Learning Microsoft Azure, 2nd Edition (First Early Release) (Jonah Carrio Andersson) (z-library.sk, 1lib.sk, z-lib.sk)

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Learning Microsoft Azure SECOND EDITION Cloud and AI Native Development With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles. Jonah Carrio Andersson

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Learning Microsoft Azure by Jonah Carrio Andersson Copyright © 2027 Jonah Andersson. All rights reserved. Published by O’Reilly Media, Inc., 141 Stony Circle, Suite 195, Santa Rosa, CA 95401. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (https://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com. Acquisitions Editor: Megan Laddusaw Development Editor: Jill Leonard Production Editor: Aleeya Rahman Cover Designer: Susan Brown Interior Designer: David Futato Interior Illustrator: Kate Dullea July 2027: Second Edition Revision History for the Early Release 2026-02-27: First Release See https://oreilly.com/catalog/errata.csp?isbn=9798341672468 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Learning Microsoft Azure, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc.

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The views expressed in this work are those of the author and do not represent the publisher’s views. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. 979-8-341-67242-0

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Brief Table of Contents (Not Yet Final) Preface (unavailable) Chapter 1: Cloud Computing, Edge, and AI Computing Fundamentals (available) Chapter 2: Microsoft Azure Fundamentals (available) Chapter 3: Microsoft Azure Cloud Compute Services (unavailable) Chapter 4: Microsoft Azure Cloud Networking (unavailable) Chapter 5: Microsoft Azure Cloud Storage and Databases (unavailable) Chapter 6: Microsoft Azure Cloud AI, Cognitive Services, Azure Bot Service and Machine Learning (unavailable) Chapter 7: Developing with Intelligent Applications using Azure AI: Azure Open AI, Azure AI Foundry and MCP Servers for Agentic AI Development (unavailable) Chapter 8: Cloud Security and Identity Services in Azure (unavailable) Chapter 9: Big Data, Reporting and Analytical Services in Azure (unavailable) Chapter 10: Microsoft Azure Cloud IoT (Internet of Things) and Maps Services (unavailable) Chapter 11: Microsoft Azure Cloud Integration Services (unavailable) Chapter 12: Developing with Microsoft Azure, Agentic DevOps, CI/CD, Platform Engineering, AI-Assisted Programming with Azure (unavailable) Chapter 13: Microsoft Azure Cloud Management, Cloud Governance, Responsible AI (unavailable)

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Chapter 14: Microsoft Azure Cloud Migration and Architecture Guide (unavailable) Chapter 15: Microsoft Azure Development Tools in different Programming Languages (unavailable) Chapter 16: Use Cases and Learn By Doing Hands-On Azure Projects (unavailable)

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Chapter 1. Cloud, Edge, and AI Computing Fundamentals A NOTE FOR EARLY RELEASE READERS With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles. This will be the 1st chapter of the final book. Please note that the GitHub repo will be made active later on. If you’d like to be actively involved in reviewing and commenting on this draft, please reach out to the editor at jleonard@oreilly.com. From Cloud Computing Origins to Cutting- Edge AI Innovations Before diving into everything you need to know about Microsoft Azure, it’s essential to understand the basic concepts and historical significance of cloud computing. As one of the most important technological innovations in computer science, cloud computing has indeed revolutionized the way we design, architect, develop, and host applications for different purposes today. If you work in IT, you can definitely relate to this. Computing resources such as web servers for hosting, databases, data storage, virtual machines, applications, network, security, and other IT resources deployed on- premises at its own local center with physical limitations can now be

