Databaser

Data is at the center of many challenges in system design today. Difficult issues such as scalability, consistency, reliability, efficiency, and maintainability need to be resolved. In addition, there's an overwhelming variety of tools and analytical systems, including relational databases, NoSQL datastores, plus data warehouses and data lakes. What are the right choices for your application? How do you make sense of all these buzzwords?In this second edition, authors Martin Kleppmann and Chris Riccomini build on the foundation laid in the acclaimed first edition, integrating new technologies and emerging trends. You'll be guided through the maze of decisions and trade-offs involved in building a modern data system, from choosing the right tools like Spark and Flink to understanding the intricacies of data laws like the GDPR.Peer under the hood of the systems you already use, and learn to use them more effectivelyMake informed decisions by identifying the strengths and weaknesses of different toolsNavigate the trade-offs around consistency, scalability, fault tolerance, and complexityUnderstand the distributed systems research upon which modern databases are builtPeek behind the scenes of major online services, and learn from their architectures

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you’ll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher-quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

Winner of the 2019 International Cartographic Conference – Educational Products awardA lavishly illustrated reference guide, Cartography. by Kenneth Field is an inspiring and creative companion along the nonlinear journey toward making a great map. This sage compendium for contemporary mapmakers distills the essence of cartography into useful topics, organized for convenience in finding the specific idea or method you need. Unlike books targeted to deep scholarly discourse of cartographic theory, this book provides sound, visually compelling information that translates into practical and useful tools for modern mapmaking. At the intersection of science and art, this book serves as a guidepost for designing an accurate and effective map.

This text provides a mathematically rigorous introduction to modern methods of machine learning and data analysis at the advanced undergraduate/beginning graduate level. The book is self-contained and requires minimal mathematical prerequisites. There is a strong focus on learning how and why algorithms work, as well as developing facility with their practical applications. Apart from basic calculus, the underlying mathematics — linear algebra, optimization, elementary probability, graph theory, and statistics — is developed from scratch in a form best suited to the overall goals.  In particular, the wide-ranging linear algebra components are unique in their ordering and choice of topics, emphasizing those parts of the theory and techniques that are used in contemporary machine learning and data analysis.  The book will provide a firm foundation to the reader whose goal is to work on applications of machine learning and/or research into the further development of this highly active field of contemporary applied mathematics.To introduce the reader to a broad range of machine learning algorithms and how they are used in real world applications, the programming language Python is employed and offers a platform for many of the computational exercises. Python notebooks complementing various topics in the book are available on a companion GitHub site specified in the Preface, and can be easily accessed by scanning the QR codes or clicking on the links provided within the text. Exercises appear at the end of each section, including basic ones designed to test comprehension and computational skills, while others range over proofs not supplied in the text, practical computations, additional theoretical results, and further developments in the subject.  The Students’ Solutions Manual may be accessed from GitHub. Instructors may apply for access to the Instructors’ Solutions Manual from the link supplied on the text’s Springer website.The book can be used in a junior or senior level course for students majoring in mathematics with a focus on applications as well as students from other disciplines who desire to learn the tools of modern applied linear algebra and optimization. It may also be used as an introduction to fundamental techniques in data science and machine learning for advanced undergraduate and graduate students or researchers from other areas, including statistics, computer science, engineering, biology, economics and finance, and so on.

Updated new edition of Ralph Kimball's groundbreaking book on dimensional modeling for data warehousing and business intelligence!The first edition of Ralph Kimball's The Data Warehouse Toolkit introduced the industry to dimensional modeling, and now his books are considered the most authoritative guides in this space. This new third edition is a complete library of updated dimensional modeling techniques, the most comprehensive collection ever. It covers new and enhanced star schema dimensional modeling patterns, adds two new chapters on ETL techniques, includes new and expanded business matrices for 12 case studies, and more. Authored by Ralph Kimball and Margy Ross, known worldwide as educators, consultants, and influential thought leaders in data warehousing and business intelligenceBegins with fundamental design recommendations and progresses through increasingly complex scenariosPresents unique modeling techniques for business applications such as inventory management, procurement, invoicing, accounting, customer relationship management, big data analytics, and moreDraws real-world case studies from a variety of industries, including retail sales, financial services, telecommunications, education, health care, insurance, e-commerce, and moreDesign dimensional databases that are easy to understand and provide fast query response with The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition.

