This concise yet comprehensive guide explains how to adopt a data lakehouse architecture to implement modern data platforms. It reviews the design considerations, challenges, and best practices for implementing a lakehouse and provides key insights into the ways that using a lakehouse can impact your data platform, from managing structured and unstructured data and supporting BI and AI/ML use cases to enabling more rigorous data governance and security measures. Practical Lakehouse Architecture shows you how to: Understand key lakehouse concepts and features like transaction support, time travel, and schema evolution Understand the differences between traditional and lakehouse data architectures Differentiate between various file formats and table formats Design lakehouse architecture layers for storage, compute, metadata management, and data consumption Implement data governance and data security within the platform Evaluate technologies and decide on the best technology stack to implement the lakehouse for your use case Make critical design decisions and address practical challenges to build a future-ready data platform Start your lakehouse implementation journey and migrate data from existing systems to the lakehouse

This book is a comprehensive exploration of bio-inspired optimization techniques and their potential applications in healthcare. Bio-Inspired Optimization for Medical Data Mining is a groundbreaking book that delves into the convergence of nature’s ingenious algorithms and cutting-edge healthcare technology. Through a comprehensive exploration of state-of-the-art algorithms and practical case studies, readers gain unparalleled insights into optimizing medical data processing, enabling more precise diagnosis, optimizing treatment plans, and ultimately advancing the field of healthcare. Organized into 15 chapters, readers learn about the theoretical foundation of pragmatic implementation strategies and actionable advice. In addition, it addresses current developments in molecular subtyping and how they can enhance clinical care. By bridging the gap between cutting-edge technology and critical healthcare challenges, this book is a pivotal contribution, providing a roadmap for leveraging nature-inspired algorithms. In this book, the reader will discover Cutting-edge bio-inspired algorithms designed to optimize medical data processing, providing efficient and accurate solutions for complex healthcare challenges; How bio-inspired optimization can fine-tune diagnostic accuracy, leading to better patient outcomes and improved medical decision-making; How bio-inspired optimization propels healthcare into a new era, unlocking transformative solutions for medical data analysis; Practical insights and actionable advice on implementing bio-inspired optimization techniques and equipping effective real-world medical data scenarios; Compelling case studies illustrating how bio-inspired optimization has made a significant impact in the medical field, inspiring similar success stories. Audience This book is designed for a wide-ranging audience, including medical professionals, healthcare researchers, data scientists, and technology enthusiasts.

Discover how to design and optimize modern databases efficiently using PostgreSQL and MySQL. This book guides you through database design for scalability and performance, covering data modeling, query optimization, and real-world application integration. What this Book will help me do Build efficient and scalable relational database schemas for real-world applications. Master data modeling with normalization and denormalization techniques. Understand query optimization strategies for better database performance. Learn database strategies such as sharding, replication, and backup management. Integrate relational databases with applications and explore future database trends. Author(s) Alkin Tezuysal and Ibrar Ahmed are seasoned database professionals with decades of experience. Alkin specializes in database scalability and performance, while Ibrar brings expertise in database systems and development. Together, they bring a hands-on approach, providing clear and insightful guidance for database professionals. Who is it for? This book is oriented towards software developers, database administrators, and IT professionals looking to enhance their knowledge in database design using PostgreSQL and MySQL. Beginners in database design will find its structured approach approachable. Advanced professionals will appreciate its depth on cutting-edge topics and practical optimizations.

