Snowflake is reshaping data management by integrating AI, analytics, and enterprise workloads into a single cloud platform. Snowflake: The Definitive Guide is a comprehensive resource for data architects, engineers, and business professionals looking to harness Snowflake's evolving capabilities, including Cortex AI, Snowpark, and Polaris Catalog for Apache Iceberg. This updated edition provides real-world strategies and hands-on activities for optimizing performance, securing data, and building AI-driven applications. With hands-on SQL examples and best practices, this book helps readers process structured and unstructured data, implement scalable architectures, and integrate Snowflake's AI tools seamlessly. Whether you're setting up accounts, managing access controls, or leveraging generative AI, this guide equips you with the expertise to maximize Snowflake's potential. Implement AI-powered workloads with Snowflake Cortex Explore Snowsight and Streamlit for no-code development Ensure security with access control and data governance Optimize storage, queries, and computing costs Design scalable data architectures for analytics and machine learning

SQL is no longer just a querying language for relational databases—it's a foundational tool for building scalable, modern data solutions across real-time analytics, machine learning workflows, and even generative AI applications. Advanced SQL shows data professionals how to move beyond conventional SELECT statements and tap into the full power of SQL as a programming interface for today's most advanced data platforms. Written by seasoned data experts Rui Pedro Machado, Hélder Russa, and Pedro Esmeriz, this practical guide explores the role of SQL in streaming architectures (like Apache Kafka and Flink), data lake ecosystems, cloud data warehouses, and ML pipelines. Geared toward data engineers, analysts, scientists, and analytics engineers, the book combines hands-on guidance with architectural best practices to help you extend your SQL skills into emerging workloads and real-world production systems. Use SQL to design and deploy modern, end-to-end data architectures Integrate SQL with data lakes, stream processing, and cloud platforms Apply SQL in feature engineering and ML model deployment Master pipe syntax and other advanced features for scalable, efficient queries Leverage SQL to build GenAI-ready data applications and pipelines

What will you learn from this book? Do you have an abundance of data but don't know how to make sense of it? Do you want to gain useful insights from your data, but you're not sure where to begin? Mining data is a vital, well-paying skill, and SQL provides the most fundamental way to query and manage data. But learning SQL can be intimidating. This thoroughly revised book teaches you SQL fundamentals in a painless and enjoyable manner. With the Head First series' hands-on, conversational style, you'll quickly grasp SQL concepts, then move to intermediate topics, including stored procedures and cloud databases. You'll gain the knowledge, skills, and confidence necessary to get the most out of your data with SQL. Why does this book look so different? If you've read a Head First book, you know what to expect: a visually rich format designed for the way your brain works. If you haven't, you're in for a treat. With this book, you'll learn about SQL through a multisensory experience that engages your mind—rather than a text-heavy approach that puts you to sleep.

A shift is underway in how organizations approach data infrastructure for AI-driven transformation. As multimodal AI systems and applications become increasingly sophisticated and data hungry, data systems must evolve to meet these complex demands. Data Engineering for Multimodal AI is one of the first practical guides for data engineers, machine learning engineers, and MLOps specialists looking to rapidly master the skills needed to build robust, scalable data infrastructures for multimodal AI systems and applications. You'll follow the entire lifecycle of AI-driven data engineering, from conceptualizing data architectures to implementing data pipelines optimized for multimodal learning in both cloud native and on-premises environments. And each chapter includes step-by-step guides and best practices for implementing key concepts. Design and implement cloud native data architectures optimized for multimodal AI workloads Build efficient and scalable ETL processes for preparing diverse AI training data Implement real-time data processing pipelines for multimodal AI inference Develop and manage feature stores that support multiple data modalities Apply data governance and security practices specific to multimodal AI projects Optimize data storage and retrieval for various types of multimodal ML models Integrate data versioning and lineage tracking in multimodal AI workflows Implement data-quality frameworks to ensure reliable outcomes across data types Design data pipelines that support responsible AI practices in a multimodal context

