Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problemAutomating the process for continually developing, evaluating, deploying, and updating modelsDeveloping a monitoring system to quickly detect and address issues your models might encounter in productionArchitecting an ML platform that serves across use casesDeveloping responsible ML systems Read more

Step into the world of LLMs with this practical guide that takes you from the fundamentals to deploying advanced applications using LLMOps best practicesPurchase of the print or Kindle book includes a free eBook in PDF format“This book is instrumental in making sure that as many people as possible can not only use LLMs but also adapt them, fine-tune them, quantize them, and make them efficient enough to deploy in the real world.”- Julien Chaumond, CTO and Co-founder, Hugging FaceBook DescriptionThis LLM book provides practical insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps' best practices. The guide walks you through building an LLM-powered twin that’s cost-effective, scalable, and modular. It moves beyond isolated Jupyter Notebooks, focusing on how to build production-grade end-to-end LLM systems. Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects.What you will learnImplement robust data pipelines and manage LLM training cyclesCreate your own LLM and refine with the help of hands-on examplesGet started with LLMOps by diving into core MLOps principles like IaCPerform supervised fine-tuning and LLM evaluationDeploy end-to-end LLM solutions using AWS and other toolsExplore continuous training, monitoring, and logic automationLearn about RAG ingestion as well as inference and feature pipelinesWho this book is forThis book is for AI engineers, NLP professionals, and LLM engineers looking to deepen their understanding of LLMs. Basic knowledge of LLMs and the Gen AI landscape, Python and AWS is recommended. Whether you are new to AI or looking to enhance your skills, this book provides comprehensive guidance on implementing LLMs in real-world scenarios.Table of ContentsUndersstanding the LLM Twin Concept and ArchitectureTooling and InstallationData EngineeringRAG Feature PipelineSupervised Fine-tuningFine-tuning with Preference AlignmentEvaluating LLMsInference OptimizationRAG Inference PipelineInference Pipeline DeploymentMLOps and LLMOpsAppendix: MLOps Principles Read more

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The '180 Days of Science' offers a purposeful, daily practice that will engage 4th-grade students throughout the school year. Crafted to promote both fun and learning, the interactive activities provide children an ideal environment to focus on content-area literacy, analyze and evaluate scientific data and scenarios, engineering practices, and higher-order thinking skills. Great for homeschooling or learning in classrooms, these workbooks also help kids retain educational knowledge during the summer or stay-at-home learning. This workbook will help students analyze and evaluate scientific data and scenarios, improve their understanding of science and engineering practices, answer constructed-response questions, and increase their higher-order thinking skills.The resource book is divided into three sections: Life Science, Physical Science, and Earth and Space Science. It is structured to give students a strong foundation on which to build throughout the year.All 36 weeks of this book follow a regular weekly structure where each week covers a particular topic within one of three science strands. COMPREHENSIVE WORKBOOKS: 180 days of reading, writing, and science of life, physical, earth and space; designed to practice key skills, demonstrate understanding, and build the confidence needed for 4th-graders INDIVIDUALIZED LEARNING: Practice-Assess-Diagnose format assesses children’s unique learning abilities focusing on building content knowledge, analyzing data, and communicating results ALIGNS WITH EDUCATION STANDARDS: Packed with engaging activities to help students flourish and thrive in 4th-grade. Aligned to state, national, and College & Career Readiness standards IDEAL FOR HOMESCHOOLING: Designed for parents and teachers to provide daily practice to young scholars at home or online CREATED BY TEACHERS: TCM develops innovative and imaginative educational materials for students and teachers worldwide to make teaching more effective and learning more fun Read more

Leverage cutting-edge generative AI techniques such as RAG to realize the potential of your data and drive innovation as well as gain strategic advantageKey FeaturesOptimize data retrieval and generation using vector databasesBoost decision-making and automate workflows with AI agentsOvercome common challenges in implementing real-world RAG systemsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGenerative AI is helping organizations tap into their data in new ways, with retrieval-augmented generation (RAG) combining the strengths of large language models (LLMs) with internal data for more intelligent and relevant AI applications. The author harnesses his decade of ML experience in this book to equip you with the strategic insights and technical expertise needed when using RAG to drive transformative outcomes.The book explores RAG’s role in enhancing organizational operations by blending theoretical foundations with practical techniques. You’ll work with detailed coding examples using tools such as LangChain and Chroma’s vector database to gain hands-on experience in integrating RAG into AI systems. The chapters contain real-world case studies and sample applications that highlight RAG’s diverse use cases, from search engines to chatbots. You’ll learn proven methods for managing vector databases, optimizing data retrieval, effective prompt engineering, and quantitatively evaluating performance. The book also takes you through advanced integrations of RAG with cutting-edge AI agents and emerging non-LLM technologies.By the end of this book, you’ll be able to successfully deploy RAG in business settings, address common challenges, and push the boundaries of what’s possible with this revolutionary AI technique.What you will learnUnderstand RAG principles and their significance in generative AIIntegrate LLMs with internal data for enhanced operationsMaster vectorization, vector databases, and vector search techniquesDevelop skills in prompt engineering specific to RAG and design for precise AI responsesFamiliarize yourself with AI agents' roles in facilitating sophisticated RAG applicationsOvercome scalability, data quality, and integration issuesDiscover strategies for optimizing data retrieval and AI interpretabilityWho this book is forThis book is for AI researchers, data scientists, software developers, and business analysts looking to leverage RAG and generative AI to enhance data retrieval, improve AI accuracy, and drive innovation. It is particularly suited for anyone with a foundational understanding of AI who seeks practical, hands-on learning. The book offers real-world coding examples and strategies for implementing RAG effectively, making it accessible to both technical and non-technical audiences. A basic understanding of Python and Jupyter Notebooks is required.Table of ContentsWhat Is Retrieval-Augmented Generation (RAG)Code Lab – An Entire RAG PipelinePractical Applications of RAGComponents of a RAG SystemManaging Security in RAG ApplicationsInterfacing with RAG and GradioThe Key Role Vectors and Vector Stores Play in RAGSimilarity Searching with VectorsEvaluating RAG Quantitatively and with VisualizationsKey RAG Components in LangChainUsing LangChain to Get More from RAGCombining RAG with the Power of AI Agents and LangGraphUsing Prompt Engineering to Improve RAG EffortsAdvanced RAG-Related Techniques for Improving Results Read more