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architecture

Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process.Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.Use the Jupyter notebook and IPython shell for exploratory computingLearn basic and advanced features in NumPyGet started with data analysis tools in the pandas libraryUse flexible tools to load, clean, transform, merge, and reshape dataCreate informative visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsAnalyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, detailed examples

Databasteknik används nästan överallt i dag där data behöver lagras och bearbetas. Allt från webbplatser till mobilappar lagrar sina data i databaser. Databaser har därför blivit ett av de områden inom datatekniken som man har störst nytta av att kunna, vare sig man är användare, datatekniker eller programmerare.Boken Databasteknik börjar med grunderna om databaser, som scheman, datamodellering och användningsområden för databaser. Vi går vidare bland annat med frågespråk, transaktionshantering, säkerhet, prestanda och hur databashanteraren arbetar internt.Den vanligaste och populäraste typen av databaser är relations­databaser, och tyngdpunkten ligger därför på dem, bland annat med frågespråket SQL, och vi beskriver system som MySQL, PostgreSQL och Microsoft SQL Server. Men vi vänder oss till alla som arbetar med databaser, även till exempel objektorienterade databaser och NoSQL-databaser.

International bestseller with over half a million copies sold!Celebrate a decade of data storytelling with this special 10th anniversary edition of Storytelling with Data. Featuring refreshed visuals, expanded content, and new insights, this collector's edition builds on the classic's foundational lessons, empowering you to craft persuasive data stories.Don't simply show your data—tell a story with it!Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation.Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story.Specifically, you'll learn how to: Understand the importance of context and audienceDetermine the appropriate type of graph for your situationRecognize and eliminate the clutter clouding your informationDirect your audience's attention to the most important parts of your dataThink like a designer and utilize concepts of design in data visualizationLeverage the power of storytelling to help your message resonate with your audienceTogether, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it.

There is growing recognition of the need to address the fragility of digital information, on which our society heavily depends for smooth operation in all aspects of daily life. This has been discussed in many books and articles on digital preservation, so why is there a need for yet one more? Because, for the most part, those other publications focus on documents, images and webpages – objects that are normally rendered to be simply displayed by software to a human viewer. Yet there are clearly many more types of digital objects that may need to be preserved, such as databases, scientific data and software itself.David Giaretta, Director of the Alliance for Permanent Access,  and his contributors explain why the tools and techniques used for preserving rendered objects are inadequate for all these other types of digital objects, and they provide the concepts, techniques and tools that are needed. The book is structured in three parts. The first part is on theory, i.e., the concepts and techniques that are essential for preserving digitally encoded information. The second part then shows practice, i.e., the use and validation of these tools and techniques. Finally, the third part concludes by addressing how to judge whether money is being well spent, in terms of effectiveness and cost sharing.Various examples of digital objects from many sources are used to explain the tools and techniques presented. The presentation style mainly aims at practitioners in libraries, archives and industry who are either directly responsible for preservation or who need to prepare for audits of their archives. Researchers in digital preservation and developers of preservation tools and techniques will also find valuable practical information here. Researchers creating digitally encoded information of all kinds will also need to be aware of these topics so that they can help to ensure that their data is usable and can be valued by others now and in thefuture.To further assist the reader, the book is supported by many hours of videos and presentations from the CASPAR project and by a set of open source software.

Operations from logic and set theory can be applied to existing sets of truths to derive new sets of truths. Applied Mathematics for Database Professionals introduces you to this way of thinking, to the logic and set theory that underlies relational database technology.