Bring your data to life with the ultimate beginner's guide to Power BI, now featuring Microsoft Fabric, Copilot, and full-color visuals to make learning data modeling, storytelling, and dashboards easier and faster than ever Key Features Build data literacy and gain confidence using Power BI through real-world, beginner-friendly examples Learn to shape, clean, and model data using Power BI Desktop and Power Query, with zero experience required Build vibrant, accurate reports and dashboards with real-world modeling examples Book Description Updated with the latest innovations in Power BI, including integration with Microsoft Fabric for seamless data unification and Copilot for AI-powered guidance. This comprehensive guide empowers you to build compelling reports and dashboards from the ground up. Whether you're new to Power BI or stepping into a data role, this book provides a friendly, approachable introduction to business intelligence and data storytelling You'll start with the Power BI Desktop interface and its core functionality, then move into shaping and cleaning your data using the Power Query Editor. From designing intuitive data models to writing your first DAX formulas, you’ll develop practical skills that apply directly to real-world scenarios. he book emphasizes how to use visualizations and narrative techniques to turn numbers into meaningful insights The chapters focus on hands-on, real-world examples—like analyzing sales trends, tracking KPIs, and cleaning messy data. You'll learn to build and refresh reports, scale your Power BI setup, and enhance your solutions using Microsoft Fabric and Copilot. Fabric unifies analytics across your organization, while Copilot speeds up your workflow with AI-driven insights and report suggestions By the end of the book, you’ll have the confidence and experience to turn raw data into insightful, impactful dashboards What you will learn Understand why data literacy matters in decision-making and careers Connect to data using import, DirectQuery, and live connection modes Clean and transform data using Power Query Editor and dataflows Design reports with visuals that support clear data storytelling Apply row-level security to enforce access and data protection Manage and monitor Power BI cloud for scalability and teamwork Use AI tools like Copilot to speed up prep and generate insights Learn Microsoft Fabric basics to enable unified data experiences Who this book is for This book is ideal for anyone looking to build a solid foundation in Power BI, regardless of prior experience. Whether you're just starting out or stepping into a new role that involves data, you'll find clear, approachable guidance throughout. The step-by-step tutorials and real-world examples make it easy to follow along—even if it’s your first time working with business intelligence tools

Master end-to-end data engineering on Azure Databricks. From data ingestion and Delta Lake to CI/CD and real-time streaming, build secure, scalable, and performant data solutions with Spark, Unity Catalog, and ML tools. Key Features Build scalable data pipelines using Apache Spark and Delta Lake Automate workflows and manage data governance with Unity Catalog Learn real-time processing and structured streaming with practical use cases Implement CI/CD, DevOps, and security for production-ready data solutions Explore Databricks-native ML, AutoML, and Generative AI integration Book Description "Data Engineering with Azure Databricks" is your essential guide to building scalable, secure, and high-performing data pipelines using the powerful Databricks platform on Azure. Designed for data engineers, architects, and developers, this book demystifies the complexities of Spark-based workloads, Delta Lake, Unity Catalog, and real-time data processing. Beginning with the foundational role of Azure Databricks in modern data engineering, you’ll explore how to set up robust environments, manage data ingestion with Auto Loader, optimize Spark performance, and orchestrate complex workflows using tools like Azure Data Factory and Airflow. The book offers deep dives into structured streaming, Delta Live Tables, and Delta Lake’s ACID features for data reliability and schema evolution. You’ll also learn how to manage security, compliance, and access controls using Unity Catalog, and gain insights into managing CI/CD pipelines with Azure DevOps and Terraform. With a special focus on machine learning and generative AI, the final chapters guide you in automating model workflows, leveraging MLflow, and fine-tuning large language models on Databricks. Whether you're building a modern data lakehouse or operationalizing analytics at scale, this book provides the tools and insights you need. What you will learn Set up a full-featured Azure Databricks environment Implement batch and streaming ingestion using Auto Loader Optimize Spark jobs with partitioning and caching Build real-time pipelines with structured streaming and DLT Manage data governance using Unity Catalog Orchestrate production workflows with jobs and ADF Apply CI/CD best practices with Azure DevOps and Git Secure data with RBAC, encryption, and compliance standards Use MLflow and Feature Store for ML pipelines Build generative AI applications in Databricks Who this book is for This book is for data engineers, solution architects, cloud professionals, and software engineers seeking to build robust and scalable data pipelines using Azure Databricks. Whether you're migrating legacy systems, implementing a modern lakehouse architecture, or optimizing data workflows for performance, this guide will help you leverage the full power of Databricks on Azure. A basic understanding of Python, Spark, and cloud infrastructure is recommended.