Goes beyond academic discussions deeply into the applications layer of Foundation Models.This practical book offers clear, example-rich explanations of how LLMs work, how you can interact with them, and how to integrate LLMs into your own applications. Find out what makes LLMs so different from traditional software and ML, discover best practices for working with them out of the lab, and dodge common pitfalls with experienced advice. In LLMs in Production you will: • Grasp the fundamentals of LLMs and the technology behind them • Evaluate when to use a premade LLM and when to build your own • Efficiently scale up an ML platform to handle the needs of LLMs • Train LLM foundation models and finetune an existing LLM • Deploy LLMs to the cloud and edge devices using complex architectures like PEFT and LoRA • Build applications leveraging the strengths of LLMs while mitigating their weaknesses LLMs in Production delivers vital insights into delivering MLOps so you can easily and seamlessly guide one to production usage. Inside, you’ll find practical insights into everything from acquiring an LLM-suitable training dataset, building a platform, and compensating for their immense size. Plus, tips and tricks for prompt engineering, retraining and load testing, handling costs, and ensuring security. Foreword by Joe Reis. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Most business software is developed and improved iteratively, and can change significantly even after deployment. By contrast, because LLMs are expensive to create and difficult to modify, they require meticulous upfront planning, exacting data standards, and carefully-executed technical implementation. Integrating LLMs into production products impacts every aspect of your operations plan, including the application lifecycle, data pipeline, compute cost, security, and more. Get it wrong, and you may have a costly failure on your hands. About the book LLMs in Production teaches you how to develop an LLMOps plan that can take an AI app smoothly from design to delivery. You’ll learn techniques for preparing an LLM dataset, cost-efficient training hacks like LORA and RLHF, and industry benchmarks for model evaluation. Along the way, you’ll put your new skills to use in three exciting example projects: creating and training a custom LLM, building a VSCode AI coding extension, and deploying a small model to a Raspberry Pi. What's inside • Balancing cost and performance • Retraining and load testing • Optimizing models for commodity hardware • Deploying on a Kubernetes cluster About the reader For data scientists and ML engineers who know Python and the basics of cloud deployment. About the author Christopher Brousseau and Matt Sharp are experienced engineers who have led numerous successful large scale LLM deployments. Table of Contents 1 Words’ awakening: Why large language models have captured attention 2 Large language models: A deep dive into language modeling 3 Large language model operations: Building a platform for LLMs 4 Data engineering for large language models: Setting up for success 5 Training large language models: How to generate the generator 6 Large language model services: A practical guide 7 Prompt engineering: Becoming an LLM whisperer 8 Large language model applications: Building an interactive experience 9 Creating an LLM project: Reimplementing Llama 3 10 Creating a coding copilot project: This would have helped you earlier 11 Deploying an LLM on a Raspberry Pi: How low can you go? 12 Production, an ever-changing landscape: Things are just getting started A History of linguistics B Reinforcement learning with human feedback C Multimodal latent spaces Read more

Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase.Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly.You’ll get the guidance you need to confidently:Find and wrangle time series dataUndertake exploratory time series data analysisStore temporal dataSimulate time series dataGenerate and select features for a time seriesMeasure errorForecast and classify time series with machine or deep learningEvaluate accuracy and performance

Get ready to pass the certification exam on your first attempt by gaining actionable insights into AI concepts, ML techniques, and Azure AI services covered in the latest AI-900 exam syllabus from two industry experts Key FeaturesDiscover Azure AI services, including computer vision, Auto ML, NLP, and OpenAIExplore AI use cases, such as image identification, chatbots, and moreWork through 145 practice questions under chapter-end self-assessments and mock examsPurchase of this book unlocks access to web-based exam prep resources, including mock exams, flashcards, and exam tipsBook DescriptionThe AI-900 exam helps you take your first step into an AI-shaped future. Regardless of your technical background, this book will help you test your understanding of the key AI-related topics and tools used to develop AI solutions in Azure cloud.This exam guide focuses on AI workloads, including natural language processing (NLP) and large language models (LLMs). You’ll explore Microsoft’s responsible AI principles like safety and accountability. Then, you’ll cover the basics of machine learning (ML), including classification and deep learning, and learn how to use training and validation datasets with Azure ML. Using Azure AI Vision, face detection, and Video Indexer services, you’ll get up to speed with computer vision-related topics like image classification, object detection, and facial detection. Later chapters cover NLP features such as key phrase extraction, sentiment analysis, and speech processing using Azure AI Language, speech, and translator services. The book also guides you through identifying GenAI models and leveraging Azure OpenAI Service for content generation. At the end of each chapter, you’ll find chapter review questions with answers, provided as an online resource.By the end of this exam guide, you’ll be able to work with AI solutions in Azure and pass the AI-900 exam using the online exam prep resources.What you will learnDiscover various types of artificial intelligence (AI)workloads and services in AzureCover Microsoft's guiding principles for responsible AI development and useUnderstand the fundamental principles of how AI and machine learning workExplore how AI models can recognize content in images and documentsGain insights into the features and use cases for natural language processingExplore the capabilities of generative AI servicesWho this book is forWhether you're a cloud engineer, software developer, an aspiring data scientist, or simply interested in learning AI/ML concepts and capabilities on Azure, this book is for you. The book also serves as a foundation for those looking to attempt more advanced AI and data science-related certification exams (e.g. Microsoft Certified: Azure AI Engineer Associate). Although no experience in data science and software engineering is required, basic knowledge of cloud concepts and client-server applications is assumed. Table of ContentsIdentify Features of Common AI WorkloadsIdentify the Guiding Principles for Responsible AIIdentify Common Machine Learning TechniquesDescribe Core Machine Learning ConceptsDescribe Azure Machine Learning CapabilitiesIdentify Common Types of Computer Vision SolutionsIdentify Azure Tools and Services for Computer Vision TasksIdentify Features of Common NLP Workload ScenariosIdentify Azure Tools and Services for NLP WorkloadsIdentify Features of Generative AI SolutionsIdentify Capabilities of Azure OpenAI Service Read more