This highly regarded textbook provides the definitive account of Customer Relationship Management (CRM) concepts, applications, and technologies, focusing on how companies can create and maintain mutually beneficial relationships with customers.Readers will gain a thorough understanding of the conceptual foundations of CRM, see CRM in practice through illustrative case examples and exercises, and understand how to organise customer data gathering, analysis, and presentation for decision making. The book achieves these outcomes by first considering strategic CRM before moving into operational CRM and, finally, onto analytical aspects of CRM. The fifth edition has been fully updated to include:A series of new case examples to illustrate CRM within various regional and industrial contexts, including those relevant to large, medium, and small enterprisesA series of new exercises and discussion questions to help readers understand CRM concepts and to support pedagogical processes, particularly in higher education environmentsA greater emphasis on managerial applications of CRM through new content to help guide managersAn updated account of new and emerging technologies relevant to CRMExpanded coverage of customer experience (CX), customer engagement (CE), and customer journey management (CJM)Customer Relationship Management is essential reading for advanced undergraduate and postgraduate students studying CRM, Sales Management, Customer Experience Management, and Relationship Marketing, as well as executives who oversee CRM functions. Online resources include an Instructor’s Manual, chapter-by-chapter PowerPoint slides, and a bank of exam questions.

The latest and most authoritative version of the popular step-by-step tutorial for Revit Architecture The newly revised third edition of Revit 2024 for Architecture: No Experience Required is the latest update to the market-leading, real-world guide for learning and building with Revit—the powerful and sophisticated Building Information Modeling (BIM) software used by professionals around the world. This popular, user-friendly book teaches you the Revit interface and helps you understand the foundational concepts and features of the software. You’ll learn to design, document, and present a 3D BIM project with a continuous, step-by-step tutorial that guides you through every phase of the project: from placing walls, doors, windows, structural elements, dimensions, and text, to generating documentation, advanced detailing, site grading, construction scheduling, material takeoffs, and more. In addition, this book helps you prepare for the Autodesk Revit Architecture Certification Exam. Throughout the book, you will find helpful insights directly related to the exam. The last two chapters are dedicated entirely to the exam with a practice test at the end of the book. You’ll also: Learn each phase of designing, documenting, and presenting a four-story office building using a simple yet engaging continuous tutorialFollow the tutorial sequentially or jump to any chapter by downloading the project files from the Sybex websiteUse the start-to-finish tutorial project as a reference for your own real-world projects and to develop a powerful Revit skillsetGain thorough knowledge of Revit’s essential concepts and features to make the move from 2D drafting to 3D building information modelingGet up to speed with advanced features, including new coverage of advanced walls, families, sites, topography, and moreThe Autodesk Revit 2024 for Architecture: No Experience Required, 3rd Edition, is the go-to guide for professionals and students seeking to learn Revit’s essential functions quickly and effectively.

Seamlessly integrate the Python and R programming languages with spreadsheet-based data analysis to maximize productivityKey FeaturesPerform advanced data analysis and visualization techniques with R and Python on Excel dataUse exploratory data analysis and pivot table analysis for deeper insights into your dataIntegrate R and Python code directly into Excel using VBA or API endpointsPurchase of the print or Kindle book includes a free PDF eBookBook Description– Extending Excel with Python and R is a game changer resource written by experts Steven Sanderson, the author of the healthyverse suite of R packages, and David Kun, co-founder of Functional Analytics. – This comprehensive guide transforms the way you work with spreadsheet-based data by integrating Python and R with Excel to automate tasks, execute statistical analysis, and create powerful visualizations. – Working through the chapters, you’ll find out how to perform exploratory data analysis, time series analysis, and even integrate APIs for maximum efficiency. – Both beginners and experts will get everything you need to unlock Excel's full potential and take your data analysis skills to the next level. – By the end of this book, you’ll be able to import data from Excel, manipulate it in R or Python, and perform the data analysis tasks in your preferred framework while pushing the results back to Excel for sharing with others as needed. What you will learnRead and write Excel files with R and Python librariesAutomate Excel tasks with R and Python scriptsUse R and Python to execute Excel VBA macrosFormat Excel sheets using R and Python packagesCreate graphs with ggplot2 and Matplotlib in ExcelAnalyze Excel data with statistical methods and time series analysisExplore various methods to call R and Python functions from ExcelWho this book is for– If you’re a data analyst or data scientist, or a quants, actuaries, or data practitioner looking to enhance your Excel skills and expand your data analysis capabilities with R and Python, this book is for you. – The comprehensive approach to the topics covered makes it suitable for both beginners and intermediate learners. – A basic understanding of Excel, Python, and R is all you need to get started.