Generative AI is revolutionizing industries, and Kubernetes has fast become the backbone for deploying and managing these resource-intensive workloads. This book serves as a practical, hands-on guide for MLOps engineers, software developers, Kubernetes administrators, and AI professionals ready to unlock AI innovation with the power of cloud native infrastructure. Authors Roland Huß and Daniele Zonca provide a clear road map for training, fine-tuning, deploying, and scaling GenAI models on Kubernetes, addressing challenges like resource optimization, automation, and security along the way. With actionable insights with real-world examples, readers will learn to tackle the opportunities and complexities of managing GenAI applications in production environments. Whether you're experimenting with large-scale language models or facing the nuances of AI deployment at scale, you'll uncover expertise you need to operationalize this exciting technology effectively. Learn to run GenAI models on Kubernetes for efficient scalability Get techniques to train and fine-tune LLMs within Kubernetes environments See how to deploy production-ready AI systems with automation and resource optimization Discover how to monitor and scale GenAI applications to handle real-world demand Uncover the best tools to operationalize your GenAI workloads Learn how to run agent-based and AI-driven applications

A guide to pass the GCP Professional Data Engineer exam on your first attempt and upgrade your data engineering skills on GCP. Key Features Fully understand the certification exam content and exam objectives Consolidate your knowledge of all essential exam topics and key concepts Get realistic experience of answering exam-style questions Develop practical skills for everyday use Purchase of this book unlocks access to web-based exam prep resources including mock exams, flashcards, exam tips Book Description The GCP Professional Data Engineer certification validates the fundamental knowledge required to perform data engineering tasks and use GCP services to enhance data engineering processes and further your career in the data engineering/architecting field. This book is a best-in-class study guide that fully covers the GCP Professional Data Engineer exam objectives and helps you pass the exam first time. Complete with clear explanations, chapter review questions, realistic mock exams, and pragmatic solutions, this guide will help you master the core exam concepts and build the understanding you need to go into the exam with the skills and confidence to get the best result you can. With the help of relevant examples, you'll learn fundamental data engineering concepts such as data warehousing and data security. As you progress, you'll delve into the important domains of the exam, including data pipelining, data migration, and data processing. Unlike other study guides, this book contains logical reasoning behind the choice of correct answers based in scenarios and provide you with excellent tips regarding the optimal use of each service, and gives you everything you need to pass the exam and enhance your prospects in the data engineering field. What you will learn Create data solutions and pipelines in GCP Analyze and transform data into useful information Apply data engineering concepts to real scenarios Create secure, cost-effective, valuable GCP workloads Work in the GCP environment with industry best practices Who this book is for This book is for data engineers who want a reliable source for the key concepts and terms present in the most prestigious and highly-sought-after cloud-based data engineering certification. This book will help you improve your data engineering in GCP skills to give you a better chance at earning the GCP Professional Data Engineer Certification. You will already be familiar with the Google Cloud Platform, having either explored it (professionally or personally) for at least a year. You should also have some familiarity with basic data concepts (such as types of data and basic SQL knowledge).