From fundamentals and design patterns to the latest techniques such as generative AI, machine learning and cloud native architecture, gain all you need to be a pro Solutions Architect crafting secure and reliable AWS architecture.Endorsements"Hits all the key areas" - Rajesh Sheth, VP, Elastic Block Store, AWS"Offers the knowledge you need to succeed in the evolving landscape of tech architecture" - Luis Lopez Soria, Senior Specialist Solutions Architect, Google"A valuable resource for enterprise strategists looking to build resilient applications" - Cher Simon, Principal Solutions Architect, AWSBook DescriptionMaster the art of solution architecture and excel as a Solutions Architect with the Solutions Architect's Handbook. Authored by seasoned AWS technology leaders Saurabh Shrivastav and Neelanjali Srivastav, this book goes beyond traditional certification guides, offering in-depth insights and advanced techniques to meet the specific needs and challenges of solutions architects today. This edition introduces exciting new features that keep you at the forefront of this evolving field. Large language models, generative AI, and innovations in deep learning are cutting-edge advancements shaping the future of technology. Topics such as cloud-native architecture, data engineering architecture, cloud optimization, mainframe modernization, and building cost-efficient and secure architectures remain important in today's landscape. This book provides coverage of these emerging and key technologies and walks you through solution architecture design from key principles, providing you with the knowledge you need to succeed as a Solutions Architect. It will also level up your soft skills, providing career-accelerating techniques to help you get ahead.Unlock the potential of cutting-edge technologies, gain practical insights from real-world scenarios, and enhance your solution architecture skills with the Solutions Architect's Handbook.What you will learnExplore various roles of a solutions architect in the enterpriseApply design principles for high-performance, cost-effective solutionsChoose the best strategies to secure your architectures and boost availabilityDevelop a DevOps and CloudOps mindset for collaboration, operational efficiency, and streamlined productionApply machine learning, data engineering, LLMs, and generative AI for improved security and performanceModernize legacy systems into cloud-native architectures with proven real-world strategiesMaster key solutions architect soft skillsWho this book is forThis book is for software developers, system engineers, DevOps engineers, architects, and team leaders who already work in the IT industry and aspire to become solutions architect professionals. Solutions architects who want to expand their skillset or get a better understanding of new technologies will also learn valuable new skills. To get started, you'll need a good understanding of the real-world software development process and some awareness of cloud technology.Table of ContentsSolutions Architecture in an OrganizationPrinciples of Solution Architecture DesignCloud Migration and Cloud Architecture DesignSolution Architecture Design PatternsCloud-Native Architecture Design PatternsPerformance ConsiderationsSecurity ConsiderationsArchitectural Reliability ConsiderationsOperational Excellence ConsiderationsCost ConsiderationsDevOps and Solution Architecture FrameworkData Engineering for Solution ArchitectureMachine Learning Architecture(N.B. Please use the Read sample option to see further chapters) Read more

Rust in Action is a hands-on guide to systems programming with Rust. Written for inquisitive programmers, it presents real-world use cases that go far beyond syntax and structure.SummaryRust in Action introduces the Rust programming language by exploring numerous systems programming concepts and techniques. You'll be learning Rust by delving into how computers work under the hood. You'll find yourself playing with persistent storage, memory, networking and even tinkering with CPU instructions. The book takes you through using Rust to extend other applications and teaches you tricks to write blindingly fast code. You'll also discover parallel and concurrent programming. Filled to the brim with real-life use cases and scenarios, you'll go beyond the Rust syntax and see what Rust has to offer in real-world use cases. About the technologyRust is the perfect language for systems programming. It delivers the low-level power of C along with rock-solid safety features that let you code fearlessly. Ideal for applications requiring concurrency, Rust programs are compact, readable, and blazingly fast. Best of all, Rust’s famously smart compiler helps you avoid even subtle coding errors.About the bookRust in Action is a hands-on guide to systems programming with Rust. Written for inquisitive programmers, it presents real-world use cases that go far beyond syntax and structure. You’ll explore Rust implementations for file manipulation, networking, and kernel-level programming and discover awesome techniques for parallelism and concurrency. Along the way, you’ll master Rust’s unique borrow checker model for memory management without a garbage collector.What's insideElementary to advanced Rust programmingPractical examples from systems programming Command-line, graphical and networked applications About the listenerFor intermediate programmers. No previous experience with Rust required.About the authorTim McNamara uses Rust to build data processing pipelines and generative art. He is an expert in natural language processing and data engineering. Table of Contents1 Introducing RustPart 1 Rust Language Distinctives2 Language foundations3 Compound data types4 Lifetimes, ownership, and borrowingPart 2 Demystifying Systems Programming5 Data in depth6 Memory7 Files and storage8 Networking9 Time and timekeeping10 Processes, threads, and containers11 Kernel12 Signals, interrupts, and exceptionsPLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio. Read more

Deep learning has revolutionized pattern recognition, introducing tools that power a wide range of technologies in such diverse fields as computer vision, natural language processing, and automatic speech recognition. Applying deep learning requires you to simultaneously understand how to cast a problem, the basic mathematics of modeling, the algorithms for fitting your models to data, and the engineering techniques to implement it all. This book is a comprehensive resource that makes deep learning approachable, while still providing sufficient technical depth to enable engineers, scientists, and students to use deep learning in their own work. No previous background in machine learning or deep learning is required―every concept is explained from scratch and the appendix provides a refresher on the mathematics needed. Runnable code is featured throughout, allowing you to develop your own intuition by putting key ideas into practice. Read more