Praise for the first edition:“In nine succinct but information-packed chapters, the authors provide a logically structured and robust introduction to the mathematical and statistical methods underpinning the still-evolving field of AI and data science.”- Joacim Rocklöv and Albert A. Gayle, International Journal of Epidemiology, Volume 49, Issue 6“This book organizes the algorithms clearly and cleverly. The way the Python code was written follows the algorithm closely—very useful for readers who wish to understand the rationale and flow of the background knowledge.”- Yin-Ju Lai and Chuhsing Kate Hsiao, Biometrics, Volume 77, Issue 4The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.New in the Second EditionThis expanded edition provides updates across key areas of statistical learning:Monte Carlo Methods: A new section introducing regenerative rejection sampling - a simpler alternative to MCMC.Unsupervised Learning: Inclusion of two multidimensional diffusion kernel density estimators, as well as the bandwidth perturbation matching method for the optimal data-driven bandwidth selection.Regression: New automatic bandwidth selection for local linear regression.Feature Selection and Shrinkage: A new chapter introducing the klimax method for model selection in high-dimensions.Reinforcement Learning: A new chapter on contemporary topics such as policy iteration, temporal difference learning, and policy gradient methods, all complete with Python code.Appendices: Expanded treatment of linear algebra, functional analysis, and optimization that includes the coordinate-descent method and the novel Majorization–Minimization method for constrained optimization.Key Features:Focuses on mathematical understanding.Presentation is self-contained, accessible, and comprehensive.Extensive list of exercises and worked-out examples.Many concrete algorithms with Python code.Full color throughout and extensive indexing.A single-counter consecutive numbering of all theorems, definitions, equations, etc., for easier text searches.

Denna bok ger en introduktion till relationsdatabaser och en pedagogiskt upplagd genomgång av SQL-språket. Syftet är att förmedla kunskaper som behövs för att någorlunda kunna förstå en databas uppbyggnad och för att kunna ställa frågor mot databasen. Boken kan användas som kurslitteratur på databaskurser eller som en självstudiebok. Förftattarna är lärare vid högskolan i Luleå och har mångårig erfarenhet av undervisning i relationsdatabaser och SQL-språket.

Learn to research, plan, design, and test the UX of AI-powered products Unlock the future of design with UX for AI—your indispensable guide to not only surviving but thriving in a world powered by artificial intelligence. Whether you're a seasoned UX designer or a budding design student, this book offers a lifeline for navigating the new normal, ensuring you stay relevant, valuable, and indispensable to your organization. In UX for AI: A Framework for Designing AI-Driven Products, Greg Nudelman—a seasoned UX designer and AI strategist—delivers a battle-tested framework that helps you keep your edge, thrive in your design job, and seize the opportunities AI brings to the table. Drawing on insights from 35 real-world AI projects and acknowledging the hard truth that 85% of AI initiatives fail, this book equips you with the practical skills you need to reverse those odds. You'll gain powerful tools to research, plan, design, and test user experiences that seamlessly integrate human-AI interactions. From practical design techniques to proven user research methods, this is the essential guide for anyone determined to create AI products that not only succeed but set new standards of value and impact. Inside the book: Hands-on exercises: Build your confidence and skills with practice UX design tasks like Digital Twin and Value Matrix, which you can immediately apply to your own AI projects.Common AI patterns and best practices: Explore design strategies for LLMs (Large Language Models), search engines, copilots, and more.Proven user research strategies: Learn how to uncover user needs and behaviors in this brave new world of AI-powered design.Real-world case studies: See how simple, practical UX approaches have prevented multimillion-dollar failures and unlocked unprecedented value.Perfect for any UX designer working with AI-enabled and AI-driven products, UX for AI is also a must-read resource for designers-in-training and design students with an interest in artificial intelligence and contemporary design.