The book simplifies the complexities of cloud transition and offers a clear, actionable roadmap for organizations moving from SAP BW or BW/4HANA to SAP Datasphere and SAP Analytics Cloud (as part of SAP Business Data Cloud), particularly in alignment with S/4HANA transformation. Whether you are assessing your current landscape, building a business case with ROI analysis, or creating a phased implementation strategy, this book delivers both technical and strategic guidance. It highlights short- and long-term planning considerations, outlines migration governance, and provides best practices for managing projects across hybrid SAP environments. From identifying platform gaps to facilitating stakeholder discussions, this book is an essential resource for anyone involved in the analytics modernization journey. You Will: [if !supportLists] · [endif] Learn how to assess your current SAP BW or BW/4HANA landscape and identify key migration drivers [if !supportLists] · [endif] Understand best practices for leveraging out-of-the-box cloud features and AI/ML capabilities [if !supportLists] · [endif] A step-by-step approach to planning and executing the move to SAP Business Data Cloud (Mainly SAP Datasphere and SAP Analytics Cloud) This book is for: SAP BW/BW4HANA Customers, SAP Consultants, Solution Architects and Enterprise Architects

Grow your software engineering discipline, incorporating and mastering design, development, testing, and deployment best practices examples in a realistic Python project structure. Key Features Understand what makes Software Engineering a discipline, distinct from basic programming Gain practical insight into updating, refactoring, and scaling an existing Python system Implement robust testing, CI/CD pipelines, and cloud-ready architecture decisions Book Description Software engineering is more than coding; it’s the strategic design and continuous improvement of systems that serve real-world needs. This newly updated second edition of Hands-On Software Engineering with Python expands on its foundational approach to help you grow into a senior or staff-level engineering role. Fully revised for today’s Python ecosystem, this edition includes updated tooling, practices, and architectural patterns. You’ll explore key changes across five minor Python versions, examine new features like dataclasses and type hinting, and evaluate modern tools such as Poetry, pytest, and GitHub Actions. A new chapter introduces high-performance computing in Python, and the entire development process is enhanced with cloud-readiness in mind. You’ll follow a complete redesign and refactor of a multi-tier system from the first edition, gaining insight into how software evolves—and what it takes to do that responsibly. From system modeling and SDLC phases to data persistence, testing, and CI/CD automation, each chapter builds your engineering mindset while updating your hands-on skills. By the end of this book, you'll have mastered modern Python software engineering practices and be equipped to revise and future-proof complex systems with confidence. What you will learn Distinguish software engineering from general programming Break down and apply each phase of the SDLC to Python systems Create system models to plan architecture before writing code Apply Agile, Scrum, and other modern development methodologies Use dataclasses, pydantic, and schemas for robust data modeling Set up CI/CD pipelines with GitHub Actions and cloud build tools Write and structure unit, integration, and end-to-end tests Evaluate and integrate tools like Poetry, pytest, and Docker Who this book is for This book is for Python developers with a basic grasp of software development who want to grow into senior or staff-level engineering roles. It’s ideal for professionals looking to deepen their understanding of software architecture, system modeling, testing strategies, and cloud-aware development. Familiarity with core Python programming is required, as the book focuses on applying engineering principles to maintain, extend, and modernize real-world systems.

Bioinformatics with Python Cookbook provides a practical, hands-on approach to solving computational biology challenges with Python, enabling readers to analyze sequencing data, leverage AI for bioinformatics applications, and design robust computational pipelines. What this Book will help me do Perform comprehensive sequence analysis using Python libraries for refined data interpretation. Configure and run bioinformatics workflows on cloud environments for scalable solutions. Apply advanced data science practices to analyze and visualize bioinformatics data. Explore the integration of AI tools in processing multimodal biological datasets. Understand and utilize bioinformatics databases for research and development. Author(s) Shane Brubaker is an experienced computational biologist and software developer with a strong background in bioinformatics and Python programming. With years of experience in data analysis and software engineering, Shane has authored numerous solutions for real-world bioinformatics issues. He brings a practical, example-driven teaching approach, aimed at empowering readers to apply techniques effectively in their work. Who is it for? This book is suitable for bioinformatics professionals, data scientists, and software engineers with moderate experience seeking to expand their computational biology knowledge. Readers should have basic understanding of biology, programming, and cloud tools. By engaging with this book, learners can advance their skills in Python and bioinformatics to address complex biological data challenges effectively.