Data Science Step-by-Step is an accessible yet detailed exploration of data science, designed to empower beginners and students and enhance the toolkit of seasoned professionals.Data is increasingly becoming the cornerstone across industries—from healthcare and finance to retail—and this book ensures you are equipped to harness its potential.Each chapter of Data Science Step-by-Step carefully walks you through different stages of the data science process, from acquiring and cleaning data to advanced techniques in machine learning and AI.Principles and foundations of data science are covered early in the book, followed by real-world applications that not only illustrate core concepts but also teach you how to apply them effectively. Topics such as data governance, feature engineering, and model deployment are covered in depth, providing you with a holistic understanding of the data science landscape.Key features include:Step-by-step instructions on data acquisition, exploration, and visualization.Comprehensive coverage of machine learning, from basic to advanced techniques.Practical insights into deploying models and ensuring they perform optimally in production environments.In-depth discussions on cutting-edge topics like AutoML, MLOps, and explainable AI.Whether you aim to improve business outcomes through data-driven decisions, aspire to become a data scientist, or simply have a passion for uncovering insights from data, Data Science Step-by-Step is your essential guide. Through clear explanations and a friendly tone, it breaks down complex concepts and makes them understandable for readers of all backgrounds.Prepare to be challenged, inspired, and equipped to turn data into actionable insights that can drive innovation and solve real-world problems.Topical Coverage Examples:Foundations of Data Science: Introduction to data science, statistical analysis, probability, linear algebra, calculus.Data Handling: Techniques in data acquisition (APIs, web scraping), data cleaning (normalization, outlier detection), and data preparation (feature engineering).Data Analysis and Exploration: Exploratory Data Analysis (EDA), statistical methods, data visualization with Matplotlib, Seaborn, Tableau.Machine Learning: Fundamentals (supervised, unsupervised learning), model building (regression, classification, clustering), advanced methods (neural networks, deep learning).Big Data Technologies: Handling and processing big data using Hadoop, Spark; data warehousing, ETL processes.Model Deployment and Operations: Deployment strategies, model monitoring, MLOps, AutoML.Ethical and Legal Considerations: Data ethics, privacy, data governance, lifecycle management.Current and Future Trends: AI advancements, quantum computing, industry applications in healthcare, finance, retail.Practical Applications and Case Studies: Real-world data science applications, problem-solving strategies (data science for business, marketing, supply chain, finance).Technical Skills and Tools: Programming (Python, R), version control (Git), collaboration tools.Data Science Step-by-Step is your gateway to mastering data science, from foundational knowledge to advanced applications and emerging technologies. Read more

Leverage the power of SQL to perform geospatial analysis and increase your speed and efficiency working with a variety of spatial applications such as PostGIS and QGISKey FeaturesFollow along with actionable instructions with this practical guideBecome well-versed in advanced spatial modeling and machine learning techniquesLearn best practices for performing spatial analysis from an expert spatial data analystBook DescriptionGeospatial analysis is industry agnostic and a powerful tool for answering location questions. Combined with the power of SQL, developers and analysts worldwide rely on database integration to solve real-world spatial problems. This book introduces skills to help you detect and quantify patterns in datasets through data exploration, visualization, data engineering, and the application of analysis and spatial techniques.You will begin by exploring the fundamentals of geospatial analysis where you’ll learn about the importance of geospatial analysis and how location information enhances data exploration. Walter Tobler’s second law of geography states, “the phenomenon external to a geographic area of interest affects what goes on inside.” This quote will be the framework of the geospatial questions we will explore. You’ll then observe the framework of geospatial analysis using SQL while learning to create spatial databases and SQL queries and functions.By the end of this book, you will have an expanded toolbox of analytic skills such as PostGIS and QGIS to explore data questions and analysis of spatial information.What you will learnUnderstand geospatial fundamentals as a basis for learning spatial SQLGenerate point, line, and polygon data with SQLUse spatial data types to abstract and encapsulate spatial structuresWork with open source GIS combined with plug-insVisualize spatial data and expand QGIS functionality with PostgresApply location data to leverage spatial analyticsPerform single-layer and multiple-layer spatial analysesWho this book is forThis book is for anyone looking to leverage their SQL knowledge to perform geospatial analysis. GIS analysts, data analysts, and data scientists with a basic understanding of both geospatial analysis and SQL will find this book useful.Table of ContentsIntroducing the Fundamentals of Geospatial AnalyticsConceptual Framework for SQL Spatial Data Science – Geometry Versus GeographyAnalyzing and Understanding Spatial AlgorithmsAn Overview of Spatial StatisticsUsing SQL Functions – Spatial and Non-SpatialBuilding SQL Queries Visually in a Graphical Query BuilderExploring PostGIS for Geographic AnalysisIntegrating SQL with QGIS Read more

Data driven discovery is revolutionizing how we model, predict. control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data driven methods, machine learning, applied optimization. classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity. generality. Topics range from introductory to research level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics informed machine learning, significant new sections throughout. chapter exercises. Online supplementary material including lecture videos per section, homeworks, data. code in MATLAB®, Python, Julia. R available on databookuw.com. Read more

If you’re a student studying computer science or a software developer preparing for technical interviews, this practical book will help you learn and review some of the most important ideas in software engineering—data structures and algorithms—in a way that’s clearer, more concise, and more engaging than other materials.By emphasizing practical knowledge and skills over theory, author Allen Downey shows you how to use data structures to implement efficient algorithms, and then analyze and measure their performance. You’ll explore the important classes in the Java collections framework (JCF), how they’re implemented, and how they’re expected to perform. Each chapter presents hands-on exercises supported by test code online.Use data structures such as lists and maps, and understand how they workBuild an application that reads Wikipedia pages, parses the contents, and navigates the resulting data treeAnalyze code to predict how fast it will run and how much memory it will requireWrite classes that implement the Map interface, using a hash table and binary search treeBuild a simple web search engine with a crawler, an indexer that stores web page contents, and a retriever that returns user query resultsOther books by Allen Downey include Think Java, Think Python, Think Stats, and Think Bayes. Read more

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Offering a basic understanding of each important topic in vacuum science and technology, this book concentrates on pumping issues, emphasizes the behavior of vacuum pumps and vacuum systems, and explains the relationships between pumps, instrumentation and high-vacuum system performance. The book delineates the technical and theoretical aspects of the subject without getting in too deep. It leads readers through the subtleties of vacuum technology without using a dissertation on mathematics to get them there. An interesting blend of easy-to-understand technician-level information combined with engineering data and formulae, the book provides a non-analytical introduction to high vacuum technology. Read more

The DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric is a comprehensive guide designed to help professionals prepare for the Microsoft Certified: Azure Data Engineer Associate exam. This book offers an in-depth exploration of Microsoft Fabric, equipping readers with the essential skills needed to design, implement, and manage data engineering solutions on the Azure platform.The book starts by providing an overview of the DP-700 certification, followed by a deep dive into Microsoft Fabric, its architecture, and the tools available for data engineering. You’ll learn how to efficiently manage and secure data, design scalable data storage solutions, and implement complex data pipelines using Azure Data Factory. Additionally, it covers the transformation and analysis of data with tools like Power Query, Synapse Dataflows, and Notebooks, empowering readers to create robust data workflows.A significant portion of the book is dedicated to optimizing workloads and resources in Microsoft Fabric, helping readers ensure high performance and cost-efficiency. Best practices for securing data, configuring Role-Based Access Control (RBAC), and ensuring compliance using Microsoft Purview are also thoroughly discussed. The book concludes with a section on troubleshooting common issues, monitoring performance, and scaling solutions to meet enterprise requirements.Packed with practical examples and expert tips, this guide serves as an indispensable resource for those looking to master the core concepts and technical skills required for the DP-700 certification exam and excel in data engineering careers using Microsoft Fabric. Read more