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You'll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company's data science projects. You'll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization - and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you're to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates

Seasoned Experts Alberto Ferrari and Marco Russo Help You Master DAX for Superior Business Intelligence Solutions

The Definitive Guide to DAX is an authoritative resource for mastering the DAX language, which is pivotal for creating measures for semantic models in Microsoft Power BI, Fabric, Analysis Services, and Excel. With years of experience since the inception of Power Pivot, the authors offer a comprehensive reference that navigates through the unique and complex concepts of DAX, making it an essential tool for BI professionals. This third edition updates you with the latest features and provides a framework for writing efficient DAX code, enhancing your ability to customize calculations in data models. You will gain the skills necessary to understand and apply advanced DAX concepts, structure code in user-defined functions, and leverage new calendars for time intelligence calculations.

By reading this book, you will:

Understand the foundational concepts of DAX and its unique language structure Master the use of evaluation contexts to enhance data analysis Utilize CALCULATE and CALCULATETABLE functions effectively Implement variables to simplify complex DAX expressions Leverage classic and new calendar based time intelligence functions Work with iterators to perform advanced calculations Apply visual calculations to improve report interactivity Create and manage calculation groups for dynamic reporting Handle complex hierarchies and relationships in data models Author DAX queries for testing and troubleshooting

About This Book

For BI professionals and Excel power users eager to deepen their understanding of DAX and enhance their data modeling capabilities with advanced techniques For data analysts and IT specialists seeking to optimize their use of Microsoft Power BI, Fabric, Analysis Services, and Excel for more efficient and insightful data analysis

This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.

Enterprises have made amazing advances by taking advantage of data about their business to provide predictions and understanding of their customers, markets, and products. But as the world of business becomes more interconnected and global, enterprise data is no long a monolith; it is just a part of a vast web of data. Managing data on a world-wide scale is a key capability for any business today.The Semantic Web treats data as a distributed resource on the scale of the World Wide Web, and incorporates features to address the challenges of massive data distribution as part of its basic design. The aim of the first two editions was to motivate the Semantic Web technology stack from end-to-end; to describe not only what the Semantic Web standards are and how they work, but also what their goals are and why they were designed as they are. It tells a coherent story from beginning to end of how the standards work to manage a world-wide distributed web of knowledge in a meaningful way.The third edition builds on this foundation to bring Semantic Web practice to enterprise. Fabien Gandon joins Dean Allemang and Jim Hendler, bringing with him years of experience in global linked data, to open up the story to a modern view of global linked data. While the overall story is the same, the examples have been brought up to date and applied in a modern setting, where enterprise and global data come together as a living, linked network of data. Also included with the third edition, all of the data sets and queries are available online for study and experimentation at data.world/swwo.

Master Microsoft Fabric from basics to advanced architectures with expert guidance to unify, secure, and scale analytics on real-world data platformsKey FeaturesBuild a complete data analytics platform with Microsoft FabricApply proven architectures, governance, and security strategiesGain real-world insights from five seasoned data expertsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMicrosoft Fabric is reshaping how organizations manage, analyze, and act on data by unifying ingestion, storage, transformation, analytics, AI, and visualization in a single platform.The Definitive Guide to Microsoft Fabric takes you from your very first workspace to building a secure, scalable, and future-proof analytics environment. You’ll learn how to unify data in OneLake, design data meshes, transform and model data, implement real-time analytics, and integrate AI capabilities. The book also covers advanced topics, such as governance, security, cost optimization, and team collaboration using DevOps and DataOps principles. Drawing on the real-world expertise of five seasoned professionals who have built and advised on platforms for startups, SMEs, and Europe’s largest enterprises, this book blends strategic insight with practical guidance.By the end of this book, you’ll have gained the knowledge and skills to design, deploy, and operate a Microsoft Fabric platform that delivers sustainable business value.What you will learnUnderstand Microsoft Fabric architecture and conceptsUnify data storage and data governance with OneLakeIngest and transform data using multiple Fabric toolsImplement real-time analytics and event processingDesign effective semantic models and reportsIntegrate AI and machine learning into data workflowsApply governance, security, and compliance controlsOptimize performance and costs at scaleWho this book is forThis book is for data engineers, analytics engineers, architects, and data analysts moving into platform design roles. It’s also valuable for technical leaders seeking to unify analytics in their organizations. You’ll need only a basic grasp of databases, SQL, and Python.