This book serves as a comprehensive guide to mastering Microsoft Power Automate, offering step-by-step instructions for creating and managing low-code workflows. From beginner to advanced techniques, it covers cloud and RPA functionalities, enhanced by AI features like Co-pilot. You'll gain the skills to build, analyze, and optimize powerful automations tailored to your organization's needs. What this Book will help me do Understand and implement workflows using Power Automate's connectors and triggers for seamless integration. Utilize AI Builder and the Co-pilot feature to design intelligent workflows with generative AI capabilities. Master robotic process automation to bridge digital and legacy systems effectively. Learn to monitor and troubleshoot workflows while ensuring security and compliance in automation. Scale and govern enterprise-level workflows with best practices for maintainability. Author(s) Aaron Guilmette is a seasoned expert in the field of workflow automation with extensive experience in the Microsoft ecosystem. As the author of multiple books on Power Automate, Aaron combines technical depth with practical know-how. He brings a hands-on approach to guiding readers through advanced features, making automation accessible and effective. Who is it for? This book is ideal for power users, information workers, and citizen developers looking to integrate automation into their work. Whether you're new to automation or expanding your skills, this book provides actionable insights. Familiarity with the Microsoft 365 platform is recommended but not required, as the book covers foundational as well as advanced topics. It is perfect for anyone aiming to streamline workflows and drive efficiency in their projects or organization.

Secure your expertise in the next wave of computing with this essential book, which provides a comprehensive guide to Edge AI, detailing its foundational concepts, deployment strategies, and real-world applications for revolutionizing performance and privacy across various industries. Edge AI has the potential to bring the computational power of AI algorithms closer to where data is generated, processed, and utilized. Traditionally, AI models are deployed in centralized cloud environments, leading to latency issues, bandwidth constraints, and privacy concerns. Edge AI addresses these limitations by enabling AI inference and decision-making directly on edge devices, such as smartphones, IoT sensors, and edge servers. Despite its challenges, edge AI presents numerous opportunities across various domains. From real-time health monitoring and predictive maintenance in industrial IoT to personalized recommendations in retail and immersive experiences in augmented reality, edge AI has the potential to revolutionize how we interact with technology. This book aims to provide a comprehensive exploration of edge AI, covering its foundational concepts, development frameworks, deployment strategies, security considerations, ethical implications, emerging trends, and real-world applications. This guide is essential for anyone pushing the boundaries to leverage edge computing for enhanced performance and efficiency. Readers will find this volume: Dives deep into the world of edge AI with a comprehensive exploration covering foundational concepts, development frameworks, deployment strategies, security considerations, ethical implications, governance frameworks, optimization techniques, and real-world applications; Offers practical guidance on implementing edge AI solutions effectively in various domains, including architecture design, development frameworks, deployment strategies, and optimization techniques; Explores concrete examples of edge AI applications across diverse domains such as healthcare, industrial IoT, smart cities, and autonomous systems, providing insights into how edge AI is revolutionizing industries and everyday life; Provides insights into emerging trends and technologies in the field of edge AI, including convergence with blockchain, augmented reality, virtual reality, autonomous systems, personalized experiences, and cybersecurity. Audience Researchers, AI experts, and industry professionals in the field of computer science, IT, and business management.

Transform your data replication strategy into a competitive advantage with Oracle GoldenGate 23ai. This comprehensive guide delivers the practical knowledge DBAs and architects need to implement, optimize , and scale Oracle GoldenGate 23ai in production environments. Written by Oracle ACE Director Bobby Curtis, it blends deep technical expertise with real-world business insights from hundreds of implementations across manufacturing, financial services, and technology sectors. Beyond traditional replication, this book explores the groundbreaking capabilities that make GoldenGate 23ai essential for modern AI initiatives. Learn how to implement real-time vector replication for RAG systems, integrate with cloud platforms like GCP and Snowflake, and automate deployments using REST APIs and Python. Each chapter offers proven strategies to deliver measurable ROI while reducing operational risk. Whether you're upgrading from Classic GoldenGate , deploying your first cloud data pipeline, or building AI-ready data architectures, this book provides the strategic guidance and technical depth to succeed. With Bobby's signature direct approach, you'll avoid common pitfalls and implement best practices that scale with your business. What You Will Learn Master the microservices architecture and new capabilities of Oracle GoldenGate 23ai Implement secure, high-performance data replication across Oracle, PostgreSQL, and cloud databases Configure vector replication for AI and machine learning workloads, including RAG systems Design and build multi-master replication models with automatic conflict resolution Automate deployments and management using RESTful APIs and Python Optimize performance for sub-second replication lag in production environments Secure your replication environment with enterprise-grade features and compliance Upgrade from Classic to Microservices architecture with zero downtime Integrate with cloud platforms including OCI, GCP, AWS, and Azure Implement real-time data pipelines to BigQuery , Snowflake, and other cloud targets Navigate Oracle licensing models and optimize costs Who This Book Is For Database administrators, architects, and IT leaders working with Oracle GoldenGate —whether deploying for the first time, migrating from Classic architecture, or enabling AI-driven replication—will find actionable guidance on implementation, performance tuning, automation, and cloud integration. Covers unidirectional and multi-master replication and is packed with real-world use cases.