Discover how to seamlessly integrate data science into your leadership toolkit with 'Data Science for Decision Makers.' This practical guide emphasizes bridging business challenges with technical data insights, enabling you to make informed decisions leveraging modern data-driven methodologies. What this Book will help me do Gain foundational knowledge of statistics and machine learning to interpret data and drive insights. Learn to plan, execute, and evaluate data science projects effectively from start to finish. Understand the differences between machine learning, statistical methods, and traditional analysis and when to employ each. Acquire tools to manage and maximize the capabilities of high-performing data teams. Develop the skills to translate business challenges into data science problems for actionable solutions. Author(s) The author, None Howells, comes with an extensive background in data science leadership and AI technologies. With years of experience in guiding organizations through implementing data science solutions, they bring clarity and practicality to tackling complex problems. Their writing aims to be an accessible resource for both technical professionals taking on managerial roles and executives looking to understand the potential of data science. Who is it for? This book is tailored for executives, such as CDOs, data managers, or business leaders, who wish to understand data science concepts and their applications. It's also valuable for managers of technical teams aiming to bridge communication gaps and improve project outcomes. If you are at the intersection of leadership and data challenges, this book provides essential context and tools to thrive.

An accessible learning resource that develops data analysis skills for natural science students in an efficient style using the R programming language R-ticulate: A Beginner’s Guide to Data Analysis for Natural Scientists is a compact, example-based, and user-friendly statistics textbook without unnecessary frills, but instead filled with engaging, relatable examples, practical tips, online exercises, resources, and references to extensions, all on a level that follows contemporary curricula taught in large parts of the world. The content structure is unique in the sense that statistical skills are introduced at the same time as software (programming) skills in R. This is by far the best way of teaching from the authors’ experience. Readers of this introductory text will find: Explanations of statistical concepts in simple, easy-to-understand language A variety of approaches to problem solving using both base R and tidyverse Boxes dedicated to specific topics and margin text that summarizes key points A clearly outlined schedule organized into 12 chapters corresponding to the 12 semester weeks of most universities While at its core a traditional printed book, R-ticulate: A Beginner’s Guide to Data Analysis for Natural Scientists comes with a wealth of online teaching material, making it an ideal and efficient reference for students who wish to gain a thorough understanding of the subject, as well as for instructors teaching related courses.

Fine-tune your marketing research with this cutting-edge statistical toolkit Bayesian Statistics and Marketing illustrates the potential for applying a Bayesian approach to some of the most challenging and important problems in marketing. Analyzing household and consumer data, predicting product performance, and custom-targeting campaigns are only a few of the areas in which Bayesian approaches promise revolutionary results. This book provides a comprehensive, accessible overview of this subject essential for any statistically informed marketing researcher or practitioner. Economists and other social scientists will find a comprehensive treatment of many Bayesian methods that are central to the problems in social science more generally. This includes a practical approach to computationally challenging problems in random coefficient models, non-parametrics, and the problems of endogeneity. Readers of the second edition of Bayesian Statistics and Marketing will also find: Discussion of Bayesian methods in text analysis and Machine Learning Updates throughout reflecting the latest research and applications Discussion of modern statistical software, including an introduction to the R package bayesm, which implements all models incorporated here Extensive case studies throughout to link theory and practice Bayesian Statistics and Marketing is ideal for advanced students and researchers in marketing, business, and economics departments, as well as for any statistically savvy marketing practitioner.

In today's fast-paced world, more and more organizations require rapid application development with reduced development costs and increased productivity. This practical guide shows application developers how to use PowerApps, Microsoft's no-code/low-code application framework that helps developers speed up development, modernize business processes, and solve tough challenges. Author Arpit Shrivastava provides a comprehensive overview of designing and building cost-effective applications with Microsoft Power Apps. You'll learn fundamental concepts behind low-code and no-code development, how to build applications using pre-built and blank templates, how to design an app using Copilot AI and drag and drop PowerPoint-like controls, use Excel-like expressions to write business logic for an app, and integrate apps with external data sources. With this book, you'll: Learn the importance of no-code/low-code application development Design mobile/tablet (canvas apps) applications using pre-built and blank templates Design web applications (model-driven apps) using low-code, no-code, and pro-code components Integrate PowerApps with external applications Learn basic coding concepts like JavaScript, Power Fx, and C# Apply best practices to customize Dynamics 365 CE applications Dive into Azure DevOps and ALM concepts to automate application deployment

Can you forecast the future value by considering historical data? Accurate forecasting requires more than just plugging in historical data into models. Readers will find the latest techniques used by managers in business today, discover the importance of forecasting and learn how it is accomplished.