Managing data continues to grow as a necessity for modern organizations. There are seemingly infinite opportunities for organic growth, reduction of costs, and creation of new products and services. It has become apparent that none of these opportunities can happen smoothly without data governance. The cost of exponential data growth and privacy / security concerns are becoming burdensome. Organizations will encounter unexpected consequences in new sources of risk. The solution to these challenges is also data governance; ensuring balance between risk and opportunity.

Data Governance, Second Edition, is for any executive, manager or data professional who needs to understand or implement a data governance program. It is required to ensure consistent, accurate and reliable data across their organization. This book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. This valuable resource provides comprehensive guidance to beginning professionals, managers or analysts looking to improve their processes, and advanced students in Data Management and related courses. With the provided framework and case studies all professionals in the data governance field will gain key insights into launching successful and money-saving data governance program.

Incorporates industry changes, lessons learned and new approaches Explores various ways in which data analysts and managers can ensure consistent, accurate and reliable data across their organizations Includes new case studies which detail real-world situations Explores all of the capabilities an organization must adopt to become data driven Provides guidance on various approaches to data governance, to determine whether an organization should be low profile, central controlled, agile, or traditional Provides guidance on using technology and separating vendor hype from sincere delivery of necessary capabilities Offers readers insights into how their organizations can improve the value of their data, through data quality, data strategy and data literacy Provides up to 75% brand-new content compared to the first edition

Practical Bioinformatics is specifically designed as an introduction to bioinformatics sequence analysis. Many readers are exploring their future paths or are fascinated by the headlines about interesting gene discoveries and want to understand more. There is heavy emphasis on the steps required to perform bioinformatics analysis to answer biological questions. The book contains many exercises such as database searches, protein analysis, and data interpretation to complement the straightforward and practical topics. The chapters are focused on DNA, RNA, and protein sequence analysis—frequently performed subsets of the field of bioinformatics—taking the reader through the commonly asked ques>tion: “what can I learn about this sequence?”Established scientists at universities, biotech firms, and pharmaceutical companies are finding their work is becoming more dependent on bioinformatics and are missing these skills. More and more, genomic data has become part of their daily lives, and the pace is accelerating. Those not trained in bioinformatics will find that this book will serve as a solid foundation for terms and concepts of genomics and bioinformatics. With this new knowledge, you will have a better appreciation of new developments in your field and can turn a more critical eye toward the information crossing your desk.Key Features• Using compelling biological examples, sequence analysis problems are shown and solved.• Each chapter introduces essential topics and skills to be learned.• The book includes over 300 figures, most of which have been updated since the last edition.• Screenshots from actual websites (for example, the NCBI) are used to illustrate the examples and exercises.• Exercises help solidify the understanding of concepts and help the student learn how to solve biological problems. Many exercises can be adapted to accommodate a reader’s gene of interest.Michael Agostino received his PhD in molecular biology from Roswell Park, a division of SUNY at Buffalo, New York. He did genetic engineering laboratory research for 13 years before making the formal switch to bioinformatics, working at GlaxoSmithKline, Genetics Institute, and Pfizer for over 33 years. At those companies he worked with other scientists, providing sequence analysis support for projects too numerous to count. Michael is now an instructor for Harvard Extension School, where, for 10 years, he has been teaching a course largely based on this book.