Learn everything you need to know to manage data as a product and shift toward a more modular and decentralized socio-technical data architecture to deliver business value in an incremental, measurable, and sustainable wayKey FeaturesLeverage data-as-product to unlock the modular platform potential and fix flaws in traditional monolithic architecturesLearn how to identify, implement, and operate data products throughout their life cycleDesign and execute a forward-thinking strategy to turn your data products into organizational assetsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionTraditional monolithic data platforms struggle with scalability and burden central data teams with excessive cognitive load, leading to challenges in managing technological debt. As maintenance costs escalate, these platforms lose their ability to provide sustained value over time. With two decades of hands-on experience implementing data solutions and his pioneering work in the Open Data Mesh Initiative, Andrea Gioia brings practical insights and proven strategies for transforming how organizations manage their data assets.Managing Data as a Product introduces a modular and distributed approach to data platform development, centered on the concept of data products. In this book, you’ll explore the rationale behind this shift, understand the core features and structure of data products, and learn how to identify, develop, and operate them in a production environment. The book guides you through designing and implementing an incremental, value-driven strategy for adopting data product-centered architectures, including strategies for securing buy-in from stakeholders. Additionally, it explores data modeling in distributed environments, emphasizing its crucial role in fully leveraging modern generative AI solutions.By the end of this book, you’ll have gained a comprehensive understanding of product-centric data architecture and the essential steps needed to adopt this modern approach to data management.What you will learnOvercome the challenges in scaling monolithic data platforms, including cognitive load, tech debt, and maintenance costsDiscover the benefits of adopting a data-as-a-product approach for scalability and sustainabilityNavigate the complete data product lifecycle, from inception to decommissioningAutomate data product lifecycle management using a self-serve platformImplement an incremental, value-driven strategy for transitioning to data-product-centric architecturesOptimize data modeling in distributed environments to enhance GenAI-based use casesWho this book is forIf you’re an experienced data engineer, data leader, architect, or practitioner committed to reimagining your data architecture and designing one that enables your organization to get the most value from your data in a sustainable and scalable way, this book is for you. Whether you’re a staff engineer, product manager, or a software engineering leader or executive, you’ll find this book useful. Familiarity with basic data engineering principles and practices is assumed.Table of ContentsFrom Data as a Byproduct to Data as a ProductData ProductsData Product-Centered ArchitecturesIdentifying Data Products and Prioritizing DevelopmentsDesigning and Implementing Data ProductsOperating Data Products in ProductionAutomating Data Product Lifecycle ManagementMoving through the Adoption JourneyTeam Topologies and Data Ownership at ScaleDistributed Data ModelingBuilding an AI-Ready Information ArchitectureBringing It All Together Read more

This Oilfield Data Pocket Handbook is designed to be an essential quick-reference guide for oil & gas professionals. Whether you're working in drilling, well control, production, or field supervision, this book keeps critical technical data, calculations, and charts easily accessible when you need them most. Developed by experienced oilfield experts.📌 Inside This Handbook:✅ Drilling Formulas & Calculations – Includes pressure, volume, displacement, and mud weight formulas✅ Casing & Drill Pipe Data – OD, weight, grade, capacity, and displacement tables✅ Hole & Annular Capacity Charts – For cementing, fluids, and displacement planning✅ Torque Charts & Pipe Strength Tables – For safe make-up and operational limits✅ Hydraulics & Mud Engineering Data – Pump output, fluid properties, and rheology references✅ Unit Conversion Tables & Safety Systems – Including friction loss, pressure drops, and BOP references✅ 160 Pages in a 4" x 8.5" Pocket Format – Compact, durable, and rig-ready📖 Who Will Benefit from This Handbook?🔹 Drillers, Toolpushers & Rig Hands🔹 Directional Drillers & Mud Engineers🔹 Drilling Supervisors & Company Men🔹 Wellsite Geologists & Petroleum Engineers🔹 Anyone Needing a Reliable Oilfield Calculation & Reference Guide💡 Boost efficiency, reduce downtime, and keep your key drilling data right in your pocket.👉 Get your copy of the Oilfield Data Pocket Handbook today! Read more

Cracking the Data Science Interview is the first book that attempts to capture the essence of data science in a concise, compact, and clean manner. In a Cracking the Coding Interview style, Cracking the Data Science Interview first introduces the relevant concepts, then presents a series of interview questions to help you solidify your understanding and prepare you for your next interview. Topics include: • Necessary Prerequisites (statistics, probability, linear algebra, and computer science) • 18 Big Ideas in Data Science (such as Occam’s Razor, Overfitting, Bias/Variance Tradeoff, Cloud Computing, and Curse of Dimensionality) • Data Wrangling (exploratory data analysis, feature engineering, data cleaning and visualization) • Machine Learning Models (such as k-NN, random forests, boosting, neural networks, k-means clustering, PCA, and more) • Reinforcement Learning (Q-Learning and Deep Q-Learning) • Non-Machine Learning Tools (graph theory, ARIMA, linear programming) • Case Studies (a look at what data science means at companies like Amazon and Uber) Maverick holds a bachelor’s degree from the College of Engineering at Cornell University in operations research and information engineering (ORIE) and a minor in computer science. He is the author of the popular Data Science Cheatsheet and Data Engineering Cheatsheet on GCP and has previous experience in data science consulting for a Fortune 500 company focusing on fraud analytics. Read more

Data projects are an intrinsic part of an organization’s technical ecosystem, but data engineers in many companies continue to work on problems that others have already solved. This hands-on guide shows you how to provide valuable data by focusing on various aspects of data engineering, including data ingestion, data quality, idempotency, and more.Author Bartosz Konieczny guides you through the process of building reliable end-to-end data engineering projects, from data ingestion to data observability, focusing on data engineering design patterns that solve common business problems in a secure and storage-optimized manner. Each pattern includes a user-facing description of the problem, solutions, and consequences that place the pattern into the context of real-life scenarios.Throughout this journey, you’ll use open source data tools and public cloud services to apply each pattern. You'll learn:Challenges data engineers face and their impact on data systemsHow these challenges relate to data system componentsUseful applications of data engineering patternsHow to identify and fix issues with your current data componentsTTechnology-agnostic solutions to new and existing data projects, with open source implementation examplesBartosz Konieczny is a freelance data engineer who's been coding since 2010. He's held various senior hands-on positions that allowed him to work on many data engineering problems in batch and stream processing. Read more