Big Data on Kubernetes is your comprehensive guide to leveraging Kubernetes for scalable and efficient big data solutions. You will learn key concepts of Kubernetes architecture and explore tools like Apache Spark, Airflow, and Kafka. Gain hands-on experience building complete data pipelines to tackle real-world data challenges. What this Book will help me do Understand Kubernetes architecture and learn to deploy and manage clusters. Build and orchestrate big data pipelines using Spark, Airflow, and Kafka. Develop scalable and resilient data solutions with Docker and Kubernetes. Integrate and optimize data tools for real-time ingestion and processing. Apply concepts to hands-on projects addressing actual big data scenarios. Author(s) Neylson Crepalde is an experienced data specialist with extensive knowledge of Kubernetes and big data solutions. With deep practical experience, Neylson brings real-world insights to his writing. His approach emphasizes actionable guidance and relatable problem-solving with a strong foundation in scalable architecture. Who is it for? This book is ideal for data engineers, BI analysts, data team leaders, and tech managers familiar with Python, SQL, and YAML. Targeted at professionals seeking to develop or expand their expertise in scalable big data solutions, it provides practical insights into Docker, Kubernetes, and prominent big data tools.

The book is a comprehensive guide that explores the use of artificial intelligence and machine learning in drug discovery and development covering a range of topics, including the use of molecular modeling, docking, identifying targets, selecting compounds, and optimizing drugs. The intersection of Artificial Intelligence (AI) and Machine Learning (ML) within the field of drug design and development represents a pivotal moment in the history of healthcare and pharmaceuticals. The remarkable synergy between cutting-edge technology and the life sciences has ushered in a new era of possibilities, offering unprecedented opportunities, formidable challenges, and a tantalizing glimpse into the future of medicine. AI can be applied to all the key areas of the pharmaceutical industry, such as drug discovery and development, drug repurposing, and improving productivity within a short period. Contemporary methods have shown promising results in facilitating the discovery of drugs to target different diseases. Moreover, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the vast amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs that can be repurposed for alternative use in medicine. This book is a comprehensive exploration of this dynamic and rapidly evolving field. In an era where precision and efficiency are paramount in drug discovery, AI and ML have emerged as transformative tools, reshaping the way we identify, design, and develop pharmaceuticals. This book is a testament to the profound impact these technologies have had and will continue to have on the pharmaceutical industry, healthcare, and ultimately, patient well-being. The editors of this volume have assembled a distinguished group of experts, researchers, and thought leaders from both the AI, ML, and pharmaceutical domains. Their collective knowledge and insights illuminate the multifaceted landscape of AI and ML in drug design and development, offering a roadmap for navigating its complexities and harnessing its potential. In each section, readers will find a rich tapestry of knowledge, case studies, and expert opinions, providing a 360-degree view of AI and ML’s role in drug design and development. Whether you are a researcher, scientist, industry professional, policymaker, or simply curious about the future of medicine, this book offers 19 state-of-the-art chapters providing valuable insights and a compass to navigate the exciting journey ahead. Audience The book is a valuable resource for a wide range of professionals in the pharmaceutical and allied industries including researchers, scientists, engineers, and laboratory workers in the field of drug discovery and development, who want to learn about the latest techniques in machine learning and AI, as well as information technology professionals who are interested in the application of machine learning and artificial intelligence in drug development.

Break down biostatistics, make sense of complex concepts, and pass your class If you're taking biostatistics, you may need or want a little extra assistance as you make your way through. Biostatistics For Dummies follows a typical biostatistics course at the college level, helping you understand even the most difficult concepts, so you can get the grade you need. Start at the beginning by learning how to read and understand mathematical equations and conduct clinical research. Then, use your knowledge to analyze and graph your data. This new edition includes more example problems with step-by-step walkthroughs on how to use statistical software to analyze large datasets. Biostatistics For Dummies is your go-to guide for making sense of it all. Review basic statistics and decode mathematical equations Learn how to analyze and graph data from clinical research studies Look for relationships with correlation and regression Use software to properly analyze large datasets Anyone studying in clinical science, public health, pharmaceutical sciences, chemistry, and epidemiology-related fields will want this book to get through that biostatistics course.