With the shift from data warehouses to data lakes, data now lands in repositories before it's been transformed, enabling engineers to model raw data into clean, well-defined datasets. DBT (data build tool) helps you take data further. This practical book shows data analysts, data engineers, BI developers, and data scientists how to create a true self-service transformation platform through the use of dynamic SQL.Authors Rui Machado from Monstarlab and Helder Russa from Jumia show you how to quickly deliver new data products by focusing more on value delivery and less on architectural and engineering aspects. If you know your business well and have the technical skills to model raw data into clean, well-defined datasets, you'll learn how to design and deliver data models without any technical influence.With this book, you'll learn:What DBT is and how a DBT project is structuredHow DBT fits into the data engineering and analytics worldsHow to collaborate on building data modelsThe main tools and architectures for building useful, functional data modelsHow to fit DBT into data warehousing and laking architectureHow to build tests for data transformations

Learn how to use R to turn data into insight, knowledge, and understanding. Ideal for current and aspiring data scientists, this book introduces you to doing data science with R and RStudio, as well as the tidyverse-a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly.You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Each section in this edition includes exercises to help you practice what you've learned along the way.Updated for the latest tidyverse best practices, new chapters dive deeper into visualization and data wrangling, show you how to get data from spreadsheets, databases, and websites, and help you make the most of new programming tools.You'll learn how to:Visualize-create plots for data exploration and communication of resultsTransform-discover types of variables and the tools you can use to work with themImport-get data into R and in a form convenient for analysisProgram-learn R tools for solving data problems with greater clarity and easeCommunicate-integrate prose, code, and results with Quarto

Dive into SQL Server 2025 and uncover its groundbreaking features—guided by one of its key architects. The 2025 release brings SQL Server firmly into the AI age, and this book ensures that you're ready for the leap, walking you through built-in AI for application development, AI-enhanced data analytics with Microsoft Fabric integration—and much more.At every step of the way, you’ll gain exclusive insights from author Bob Ward, a Principal Architect for the Microsoft Azure Data team, who has worked on every version of SQL Server since OS/2 1.1. With more than 31 years at Microsoft, Ward brings unparalleled expertise and practical knowledge, leaving you and your database in excellent hands.Ward covers the fundamentals of setup, upgrades, and configuration, and covers advanced features in security, performance, and high availability. He also highlights what makes this the most significant SQL Server release for developers in a decade: support for JSON, RegEx, REST APIs, and event streaming.Most critically, Ward explores SQl Server 2025’s advanced, secure, and scalable AI integration. After a practical introduction to AI fundamentals to help you integrate artificial intelligence with your data, you’ll learn how to build AI-powered applications deeply integrated with the SQL engine—and elevate your analytics to the next level.The AI age is here. Be sure your SQL Server databases are ready. What You Will Learn·         Understand why SQL Server 2025 is a unique new release·         Grasp the fundamentals of AI to leverage AI with your data, using the industry-proven security and scale of SQL Server·         Utilize AI models of your choice, services, and frameworks to build new AI applications·         Explore new developer features such as JSON, Regular Expressions, REST API, and Change Event Streaming·         Discover SQL Server 2025's powerful new engine capabilities to increase application concurrency·         Examine new high availability features to enhance uptime and diagnose complex HADR configurations·         Use new query processing capabilities to extend the performance of your application·         Connect SQL Server to Azure with Arc for advanced management and security capabilities·         Secure and govern your data using Microsoft Entra·         Achieve near-real-time analytics with the unified data platform Microsoft Fabric·         Integrate AI capabilities with SQL Server for enterprise AI.·         Leverage new tools such as SQL Server Management 21 and Copilot experiences to assist your SQL Server journey Who This Book Is ForThe SQL Server community, including DBAs, architects, and developers eager to stay ahead with the latest advancements in SQL Server 2025, and those interested in the intersection of AI and data, particularly how artificial intelligence (AI) can be seamlessly integrated with SQL Server to unlock deeper insights and smarter solutions