Unlock the Secrets Behind Mathematics' Most Essential Method!Whether you're a student tackling your first course, a scientist applying mathematical tools, or even a curious mind wondering how Google ranks webpages, this comprehensive textbook gently guides you from the fundamentals of solving simple equations through the most insightful matrix transformations and vector analysis. Dive deep into the most vital concepts underpinning modern mathematics, physics, engineering, data science, and more, simplifying even the most complex ideas clearly and intuitively.✅ Step-by-Step Clarity: Master key topics like Systems of Linear Equations, Gaussian Elimination, Matrix Operations, and Determinants with detailed walkthroughs that keep you confident every step of the way.✅ Conceptual Insights: Clearly understand abstract concepts such as Vector Spaces, Eigenvalues and Eigenvectors, Orthogonality, and Diagonalization through practical, engaging explanations and relatable examples.✅ Practical Mastery: Harness powerful techniques such as Singular Value Decomposition (SVD), LU and QR Factorizations, and the Least Squares Method to handle real-world problems in fields as diverse as economics, physics, machine learning, and computer graphics.Throughout these pages, numerous illustrative examples help ground your learning in real-world scenarios:Predicting population dynamics and ecological outcomes using matrix modeling.Using Google’s Page Rank algorithm explained through eigenvectors.Analyzing economic models and business forecasting with determinants and matrix algebra.Perfecting data-fitting and minimizing error in scientific experiments using Least Squares Approximations.Simplifying computations and visualizing data using Orthogonal Projections and the Gram-Schmidt Process.Structured precisely for learners at all levels, this book delivers clarity and versatility for self-study, classwork, and professional reference alike. Transform confusion into confidence, complexity into clarity, and linear algebra into your greatest mathematical asset.Take control of your understanding, boost your career prospects, and sharpen your analytical thinking today! Read more

Designing Data-Intensive Applications: 2 Books in 1 – Mastering Scalable Data Pipelines and Real-Time SystemsBook 1: Designing Data-Intensive Applications: From Core Concepts to Scalable ArchitecturesBook 2: Building Scalable Data Pipelines – Hands-On Projects for Batch and Real-Time ProcessingThis isn’t just another tech book—it’s your complete roadmap to mastering data engineering and building scalable, high-performance data systems. From designing distributed architectures to creating data pipelines, this 2-in-1 bundle combines foundational concepts with hands-on projects, delivering a practical and immersive learning experience.Whether you’re a data engineer, software developer, or aspiring tech professional, theory alone won’t cut it—you need actionable skills. This guide seamlessly bridges the gap between knowledge and implementation, equipping you with the tools to design, build, and optimize data systems that meet the demands of today’s fast-paced, data-driven world.Here’s What You’ll Master:Core Principles of Data System Design: Grasp the essentials of data modeling, lifecycle management, and fault-tolerant systems.Hands-On Data Pipeline Projects: Build batch and real-time pipelines with tools like Apache Kafka, Spark, and cloud platforms.Scalable Distributed Systems: Learn to implement microservices and distributed databases for robust architectures.Performance Optimization Techniques: Apply caching, load balancing, and query tuning to ensure top-tier system efficiency.Real-World Applications: Work on practical projects like recommendation engines, analytics dashboards, and event-driven pipelines.Best Practices for Cloud Deployment: Master cloud platforms like AWS and GCP to deploy and scale your systems.System Design Interview Prep: Boost your confidence with strategies and frameworks to tackle complex design challenges.Event-Driven Architectures: Master the design and implementation of systems that react to real-time events, ensuring flexibility and responsiveness in your applications.Data Lake and Warehouse Design: Understand the differences and learn how to build scalable storage solutions for structured and unstructured data.CI/CD for Data Pipelines: Automate the deployment and testing of your data systems using Continuous Integration and Continuous Delivery practices, ensuring faster and more reliable updates....and so, so much more.What Makes This Book Unique?Action-Oriented Learning: Each chapter includes hands-on projects that reinforce key concepts.Comprehensive and Practical: Covers both batch and real-time processing to ensure you’re ready for any data challenge.Career-Boosting: Equips you with skills highly sought after in data engineering, making you an invaluable asset in any tech role.Who Should Read This Book?Data Engineers and Developers: Build scalable, reliable data systems.Aspiring Data Professionals: Transition into data engineering with practical skills.Tech Students and Practitioners: Expand your knowledge of real-time and batch processing.Don’t just learn data systems—build them. This book provides the practical tools and knowledge you need to excel in your career.Click “Buy Now” and start designing scalable, data-intensive applications today! 🚀 Read more

4 Books in 1: Transform Your SQL Skills - From Beginner to Advanced Mastery:Struggling with complex SQL queries?Need to learn SQL really fast?Want to crush those SQL interview questions with confidence?This comprehensive SQL guide is your go-to cheat sheet for mastering SQL programming. Packed with practical skills, no fluff – just the tools that boost your career. With this guide, you’ll deal with any data challenge confidently and ace your SQL interviews.What's Inside:SQL Fundamentals: Build a solid foundationAdvanced SQL Techniques: Level up your skillsExpert SQL Strategies: Master cutting-edge conceptsSQL Interview Prep: Ace your next database job interviewKey Features:270+ pages of SQL content for beginners to advanced users350+ real-world, practical SQL examples and exercises you can use today140+ SQL interview questions and tasks with solutions, to boost your confidenceComplete e-commerce database and all SQL code available as a free digital downloadExclusive BONUS - Grab Your FREE Downloadable Content!Complete SQL Code Library: All SQL Statements from the Book, Ready to Run! Hands-On Learning: Complete E-commerce Database for Real-World Practice!SQL Cheat Sheet #1: Must-Know SQL Terms Glossary!SQL Cheat Sheet #2: SQL Dialect Breakdown for the 4 Most Popular Databases!This SQL Crash Course book is perfect for:SQL beginners starting their database journeyDevelopers enhancing their SQL, T-SQL and PL/SQL skillsData analysts sharpening their SQL for data analytics toolkitDatabase administrators mastering SQL database managementJob seekers preparing for SQL coding interviewsWhy buy multiple books when one can take you from SQL beginner to SQL pro?Here are the challenges this SQL guide solves for you:Confused by SQL dialects? Master PostgreSQL techniques that work across platforms, including MySQL and SQL Server.Need a SQL quickstart guide? Our step-by-step approach takes you from SQL basics to advanced SQL programming.Want SQL hands-on practice? Work with our e-commerce database example throughout the book.Ready for SQL server query optimization? Learn performance tuning and tackle complex queries with ease.Unsure about data modeling? Master database design and efficient SQL indexing for any scenario.Keeping up with trends? Explore cutting-edge topics like cloud databases, NoSQL, and more.Preparing for a job? Our SQL interview prep section covers everything from SQL joins to window functions.Boost your data engineering skills with practical SQL for data analysis, ETL processes, and big data handling.This isn't just another basic 'SQL for Dummies' book – it's a complete SQL guide designed to help you master real-world SQL skills, tackle complex challenges, and excel in your career!Remember: In tech, the best time to level up your SQL skills was yesterday. The next best time is now. Grab this practical SQL book and start your journey to database mastery today! Read more