Leverage your smartphone and smartwatch for improved personal safety! Version 2.0, updated November 12, 2025 The digital and “real” worlds can both be scary places. The smartphone (and often smartwatch) you already carry with you can help reduce risks, deter theft, and mitigate violence. This book teaches you to secure your hardware, block abuse, automatically call emergency services, connect with others to ensure you arrive where and when you intended, detect stalking by compact trackers, and keep your ecosystem accounts from Apple, Google, and Microsoft secure. You don’t have to be reminded of the virtual and physical risks you face every day. Some of us are targeted more than others. Modern digital features built into mobile operating systems (and some computer operating systems) can help reduce our anxiety by putting more power in our hands to deter, deflect, block, and respond to abuse, threats, and emergencies. Keep Safe Using Mobile Tech looks at both digital threats, like online abuse and account hijacking, and ones in the physical world, like being stalked through Bluetooth trackers, facing domestic violence, or being in a car crash. The book principally covers the iPhone, Apple Watch, Android devices, and Wear OS watches. It also covers more limited but useful features available on the iPad and on computers running macOS or Windows. This second edition incorporates the massive number of new safety features Google added since October 2024 to the Android operating system, some particular to Google Pixel phones and smartwatches, and improved blocking, filtering, and screening added to Apple’s iOS 26 and related operating system updates in fall 2025. This book explores many techniques to help:

Learn how to harden your Apple Account, Google Account, and Microsoft Account beyond just a password or a text-message token. Discover filtering and blocking tools from Apple and Google that can prevent abusive, fraudulent, and phishing messages and calls from reaching you. Block seeing unwanted sensitive images on your iPhone, iPad, Mac, Apple Watch, or Android phone—and help your kids receive advice on how not to send them. Turn on tracking on your Apple, Google, and Microsoft devices, and use it to recover or erase stolen hardware. Keep your cloud-archived messages from leaking to attackers. Screen calls with an automated assistant so that you know who wants you before picking up and without sending to voicemail. Lock down your devices to keep thieves and other personal invaders from accessing them. Prepare for emergencies by setting up medical information on your mobile devices. Let a supported smartphone or smartwatch recognize when you’re in a car crash or have taken a hard fall and call emergency services for you (and text your emergency contacts) if you can’t respond. Keep track of heart anomalies through smartwatch alerts and tests on your Apple Watch and many Android Wear smartwatches. Tell others where or when you expect to check in with them again, and let your smartphone alert them if you don’t with your Apple iPhone or Android phone. Deter stalking from tiny Bluetooth trackers. Protect your devices and accounts against access from domestic assailants. Block thieves who steal your phone—potentially threatening you or attacking you in person—from gaining access to the rest of your digital life.