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provided over the internet, on a Pay-As-You go basis that is accessible globally, with the flexibility to manage them from anywhere. The core idea behind cloud computing is that users can store and retrieve their data online, while the actual processing and management take place remotely in the cloud. This paradigm shift enables users to benefit from powerful computing resources without the need for extensive local infrastructure. Modernizing or developing applications hosted on a cloud computing platform, such as Microsoft Azure, requires a thorough understanding of the environment in which you are hosting the applications you build. This is regardless of any use case and problems you are trying to solve with this platform. Understanding the fundamentals of cloud computing is essential for designing, deploying, and managing solutions in today’s digital landscape. Microsoft Azure, as a leading public cloud platform, offers a secure, scalable, and reliable environment for organizations and developers to build and operate cloud-based applications. Its comprehensive suite of services and tools is tailored to encourage innovation and solve complex business challenges with efficiency and agility, while maintaining strict security and privacy standards to protect sensitive data involved in AI and other applications. By using cloud computing, businesses, organizations, and teams gain immediate access to a wide range of IT resources—such as computing, storage, integration with modern machine learning and AI tools, and data analytics—that are not readily available from traditional IT infrastructure. Using cloud computing platforms, there are potential opportunities for applications to be easily created, developed, and integrated with other systems for rapid development and deployment that can also be scaled and managed with emphasis on security, flexibility, and cost-effectiveness, enabling them to respond to evolving market demands and drive continuous innovation.

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Cloud Computing’s Journey: Igniting the AI Revolution The concept of making computing power as accessible and ubiquitous as electricity was first imagined by John McCarthy, widely regarded as the “Father of Artificial Intelligence,” in 1961. He foresaw a future where computing would be delivered as a public utility, available to all and limited only by the boundaries of human imagination. Today, this vision has become reality: cloud computing now underpins AI development, enabling organizations to create, train, and deploy AI models at scale. No longer limited to data storage, the cloud provides on-demand access to immense computing power, facilitating advanced AI solutions that solve complex problems, automate tasks, and unlock new growth opportunities. From real-time analytics in finance and precision medicine in healthcare, to autonomous vehicles and personalized recommendations in e-commerce, cloud-powered AI is driving unprecedented advances across sectors. This technology allows businesses and individuals to experiment, collaborate, and scale ideas faster than ever before—making the once-distant dream of ubiquitous, intelligent computing a tangible force shaping our modern lives. Significant technological advancements mark the evolution of cloud computing. A 2011 Gartner report anticipated that by 2015, more than half of the Global 1000 companies would store customer-sensitive data in the public cloud—a prediction that materialized as cloud spending surpassed $500 billion by 2023. According to Statista, over 60% of all corporate data is now stored in the cloud, highlighting its dominance and reliability. Significant developments such as the launches of Amazon EC2 in 2006, Microsoft Azure in 2010, and Google Cloud Platform in 2011 transformed the IT landscape, enabling millions of applications to run on scalable computing, on-demand storage, and strong security. IDC research projects that by 2025, the volume of data created and processed in the cloud and at the edge will exceed 175 zettabytes,

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underscoring the explosive growth propelled by AI and the Internet of Things (IoT). The partnership between cloud computing and AI is demonstrated through impactful real-world examples. In healthcare, Mount Sinai uses Microsoft Azure’s AI tools to speed up COVID-19 diagnoses by leveraging deep learning models that analyze chest X-rays, allowing clinicians to identify infections far faster than traditional techniques. Furthermore, in the automotive industry, Tesla utilizes AWS’s cloud infrastructure to gather, process, and analyze data from its global fleet, empowering its autonomous driving algorithms. In retail, Alibaba Cloud’s AI-powered recommendation systems have boosted conversion rates by 20% on its e-commerce platform, illustrating how cloud-based AI drives significant business outcomes. Regardless of what computing platform is used, the cloud provides features that are good, usable, and flexible, which are required in developing, training, and deploying AI models on a large scale. Pioneers of AI, such as OpenAI, responsible for breakthroughs in large language models (LLMs) and generative AI (GenAI), rely on the vast parallel processing power of cloud-based GPU clusters. NOTE GPU clusters are groups of powerful graphics processing units that work together to handle complex computations required for training advanced AI models Microsoft Azure’s AI Services enable organizations to build, test, and operate AI models without enormous upfront hardware costs. This democratization of AI gives both startups and large enterprises access to advanced machine learning and analytics capabilities, driving innovation across fields like natural language processing, predictive analytics, and computer vision.