Master the Databricks Data Engineer Certification — with ConfidenceLooking to validate your data engineering skills and pass the Databricks Certified Data Engineer Associate exam? This practical and comprehensive guide walks you through every concept you need — from Spark and Delta Lake to structured streaming and pipeline orchestration.Designed for beginners and professionals alike, this book simplifies the technical details and prepares you with a clear, exam-focused approach.What you'll learn inside:Understand the Databricks Lakehouse Platform and its core toolsWork with Spark: transformations, actions, joins, aggregations, and optimizationsBuild real-time data pipelines using Structured StreamingManage and optimize Delta Lake tables with ACID transactionsOrchestrate production-grade jobs and workflows in DatabricksNavigate the Databricks UI, CLI, Repos, Unity Catalog, and job configurationsGet hands-on with mock exams and sample questions aligned to the exam objectivesIdeal for:Aspiring data engineersData professionals transitioning into the Databricks ecosystemEngineers preparing for the Databricks Associate Certification ExamDevelopers, analysts, or data scientists seeking a structured path into data engineeringWhether you're targeting certification or simply want to become fluent in one of the most powerful data platforms today, this book is your companion to prepare, practice, and perform at your best. Read more

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.The only Oracle Press guide to MySQL Workbench explains how to design and model MySQL databases.MySQL Workbench Data Modeling and Development helps developers learn how to effectively use this powerful product for database modeling, reverse engineering, and interaction with the database without writing SQL statements. MySQL Workbench is a graphical user interface that can be used to create and maintain MySQL databases without coding. The book covers the interface and explains how to accomplish each step by illustrating best practices visually. Clear examples, instructions, and explanations reveal, in a single volume, the art of database modeling. This Oracle Press guide shows you how to get the tool to do what you want. Annotated screen shots demonstrate all interactions with the tool, and text explains the how, what, and why of each step.Complete coverageInstallation and Configuration; Creating and Managing Connections; Data Modeling Concepts; Creating an ERD; Defining the Physical Schemata; Creating and Managing Tables; Creating and Managing Relationships; Creating and Managing Views; Creating and Managing Routines; Creating and Managing Routine Groups; Creating and Managing User & Groups; Creating and Managing SQL Scripts; Generating SQL Scripts; Forward Engineering a Data Model; Synchronize a Model with a Database; Reverse Engineering a Database; Managing Differences in the Data Catalog; Creating and Managing Model Notes; Editing Table Data; Editing Generated Scripts; Creating New Instances; Managing Import and Export; Managing Security; Managing Server Instances Read more

🚀 UNLOCK THE POWER OF ADVANCED EXCEL FORMULAS: DISCOVER THE SECRETS TO ELEVATE YOUR EFFICIENCY WITH ADVANCED TECHNIQUES AND GAME-CHANGING FORMULAS! 🚀This book includes two FREE bonuses:🎁 Bonus 1: Two downloadable workbooks to practice and follow along with the examples from the book.🎁 Bonus 2: Online VBA course to automate your worksheets.Are you struggling to keep up with complex Excel tasks or spending too much time on repetitive processes? This Advanced Excel Formulas and Functions book is your ultimate solution to mastering Microsoft Excel and skyrocketing your efficiency. Whether you're a seasoned professional or someone looking to level up your skills, this essential Microsoft Excel book will transform the way you work.WHO THIS BOOK IS FORThis Excel book is perfect for professionals, analysts, data enthusiasts and anyone who regularly works with data in Excel who want to take their Excel skills from basic to extraordinary. This Excel guide is designed to cater for users of Excel 365 who are eager to learn advanced techniques and save valuable time.CHALLENGES THIS EXCEL HANDBOOK WILL SOLVEAre you facing challenges like managing complex formulas, analysing large datasets, or automating repetitive tasks? This advanced Microsoft 365 bible will help you solve:Conditional Logic Issues: Master IF, AND, OR, and NESTED IFS to handle intricate decision-making processes effortlessly.Text Manipulation Difficulties: Use TEXTSPLIT, TEXTBEFORE, TEXTAFTER, CLEAN, and UNICHAR to clean and manipulate text data with ease.Complex Financial Calculations: Simplify financial tasks with PMT, IPMT, SLN, DB, and CUMIPMT for accurate loan, depreciation, and investment calculations.Lookup Challenges: Enhance data retrieval with CHOOSE, ADDRESS, HYPERLINK, OFFSET, CHOOSEROWS, CHOOSECOLS, and SORTBY for dynamic and flexible lookups.Handling Array Data: Utilise TRANSPOSE, SEQUENCE, UNIQUE, FILTER, VSTACK, HSTACK, TAKE, DROP and array formulas to manage and analyse array data effectively.Mathematical and Engineering Calculations: Execute complex mathematical operations with MROUND, FACT, GCD, LCM, COMPLEX, DEC2BIN, and BIN2DEC.Custom Function Creation: Develop LAMBDA functions to automate and customise repetitive tasks according to your specific needs.And more!: Altogether there are over 70 advanced Excel formulas to master in this Excel 2025 book!WHAT RESULTS YOU WILL GET FROM BUYING THIS EXCEL FORMULAS BOOKBy mastering these advanced Excel formulas and functions, you will achieve:Enhanced Productivity: You will learn how to automate complex tasks, saving time and reducing errors in your work.Data Analysis Mastery: Gain the skills to analyse large datasets with ease, making data-driven decisions more effectively.Career Advancement: Mastery of advanced Excel skills will make you a more valuable employee, opening doors for promotions and new job opportunities.GET YOUR COPY NOW!Ready to transform your Excel experience? Order your copy of this advanced Excel formulas book today before the price changes and unlock the full potential of Excel.👉 Order now by clicking the BUY NOW button at the top right of this page! 👈 Read more