A hands-on technical and industry roadmap for aspiring data engineers In Data Engineering for Beginners, big data expert Chisom Nwokwu delivers a beginner-friendly handbook for everyone interested in the fundamentals of data engineering. Whether you're interested in starting a rewarding, new career as a data analyst, data engineer, or data scientist, or seeking to expand your skillset in an existing engineering role, Nwokwu offers the technical and industry knowledge you need to succeed. The book explains: Database fundamentals, including relational and noSQL databases Data warehouses and data lakes Data pipelines, including info about batch and stream processing Data quality dimensions Data security principles, including data encryption Data governance principles and data framework Big data and distributed systems concepts Data engineering on the cloud Essential skills and tools for data engineering interviews and jobs Data Engineering for Beginners offers an easy-to-read roadmap on a seemingly complicated and intimidating subject. It addresses the topics most likely to cause a beginning data engineer to stumble, clearly explaining key concepts in an accessible way. You'll also find: A comprehensive glossary of data engineering terms Common and practical career paths in the data engineering industry An introduction to key cloud technologies and services you may encounter early in your data engineering career Perfect for practicing and aspiring data analysts, data scientists, and data engineers, Data Engineering for Beginners is an effective and reliable starting point for learning an in-demand skill. It's a powerful resource for everyone hoping to expand their data engineering Skillset and upskill in the big data era.

Dive into 'Microsoft Power Platform Solution Architect's Handbook' to master the art of designing and delivering enterprise-grade solutions using Microsoft's cutting-edge Power Platform. Through a mix of practical examples and hands-on tutorials, this book equips you to harness tools like AI, Copilot, and DevOps for building innovative, scalable applications tailored to enterprise needs. What this Book will help me do Acquire the knowledge to effectively utilize AI tools such as Power Platform Copilot and ChatGPT to enhance application intelligence. Understand and apply enterprise-grade solution architecture principles for scalable and secure application development. Gain expertise in integrating heterogenous systems with Power Platform Pipes and third-party APIs. Develop proficiency in creating and maintaining reusable Dataverse data models. Learn to establish and manage a Center of Excellence to govern and scale Power Platform solutions. Author(s) Hugo Herrera is an experienced solution architect specializing in the Microsoft Power Platform with a deep focus on integrating AI and cloud-native strategies. With years of hands-on experience in enterprise software development and architectural design, Hugo brings real-world insights into his writing, emphasizing practical application of advanced concepts. His approach is clear, structured, and aimed at empowering readers to excel. Who is it for? This book is tailored for IT professionals like solution architects, enterprise architects, and technical consultants who are looking to elevate their capabilities in Power Platform development. It is also suitable for individuals with an intermediate understanding of Power Platform seeking to spearhead enterprise-level digital transformation projects. Ideal readers are those ready to deepen their integration, data modeling, and AI usage skills within the Microsoft ecosystem, particularly for enterprise applications.

AI-Driven Software Testing explores how Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing quality engineering (QE), making testing more intelligent, efficient, and adaptive. The book begins by examining the critical role of QE in modern software development and the paradigm shift introduced by AI/ML. It traces the evolution of software testing, from manual approaches to AI-powered automation, highlighting key innovations that enhance accuracy, speed, and scalability. Readers will gain a deep understanding of quality engineering in the age of AI, comparing traditional and AI-driven testing methodologies to uncover their advantages and challenges. Moving into practical applications, the book delves into AI-enhanced test planning, execution, and defect management. It explores AI-driven test case development, intelligent test environments, and real-time monitoring techniques that streamline the testing lifecycle. Additionally, it covers AI’s impact on continuous integration and delivery (CI/CD), predictive analytics for failure prevention, and strategies for scaling AI-driven testing across cloud platforms. Finally, it looks ahead to the future of AI in software testing, discussing emerging trends, ethical considerations, and the evolving role of QE professionals in an AI-first world. With real-world case studies and actionable insights, AI-Driven Software Testing is an essential guide for QE engineers, developers, and tech leaders looking to harness AI for smarter, faster, and more reliable software testing. What you will learn: • What are the key principles of AI/ML-driven quality engineering • What is intelligent test case generation and adaptive test automation • Explore predictive analytics for defect prevention and risk assessment • Understand integration of AI/ML tools in CI/CD pipelines Who this book is for: Quality Engineers looking to enhance software testing with AI-driven techniques. Data Scientists exploring AI applications in software quality assurance and engineering. Software Developers – Engineers seeking to integrate AI/ML into testing and automation workflows.