Information Modeling and Relational Databases, Third Edition, provides an introduction to ORM (Object-Role Modeling) and much more. In fact, it is the only book to go beyond introductory coverage and provide all of the in-depth instruction you need to transform knowledge from domain experts into a sound database design. This book is intended for anyone with a stake in the accuracy and efficacy of databases: systems analysts, information modelers, database designers and administrators, and programmers. Dr. Terry Halpin and Dr. Tony Morgan, pioneers in the development of ORM, blend conceptual information with practical instruction that will let you begin using ORM effectively as soon as possible. The all-new Third Edition includes coverage of advances and improvements in ORM and UML, nominalization, relational mapping, SQL, XML, data interchange, NoSQL databases, ontological modeling, and post-relational databases. Supported by examples, exercises, and useful background information, the authors’ step-by-step approach teaches you to develop a natural-language-based ORM model, and then, where needed, abstract ER and UML models from it. This book will quickly make you proficient in the modeling technique that is proving vital to the development of accurate and efficient databases that best meet real business objectives. "This book is an excellent introduction to both information modeling in ORM and relational databases. The book is very clearly written in a step-by-step manner and contains an abundance of well-chosen examples illuminating practice and theory in information modeling. I strongly recommend this book to anyone interested in conceptual modeling and databases." — Dr. Herman Balsters, Director of the Faculty of Industrial Engineering, University of Groningen, The Netherlands Presents the most in-depth coverage of object-role modeling, including a thorough update of the book for the latest versions of ORM, ER, UML, OWL, and BPMN modeling. Includes clear coverage of relational database concepts as well as the latest developments in SQL, XML, information modeling, data exchange, and schema transformation. Case studies and a large number of class-tested exercises are provided for many topics. Includes all-new chapters on data file formats and NoSQL databases.

Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. This second edition introduces the latest LLM Wolfram capabilities, delves into the exploration of data types in Mathematica, covers key programming concepts, and includes code performance and debugging techniques for code optimization. You’ll gain a deeper understanding of data science from a theoretical and practical perspective using Mathematica and the Wolfram Language. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. Existing topics have been reorganized for better context and to accommodate the introduction of Notebook styles. The book also incorporates new functionalities in code versions 13 and 14 for imported and exported data. You’ll see how to use Mathematica, where data management and mathematical computations are needed. Along the way, you’ll appreciate how Mathematica provides an entirely integrated platform: its symbolic and numerical calculation result in a mized syntax, allowing it to carry out various processes without superfluous lines of code. You’ll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out. What You Will Learn Create datasets, work with data frames, and create tables Import, export, analyze, and visualize data Work with the Wolfram data repository Build reports on the analysis Use Mathematica for machine learning, with different algorithms, including linear, multiple, and logistic regression; decision trees; and data clustering Who This Book Is For Data scientists who are new to using Wolfram and Mathematica as a programming language or tool. Programmers should have some prior programming experience, but can be new to the Wolfram language.

This IBM® Redpaper® Product Guide describes the IBM FlashCore Module (FCM) history, a general overview and then a deeper dive on the way IBM leads the field in the adoption of high speed, low latency storage. The IBM FlashCore Module is used in the latest IBM FlashSystem® solutions, which is are next-generation IBM FlashSystem control enclosures. The IBM FlashCore Module combines the performance of flash and a Non-Volatile Memory Express (NVMe) optimized architecture with the reliability and innovation of IBM FlashCore® technology and the rich feature set and high availability (HA) with IBM Storage Virtualize software.