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According to McKinsey & Company’s “The State of AI: Global Survey 2025,” organizations that adopt cloud-based AI have achieved operational efficiency gains of up to 40% and accelerated their product development cycles. At the intersection of cloud and AI, the direction of technological progress is unmistakable. Cloud computing is not just the infrastructure supporting digital transformation; it is the catalyst that enables organizations across healthcare, finance, transportation, and other sectors to harness the power of artificial intelligence fully, driving a smarter, more adaptive, and connected world.

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JOHN MCCARTHY’S VISIONARY PREDICTION: REALIZED IN TODAY’S CLOUD AND AI ERA John McCarthy, the renowned American computer scientist and cognitive scientist known as the father of artificial intelligence, made a remarkably forward-looking statement during his speech at MIT’s centennial in 1961. He envisioned computing as a public utility— readily available to everyone, just like water or electricity. This idea has now come to fruition, serving as the backbone of modern technology: cloud computing delivers scalable, on-demand access to computing resources and services worldwide. Recent advancements have accelerated the realization of McCarthy’s vision. Cloud computing has matured beyond basic storage and processing, becoming the essential platform for artificial intelligence (AI) innovation. Leading public cloud providers—Microsoft Azure, Amazon Web Services, and Google Cloud—now offer robust AI services, allowing organizations to deploy machine learning, natural language processing, and advanced analytics at scale. Examples include hospitals using cloud-based AI for rapid diagnostics, automotive companies optimizing autonomous driving with cloud data processing, and retailers enhancing customer experiences and sales with cloud- hosted AI solutions. The mutually reinforcing relationship between cloud computing and AI ensures that McCarthy’s vision is not only fulfilled but is also propelling the next wave of technological transformation. Today, cloud computing platforms provide the infrastructure that makes AI accessible, affordable, and impactful across all industries, powering a smarter, more interconnected world. From Cloud Computing Platforms to AI Platforms Built on It

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Cloud computing platforms such as Microsoft Azure have evolved from traditional cloud platforms into AI-first environments. At the heart of this transformation is Microsoft Foundry (formerly Azure AI Foundry), a unified platform for designing, customizing, developing, and integrating AI applications and agents at enterprise scale. Foundry brings together state-of- the-art models (like OpenAI GPT-4.1/5, Anthropic Claude, and xAI Grok), agent services and frameworks, knowledge grounding via Azure AI Search, and comprehensive governance with observability and policy controls. This shift empowers organizations to move from building simple cloud apps to orchestrating intelligent, autonomous systems. Evolution of Cloud Computing The journey to modern cloud computing begins with mainframe computers —robust, reliable systems designed for large-scale data processing and batch operations. Mainframes, primarily used by large organizations, excel at handling massive I/O workloads, making them ideal for enterprise-level computing tasks. Before the advent of dynamic cloud platforms like Microsoft Azure, several stages of computing evolution paved the way. Interestingly, early mainframe systems share similarities with today’s cloud environments, including client-server models and thin-client architecture. Migration to the cloud is driven by benefits such as cost savings, improved IT productivity, flexibility, speed, scalability, performance, and enhanced security, all of which have been further enabled by cloud platforms. Mainframe Computing Mainframe computing, often called “big iron,” utilizes large, high- performance machines equipped with multiple processors, centralized storage, and abundant memory. IBM pioneered these client/server-based systems, which excel at real-time transaction processing and large-scale calculations. Mainframes are known for their time-sharing capabilities and robust security, but are costly to maintain and require specialized expertise.

