Databasedesign og databaseteori

Learn the fundamentals of statistics and machine learning using R libraries for data processing, visualization, model training, and statistical inferenceKey Features:Advance your ML career with the help of detailed explanations, intuitive illustrations, and code examplesGain practical insights into the real-world applications of statistics and machine learningExplore the technicalities of statistics and machine learning for effective data presentationPurchase of the print or Kindle book includes a free PDF eBookBook Description:The Statistics and Machine Learning with R Workshop is a comprehensive resource packed with insights into statistics and machine learning, along with a deep dive into R libraries. The learning experience is further enhanced by practical examples and hands-on exercises that provide explanations of key concepts.Starting with the fundamentals, you'll explore the complete model development process, covering everything from data pre-processing to model development. In addition to machine learning, you'll also delve into R's statistical capabilities, learning to manipulate various data types and tackle complex mathematical challenges from algebra and calculus to probability and Bayesian statistics. You'll discover linear regression techniques and more advanced statistical methodologies to hone your skills and advance your career.By the end of this book, you'll have a robust foundational understanding of statistics and machine learning. You'll also be proficient in using R's extensive libraries for tasks such as data processing and model training and be well-equipped to leverage the full potential of R in your future projects.What You Will Learn:Hone your skills in different probability distributions and hypothesis testingExplore the fundamentals of linear algebra and calculusMaster crucial statistics and machine learning concepts in theory and practiceDiscover essential data processing and visualization techniquesEngage in interactive data analysis using RUse R to perform statistical modeling, including Bayesian and linear regressionWho this book is for:This book is for beginner to intermediate-level data scientists, undergraduate to masters-level students, and early to mid-senior data scientists or analysts looking to expand their knowledge of machine learning by exploring various R libraries. Basic knowledge of linear algebra and data modeling is a must.

What does it take to report from conflict zones? What good is neutrality in the face of suffering, and how much difference can one person make?From her first journalistic assignment in Gaza to covering the Arab Spring in Egypt, Sherine Tadros searched for ways to change people's lives for the better.It wasn't until her life fell apart that she found the courage to pursue her true purpose. With compassion and verve, Tadros now shares her remarkable journey, from witnessing injustice to fighting it in the corridors of power. In probing the line between journalism and activism, her memoir Taking Sides demonstrates why stories matter - and how we can all use our voices to inspire meaningful change.

A powerful and urgent explanation and vindication of our human rights and freedomsAfter the devastation of World War Two, the international community came together to enshrine fundamental rights to refuge, health, education and living standards, for privacy, fair trials and free speech, and outlawing torture, slavery and discrimination. Their goal was greater global justice, equality, and peace. That settlement is now in danger, attacked by opponents from across the political spectrum and populist and authoritarian movements worldwide. We are threatened by wars, inequality, new technologies and climate catastrophe, and we need our human rights now more than ever. In this powerful, accessible book, Shami Chakrabarti, lawyer, parliamentarian and leading British human rights defender, shows us why human rights are essential for our future. Outlining the historic national and international struggles for human rights, from the fall of Babylon, to the present day, Chakrabarti is an indispensable guide to the law and logic underpinning human dignity and universal freedoms. Her intervention will engage both sceptics and supporters, equipping believers in the battle of ideas and persuading doubters to think again. For human rights to survive, they must be far better understood by everyone.

Decision Making Models: A Perspective of Fuzzy Logic and Machine Learning presents the latest developments in the field of uncertain mathematics and decision science. The book aims to deliver a systematic exposure to soft computing techniques in fuzzy mathematics as well as artificial intelligence in the context of real-life problems and is designed to address recent techniques to solving uncertain problems encountered specifically in decision sciences. Researchers, professors, software engineers, and graduate students working in the fields of applied mathematics, software engineering, and artificial intelligence will find this book useful to acquire a solid foundation in fuzzy logic and fuzzy systems.Other areas of note include optimization problems and artificial intelligence practices, as well as how to analyze IoT solutions with applications and develop decision-making mechanisms realized under uncertainty.