This IBM Redpaper publication delivers SAP HANA architectural concepts for successful implementation on IBM Power Systems servers. This update is designed to introduce the Power10 product line and how it enhances support for SAP HANA. Also discussed is the addition of support for Red Hat Enterprise Linux as a supported operating system for SAP workloads. This publication addresses topics for sellers, IT architects, IT specialists, and anyone who wants to understand how to take advantage of running SAP HANA workloads on Power Systems servers. Moreover, this document provides information to transfer how-to skills to the technical teams, and it provides solution guidance to the sales team. This publication complements documentation that is available at IBM Knowledge Center, and it aligns with educational materials that are provided by IBM Systems.

Create stunning web-based data visualizations with D3.js. This totally-revised new edition of D3.js in Action guides you from simple charts to powerful interactive graphics. Chapter-by-chapter you’ll assemble an impressive portfolio of visualizations—including intricate networks, maps, and even a complete customized visualization layout. Plus, you'll learn best practices for building interactive graphics, animations, and integrating your work into frontend development frameworks like React and Svelte. In D3.js in Action, Third Edition you will learn how to: Set up a local development environment for D3 Include D3 in web development projects, including Node-based web apps Select and append DOM elements Size and position elements on screen Assemble components and layouts into creative data visualizations D3.js in Action, Third Edition has been extensively revised for D3.js version 7, and modern best practices for web visualizations. Its brand new chapters dive into interactive visualizations, cover responsiveness for dataviz, and show you how you can improve accessibility. About the Technology With D3.js, you can create sophisticated infographics, charts, and interactive data visualizations using standard frontend tools like JavaScript, HTML, and CSS. Granting D3 its VIS Test of Time award, the IEEE credited this powerful library for bringing data visualization to the mainstream. You’ll be blown away by how beautiful your results can be! About the Book D3.js in Action, Third Edition is a roadmap for creating brilliant and beautiful visualizations with D3.js. Like a gentle mentor, it guides you from basic charts all the way to advanced interactive visualizations like networks and maps. You’ll learn to build graphics, create animations, and set up mobile-friendly responsiveness. Each chapter contains a complete data visualization project to put your new skills into action. What's Inside Fully revised for D3.js v7 Includes 12 complete projects Create data visualizations with SVG and canvas Combine D3 with React, Svelte, and Angular About the Reader For web developers with HTML, CSS, and JavaScript skills. About the Authors Elijah Meeks was a data visualization pioneer at Stanford and the first Senior Data Visualization Engineer at Netflix. Anne-Marie Dufour is a Data Visualization Engineer. The technical editor on this book was Jon Borgman. Quotes Guides readers through the intricate world of D3 with clarity and practical insight. Whether you’re a seasoned expert or just starting, this book will be invaluable. - Connor Rothschild, Data Visualization Engineer, Moksha Data Studio Amazing job of explaining the core concepts of D3 while providing all you need to learn other fundamental concepts. - Lindsey Poulter, Visualization Engineer, New York Mets A navigation tool to explore all possible paths in the world of D3. Clear schematics and nicely selected examples guide the readers through D3’s possibilities. - Matthias Stahl, Head Data & Visualizations, Der SPIEGEL

This book constitutes the first systematic analysis of meta-discourse in the spoken domain, addressing the question of how, why, and when speakers switch from discourse to meta-discourse by means of comment clauses (e.g., ‘I think’). The case of Present-day Italian is considered, exploring the internal properties of comment clauses (e.g., morphosyntax and semantics of the verb), their relations with the surrounding discourse (e.g., position of comment clause), and their prosodic profiles. This study shows that speakers recur to meta-discourse to convey a non-random set of functions, having mainly to do with the online process of reference construction (e.g., approximation and reformulation) and with the degree of speaker’s commitment (e.g., epistemicity and emphasis). Comment clauses are also used as attention-getting or topic-resuming devices, though less frequently. One of the most interesting results of this study is the identification of a close relation between meta-discourse and stance-taking in spoken domain, with speakers recurring to comment clauses to convey their attitude. Finally, meta-discourse turns out to be highly influenced, if not constrained, by universal properties of the spoken domain (i.e., non-linearity).