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While mainframes offer impressive batch processing and reliability, their lack of support for modern architecture and the scarcity of skilled engineers pose significant challenges. Cluster Computing Cluster computing connects multiple tightly coupled computers, or nodes, via local networks to function as a unified system. These clusters distribute computational tasks, enhancing performance, availability, and scalability. Common cluster types include high-availability, high-performance, and load-balancing clusters, which are frequently used for business optimization, scientific simulations, and large-scale data processing. The cost-effectiveness and speed of cluster computing make it ideal for complex tasks such as search engine indexing, earthquake simulations, and weather forecasting. Azure, for instance, provides high-performance computing (HPC) solutions that integrate compute, storage, and networking resources for demanding workloads. Grid Computing Grid computing is a form of parallel and distributed computing where geographically dispersed clusters collaborate to complete large tasks. The grid model creates a virtual supercomputer by pooling storage and processing power across multiple systems, ensuring rapid data processing and resource sharing. Grid computing’s flexibility and scalability make it suitable for research, analytics, and scientific applications that require substantial computational capacity. Cloud Computing As the culmination of innovations in mainframe, cluster, and grid computing, cloud computing represents the fifth generation of computing. It enables on-demand access to computing resources, applications, and services over the internet. Cloud platforms like Azure deliver rapid

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scalability, reliability, cost efficiency, and global reach—factors that continue to drive widespread adoption in the digital era. According to recent studies, over 40% of organizations plan to migrate critical workloads—such as data analytics, disaster recovery, and business intelligence—to the cloud, underscoring the cloud’s central role in modern IT strategies. Edge Computing Edge computing extends cloud capabilities by processing data closer to its source—at the “edge” of the network—rather than relying solely on centralized cloud data centers. This decentralized approach significantly reduces latency, improves real-time responsiveness, and optimizes bandwidth usage, making it indispensable for scenarios that demand immediate data analysis and rapid decision-making. Edge computing plays a pivotal role in applications such as Internet of Things (IoT) devices, autonomous vehicles, and innovative city systems, where real-time information processing is crucial. Edge computing has its benefits in ways such as helping reduce latency in real-time data applications by processing information close to its source, such as IoT devices or smartphones. This local handling improves response times and reduces bandwidth requirements, which is crucial for sectors such as manufacturing, healthcare, and autonomous vehicles. As technology providers expand edge infrastructure, they are integrating edge computing into cloud platforms, resulting in hybrid solutions that offer both scalability and speed. For instance, innovative city projects can use edge computing alongside the cloud to analyze traffic sensor data in real time, demonstrating edge computing’s growing role in modern, data-driven industries. Native AI Computing Native AI computing refers to the direct integration of artificial intelligence capabilities within computing platforms and cloud services. This approach allows organizations to efficiently build, train, and deploy AI models at

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scale by utilizing advanced hardware accelerators (such as GPUs and TPUs) and specialized software frameworks. As a result, complex tasks like machine learning, natural language processing, and computer vision become more accessible and deliver higher performance. For example, businesses can implement real-time language translation or automated image analysis without needing extensive on-premises infrastructure. Recent innovations in AI computing—especially when paired with cloud platforms—have dramatically accelerated the adoption and impact of artificial intelligence across industries. Leading public cloud providers, such as Azure and AWS, now offer native AI services that support everything from automated speech recognition and sentiment analysis to predictive maintenance and fraud detection. These platforms use scalable, distributed infrastructure to dynamically allocate resources to AI workloads, ensuring rapid processing and cost efficiency. For instance, healthcare organizations leverage native AI computing for medical image diagnostics, where cloud-based AI models can quickly analyze X-rays and MRI scans to assist clinicians in identifying diseases. In finance, banks use AI-powered cloud services to detect fraudulent transactions in real time and assess credit risk more accurately. Retailers deploy AI-driven recommendation engines that personalize shopping experiences for millions of users simultaneously, thanks to cloud-native scalability. Furthermore, integrating AI with edge computing enables real-time analytics and decision-making at the source of data, such as IoT devices or autonomous vehicles. Smart factories use cloud-hosted AI models to optimize production lines, predict equipment failures, and reduce downtime. Cities are adopting AI-native cloud solutions for traffic management, energy optimization, and public safety monitoring, demonstrating the versatility and transformative potential of this technology. By combining the latest AI advancements with the flexibility and reach of cloud computing, organizations can innovate faster, reduce operational