The CompTIA A+ Exam Will Be a Piece of Cake With These Exclusive Tips and Tricks - Find Guidance for Real-World Problem Solving, Hardware and Tools Recommended by Top IT Professionals, and Useful Study Tips to Ace Your Exam!Are you aiming for a CompTIA A+ certification?Are you worried about the contents and difficulty of the exam?Are you ready to take a deep dive into everything about Networks?If you’re an aspiring IT professional that wants to pass the CompTIA Security+ exam with flying colors, then this is the book for you!It is completely normal to feel anxious when you’re about to take the exam. Simply studying for the exam can be nerve-wracking as well because of all the topics you’ll have to cover.After all, the exam is not easy.But, with the right guidance, it can transform the test from a difficult and intense exam, into a simpler, much more pleasant experience.Hone real-life skills and get exclusive advice from top IT professionals! With this book, you’ll go over all the topics you need to know, from top to bottom, and everything will be explained in easy-to-understand terms.In this book, you’ll discover:The 9 main topics of the exam: Find in-depth explanations of all these topics and learn how to swiftly answer each problem.Useful real-life applications: Know that everything you study isn’t just so you can get your certificate. Everything you’ll take everything you learn in this book into your professional life!Recommendations for a stress-free life: Discover which tools, hardware, and software are the best for you and go through the exam like a breeze.Enough stressing day and night about the contents of the exam and how well you’ll do. This book will guide you every step of the way and teach you everything you need to know!The CompTIA A+ Certificate is already in your hands.Scroll up, Click on “Buy Now”, and Get Your Copy Now!Key topics covered include-Mobile Devices-Laptop Hardware (Screens, Hardware, Memory, Smart Card Readers, Batteries, Touchpads, Speakers, CPUs, etc.)-Docking Stations & Port Replicators-Tablets, Smartphones, Wearable Technology, GPS, e-Readers-Wireless Connections (Wi-Fi, NFC, Bluetooth, Zigbee, Z-wave, LTE, RFID)-Networking -Ports and Protocols-Routers, Switches, Firewalls, Access Points, Controllers, Network Interface Cards, Repeaters, Hubs, Bridges, Modems, Patch Panels, PoE-IP Addressing-VPN, VLAN, NAT, DNS, DHCP-Networking tools -Cable and connector types-RAM-HDDs and SSDs-Motherboards-Peripherals-Power Supplies-Device Configuration-Printers-Cloud-Virtualization-Hardware & Software Troubleshooting-Printer Troubleshooting-Network Troubleshooting-File Systems-Command Line Tools-Windows, Mac, UNIX operating systems-Physical Security-Active Directory-Wireless Security-Malware-Social Engineering-Physical Data Security & Destruction-Documentation-Change Management-Disaster Recovery-Safety-Environmental Impacts-Agreements-Professionalism-Scripting-Remote Access Read more

A step-by-step guide to harness the power of Microsoft Fabric in developing data analytics solutions for various use casesKey FeaturesExplore Microsoft Fabric and its features with real-world examplesBuild data analytic solutions for lakehouse, data warehouse, real-time analytics, and data scienceMonitor, manage, and administer your Fabric platform and analytics system for flexibility, performance, security, and controlBook DescriptionMicrosoft Fabric is the first and only service/product in the market to offer a unified and comprehensive solution designed for the AI era. It seamlessly brings together data integration, OneLake, transformation, visualization, universal security, and a unified business model.Learn Microsoft Fabric provides an overview of Microsoft Fabric, its components, and the wider analytics landscape. You'll explore workloads such as Data Factory, Synapse Data Engineering, data science, data warehouse, real-time analytics, and Power BI. You’ll then learn to build end-to-end lakehouse and data warehouse solutions using the medallion architecture. You’ll unlock the potential of real-time analytics and implement machine learning and AI models. As you move ahead, you’ll learn how to monitor workloads, and administer Fabric across tenants, capacities, and workspaces. You’ll enhance security and governance practices in Microsoft Fabric. You’ll implement CI/CD workflows with Azure DevOps or GitHub. In the end, you’ll discover the power of Copilot, an AI-driven assistant that accelerates your analytics journey.By the end of this book, you’ll have unlocked the full potential of AI-driven data analytics, gaining a comprehensive understanding of the analytics landscape and essential concepts and principles of Microsoft Fabric.What you will learnExplore Microsoft Fabric and the different services it providesBuild end-to-end data analytics solution to scale and manage high performanceIntegrate data from different types of data sourcesApply transformation with Spark, Notebook, and T-SQLUnderstand, learn, and implement real-time stream processing and data science capabilitiesPerform end-to-end processes for building data analytics solutions in the AI eraDrive insights by leveraging Power BI for reporting and visualizationImprove productivity with AI assistance and Copilot integrationWho This Book Is ForThis book is intended for data professionals, such as data analysts, data engineers, data scientists, data warehouse developers, ETL developers, business analysts, AI/ML professionals, software developers, and Chief Data Officers, who want to build a future-ready data analytics solution for the era of AI in driving their organization for long-term success. For PySpark and SQL students getting into the data analytics domain, the book offers a broad foundation to develop skills to build end-to-end analytics systems for different use cases. Basic knowledge of SQL and Spark is presumed.Table of ContentsOverview of Microsoft Fabric and Understanding Different ConceptsUnderstanding Different Workloads in Microsoft Fabric and Getting StartedLakehouse (Medallion Architecture) and Data MeshData WarehouseReal-time Analytics CapabilitiesExploring Data Science CapabilitiesMonitoring overview and monitoring different workloadsAdministering FabricSecurity and Governance overviewContinuous Integration and Continuous Deployment (CI/CD)Overview - AI Assisted with Co-pilot integration Read more