Enhance your data transfer and storage skills with this comprehensive step-by-step guide to managing data migration for new on-premises SAP S/4HANA implementations. This book is tailored towards small to large projects, with a focus on the managerial aspects of the data migration process rather than the technical details. You’ll follow a project-led approach, enriched with a practical case study, and a comprehensive methodology for data migration planning and documentation. Athen traverse through a detailed plan on managing and documenting data migration throughout the project lifecycle. This book utilizes the general SAP Activate methodology for on-premises solutions as its foundational framework, enhancing it with specific strategies for data migration. Structured in alignment with the project phases of the SAP Activate methodology, Data Migration Management for SAP S/4HANA methodically covers planning, organizing, and controlling the data migration process. It serves as an essential guide for professionals tasked with implementing SAP S/4HANA in their business, ensuring a thorough understanding of each data migration phase on the project. What You'll Learn Significantly decrease the time needed for both the preparation and execution of data migration activities. Foster clear transparency in data migration processes for all stakeholders, including the customer and the project team. Facilitate a seamless and timely data migration process. Establish a benchmark for data migration management in future projects. Address and remedy any deficiencies in the SAP Activate methodology pertaining to data migration. Who This Book Is For SAP projects and data migration workstreams leads, already well-versed in SAP Activate methodology and possessing moderate experience in project and workstream management, who are seeking to enhance their skills in professionally managing data migration in implementation projects.

Data science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. This third edition delves into the latest advancements in AI, particularly focusing on large language models (LLMs), with clear distinctions made between AI and traditional data science, including AI's ability to emulate human decision-making. Author Stylianos Kampakis introduces you to the critical aspect of ethics in AI, an area of growing importance and scrutiny. The narrative examines the ethical considerations intrinsic to the development and deployment of AI technologies, including bias, fairness, transparency, and accountability. You’ll be provided with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issues surrounding data collection and algorithmic bias are some common pitfalls that Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated edition also includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists. Data science is a language that everyone at a modern company should understand across departments. Friction in communication arises most often when management does not connect with what a data scientist is doing or how impactful data collection and storage can be for their organization. The Decision Maker’s Handbook to Data Science bridges this gap and readies you for both the present and future of your workplace in this engaging, comprehensive guide. What You Will Learn Integrate AI with other innovative technologies Explore anticipated ethical, regulatory, and technical landscapes that will shape the future of AI and data science Discover how to hire and manage data scientists Build the right environment in order to make your organization data-driven Who This Book Is For Startup founders, product managers, higher level managers, and any other non-technical decision makers who are thinking to implement data science in their organization and hire data scientists. A secondary audience includes people looking for a soft introduction into the subject of data science.

Unlock the potential of the Elastic Stack with the "Elastic Stack 8.x Cookbook." This book provides over 80 hands-on recipes, guiding you through ingesting, processing, and visualizing data using Elasticsearch, Logstash, Kibana, and more. You'll also explore advanced features like machine learning and observability to create data-driven applications with ease. What this Book will help me do Implement a robust workflow for ingesting, transforming, and visualizing diverse datasets. Utilize Kibana to create insightful dashboards and visual analytics. Leverage Elastic Stack's AI capabilities, such as natural language processing and machine learning. Develop search solutions and integrate advanced features like vector search. Monitor and optimize your Elastic Stack deployments for performance and security. Author(s) Huage Chen and Yazid Akadiri are experienced professionals in the field of Elastic Stack. They bring years of practical experience in data engineering, observability, and software development. Huage and Yazid aim to provide a clear, practical pathway for both beginners and experienced users to get the most out of the Elastic Stack's capabilities. Who is it for? This book is perfect for developers, data engineers, and observability practitioners looking to harness the power of Elastic Stack. It caters to both beginners and experts, providing clear instructions to help readers understand and implement powerful data solutions. If you're working with search applications, data analysis, or system observability, this book is an ideal resource.