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complexity, and unlock new growth opportunities—without the need for costly on-premises hardware or specialized technical expertise. This is reshaping the way businesses approach data-driven decision-making and is foundational to the future of digital transformation. Why Does Cloud Computing Matter, and Why Does Adopting Key Technology Trends Matter? Over the last decade, cloud computing has fundamentally changed how we build and deploy software. Today, it’s not just about scalable infrastructure or cost savings—it’s about enabling innovation, agility, and global reach. With the rise of AI, the cloud has become the foundation for intelligent applications and agentic systems that can reason, act, and collaborate at scale. As we navigate the rapid emergence of AI, I recognize that many of us face real challenges in adapting to these new technologies, whether it’s understanding the capabilities, integrating AI into existing workflows, or addressing concerns about job roles and data security. Yet, I believe that embracing AI as an integral part of cloud computing’s evolution offers us a valuable perspective. By viewing AI not as a disruptive force but as an extension of the cloud’s promise—scalability, accessibility, and innovation —we position ourselves to benefit from intelligent applications and agentic systems that can reason, act, and collaborate on a scale we’ve never seen before. Adopting this mindset makes the journey less daunting and empowers us to harness the full potential of both AI and the cloud to drive meaningful progress across industries. In Table 1-1 you will find the top technology trends and challenges as we evolve from hosting applications to cloud computing and integrated AI platforms.

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Table 1-1. Top Five Key Trends for Cloud Computing Services and Adoption Cloud Development (2026) Key Trend Description Practical Use Case Technical Benefits AI/ML Integration & Agentic AI Development AI is embedded across the Azure platform to enable automation, analytics, and intelligent applications. Agentic AI development focuses on building autonomous agents and multi-agent systems that can reason, act, and collaborate to complete complex tasks end-to-end, opening new possibilities for dynamic workflows and intelligent automation. Train and serve custom models with Azure Machine Learning. Orchestrate retrieval- augmented applications and build agent-based solutions in Azure AI Studio Manage multi-model and agent estates using Microsoft Foundry. Leverage Azure AI Services for prebuilt APIs. Utilize Azure OpenAI Service Prebuilt APIs enable faster A delivery; enterprises gain robust controls model and agen governance; ac to GPU and TP resources is available via managed servic Agentic AI supports scalab adaptive system that can autonomously handle decision making, resour allocation, and workflow management.

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Key Trend Description Practical Use Case Technical Benefits Serverless Computing Development is event-driven, and infrastructure is abstracted, enabling simpler deployment models. Azure Functions for event handlers. Durable Functions for stateful orchestrations. Logic Apps for workflow automation. Serverless enab rapid deployme automatic scali pay-per-use pricing, and durable workfl for long-runnin processes. Security & Compliance for Cloud and Integrated AI Security strategies focus on zero-trust principles, posture management, and alignment with regulatory standards. Identity and access management with Microsoft Entra. Cloud security posture management Centralized identity, unified cloud security, automated regulatory assessments an controls provid benefits.

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Key Trend Description Practical Use Case Technical Benefits Multi-Cloud & Hybrid Strategies Operate across on-prem, multiple clouds, and edge. Govern servers, K8s, SQL anywhere with Azure Arc, GitOps + Policy at scale, Arc + adaptive cloud approach Private connectivity with ExpressRoute DR across regions with Azure Site Recovery Consistent governance, connectivity, an BCDR; best-of breed workload placement. Sustainable Cloud Practices Sustainability and ESG (Environmental, Social, and Governance) Driven Cloud Innovation, reporting influence platform choice, and architecture. Track/ report/ reduce footprint with Microsoft Sustainability Manager Choose greener regions/architectures; combine with cost/perf considerations Aligns with ES goals; operationalizes carbon/water/w data; supports disclosures (e.g CSRD). As illustrated in the table of key trends, whether we are ready or not, the evolution of cloud computing is accelerating, with artificial intelligence (AI) now deeply integrated into every layer of the cloud computing stack.