen und ohne den eigenen Alltag bis ins Letzte zu optimieren. Im Mittelpunkt steht nicht die Frage, wie sich noch mehr Aufgaben in einen Tag pressen lassen. Es geht darum, Kopfraum zurückzugewinnen und mehr Zeit für die Dinge zu schaffen, die wirklich wichtig sind.Anhand konkreter Beispiele lernst du, deinen digitalen Mitbewohner sinnvoll einzuarbeiten, klare Aufträge zu formulieren und persönliche Grenzen festzulegen. Dabei geht es unter anderem um Haushaltsorganisation, Wochenplanung, Kommunikation, Finanzen, Reisen, bewussten Konsum, Hobbys und besondere Lebenssituationen. Auch Datenschutz, Vertrauen, mögliche Fehler und der verantwortungsvolle Umgang mit persönlichen Informationen werden verständlich behandelt.Ein 30-Tage-Plan, vollständige Alltagsabläufe und direkt nutzbare Vorlagen helfen dir dabei, aus einzelnen KI-Anfragen verlässliche Routinen zu entwickeln. Du entscheidest jederzeit selbst, was die KI vorbereiten darf, welche Ergebnisse du kontrollierst und welche Bereiche bewusst privat oder frei von Optimierung bleiben.Ein praxisnaher Begleiter für alle, die Künstliche Intelligenz im persönlichen Alltag sinnvoll nutzen möchten für mehr Übersicht, weniger gedankliche Belastung und echten Freiraum.
Your Digital Companion
Artificial intelligence can do far more in private life than answer the occasional question. Used thoughtfully, it can become a digital companion that helps organise information, keep track of appointments, prepare decisions and make recurring tasks easier to manage. Your Digital Companion shows you how to build this kind of reliable and relaxed collaboration with AI without technical expertise and without turning your life into a constant optimisation project. Step by step, you will learn how to formulate clear requests, introduce your digital companion to the parts of your life that matter and set boundaries around what it should and should not do. The book explores practical uses for household organisation, weekly planning, communication, personal finances, travel, mindful consumption, hobbies and exceptional situations. It also addresses privacy, trust, inaccurate results and the responsible handling of personal information. A 30-day plan, ten complete everyday processes and a collection of reusable templates help you turn occasional AI assistance into routines that genuinely reduce mental load. You remain in control at every stage: you decide what the AI may prepare, which results need checking and which areas should remain private or free from optimisation. A practical guide for anyone who wants to use artificial intelligence in everyday life to gain greater clarity, reduce mental clutter and create more time for what truly matters.