Unlock the full financial potential of your Snowflake environment. Learn how to cut costs, boost performance, and take control of your cloud data spend with FinOps for Snowflake—your essential guide to implementing a smart, automated, and Snowflake-optimized FinOps strategy. In today’s data-driven world, financial optimization on platforms like Snowflake is more critical than ever. Whether you're just beginning your FinOps journey or refining mature practices, this book provides a practical roadmap to align Snowflake usage with business goals, reduce costs, and improve performance—without compromising agility. Grounded in real-world case studies and packed with actionable strategies, FinOps for Snowflake shows how leading organizations are transforming their environments through automation, governance, and cost intelligence. You'll learn how to apply proven techniques for architecture tuning, workload and storage efficiency, and performance optimization—empowering you to make smarter, data-driven decisions. What You Will Learn Master FinOps principles tailored for Snowflake’s architecture and pricing model Enable collaboration across finance, engineering, and business teams Deliver real-time cost insights for smarter decision-making Optimize compute, storage, and Snowflake AI and ML services for efficiency Leverage Snowflake Cortex AI and Adoptive Warehouse/Compute for intelligent cost governance Apply proven strategies to achieve operational excellence and measurable savings Who this Book is For Data professionals, cloud engineers, FinOps practitioners, and finance teams seeking to improve cost visibility, operational efficiency, and financial accountability in Snowflake environments.

Unveil the data platform of the future with SQL Server 2025—guided by one of its key architects . With built-in AI for application development and advanced analytics powered by Microsoft Fabric, SQL Server 2025 empowers you to innovate—securely and confidently. This book shows you how. Author Bob Ward, Principal Architect for the Microsoft Azure Data team, shares exclusive insights drawn from over three decades at Microsoft. Having worked on every version of SQL Server since OS/2 1.1, Ward brings unmatched expertise and practical guidance to help you navigate this transformative release. Ward covers everything from setup and upgrades to advanced features in performance, high availability, and security. He also highlights what makes this the most developer-friendly release in a decade: support for JSON, RegEx, REST APIs, and event streaming. Most critically, Ward explores SQL Server 2025’s advanced, scalable AI integrations, showing you how to build AI-powered applications deeply integrated with the SQL engine—and elevate your analytics to the next level. But innovation doesn’t come at the cost of safety: this release is built on a foundation of enterprise-grade security, helping you adopt AI safely and responsibly. You control which models to use, how they interact with your data, and where they run—from ground to cloud, or integrated with Microsoft Fabric. With built-in features like Row-Level Security (RLS), Transparent Data Encryption (TDE), Dynamic Data Masking, and SQL Server Auditing, your data remains protected at every layer. The AI age is here. Make sure your SQL Server databases are ready—and built for secure, scalable innovation . What You Will Learn [if !supportLists] · [endif]Grasp the fundamentals of AI to leverage AI with your data, using the industry-proven security and scale of SQL Server [if !supportLists] · [endif]Utilize AI models of your choice, services, and frameworks to build new AI applications [if !supportLists] · [endif]Explore new developer features such as JSON, Regular Expressions, REST API, and Change Event Streaming [if !supportLists] · [endif]Discover SQL Server 2025's powerful new engine capabilities to increase application concurrency [if !supportLists] · [endif]Examine new high availability features to enhance uptime and diagnose complex HADR configurations [if !supportLists] · Use new query processing capabilities to extend the performance of your application [if !supportLists] · [endif]Connect SQL Server to Azure with Arc for advanced management and security capabilities [if !supportLists] · [endif]Secure and govern your data using Microsoft Entra [if !supportLists] · [endif]Achieve near-real-time analytics with the unified data platform Microsoft Fabric [if !supportLists] · [endif]Integrate AI capabilities with SQL Server for enterprise AI [if !supportLists] · [endif]Leverage new tools such as SQL Server Management Studio and Copilot experiences to assist your SQL Server journey Who This Book Is For The SQL Server community, including DBAs, architects, and developers eager to stay ahead with the latest advancements in SQL Server 2025, and those interested in the intersection of AI and data, particularly how artificial intelligence (AI) can be seamlessly integrated with SQL Server to unlock deeper insights and smarter solutions