The Align > Refine > Design series covers conceptual, logical, and physical data modeling (schema design and patterns) for leading technologies, combining proven data modeling practices with database-specific features to produce better applications. Read TerminusDB Data Modeling and Schema Design if you are a data professional who needs to expand your modeling skills to include TerminusDB or a technologist who knows TerminusDB but needs to grow your schema design skills.The book's introduction and three chapters cover the Align, Refine, and Design approach. We include what the level does in the name by rebranding Conceptual, Logical, and Physical into Align, Refine, and Design. The introduction covers the three modeling characteristics of precise, minimal, and visual; the three model components of entities, relationships, and attributes (including keys); the three model levels of conceptual (align), logical (refine), and physical (design); and the three modeling perspectives of relational, dimensional, and query. Chapter 1, Align, is about agreeing on the common business vocabulary so everyone is aligned on terminology and general initiative scope. Chapter 2, Refine, is about capturing the business requirements. That is, refining our knowledge of the initiative to focus on what is essential. Chapter 3, Design, is about the technical requirements. That is, designing to accommodate our model's unique software and hardware needs.Align, Refine, and Design-that's the approach followed in this book and reinforced through an animal shelter case study. If you are interested in learning how to build multiple database solutions, read all the books in the Align > Refine > Design series. Since each book uses the same template, you can quickly skill up on additional database technologies.

As UAV technology is rapidly evolving, a pervasive need for a thorough investigation of its full capabilities has come to the forefront. Internet of Drone Things: Architectures, Approaches, and Applications fulfils this need enabling its readers to easily find the answers they are seeking by providing a comprehensive overview of the topic.This book, in fact, includes fundamental information related to IoDT architecture design and features; reviews the state of the art in hardware and software platforms to deploy, connect, and control drones or swarms of drones; and covers the latest developments in innovative drone-facilitated applications and services that can significantly improve efficiency, productivity, and sustainability of various operations in modern society and a growing number of its industries. Finally, experimental modeling and simulations are accompanied by prototyping examples, which are set to become the benchmark of next-level automation in the field.Internet of Drone Things: Architectures, Approaches, and Applications is, therefore, an invaluable resource for engineering students, researchers, and professionals, as well as sector experts who work to develop new drone standards or to identify new drone technology use and commercialization areas at an international level.

Keys and foreign keys play a crucial role in relational databases-keys identify the objects of interest, and foreign keys knit those objects together. The basic idea couldn't be simpler. As so often, however, the devil is in the detail ... The fact is, these concepts aren't quite as straightforward as they might seem on first acquaintance-or, at least, such would appear to be the case, if the literature is anything to go by. In this one of a kind book, noted database author C. J. Date traces the somewhat checkered history of the key and foreign key concepts, shedding some light on what turns out to be, on occasion, a surprisingly murky subject and explaining in detail what proper support should look like in true relational products. Topics covered include a detailed look at the pertinent theory; a critical review of the historical development of these ideas; and a couple of important case studies, one having to do with the SQL standard and one with the IBM DB2 product family. No serious database professional can afford to be without this book.

Traditional data architecture patterns are severely limited. To use these patterns, you have to ETL data into each tool--a cost-prohibitive process for making warehouse features available to all of your data. The lack of flexibility with these patterns requires you to lock into a set of priority tools and formats, which creates data silos and data drift. This practical book shows you a better way. Apache Iceberg provides the capabilities, performance, scalability, and savings that fulfill the promise of an open data lakehouse. By following the lessons in this book, you'll be able to achieve interactive, batch, machine learning, and streaming analytics with this high-performance open source format. Authors Tomer Shiran, Jason Hughes, and Alex Merced from Dremio show you how to get started with Iceberg. With this book, you'll learn: The architecture of Apache Iceberg tables What happens under the hood when you perform operations on Iceberg tables How to further optimize Iceberg tables for maximum performance How to use Iceberg with popular data engines such as Apache Spark, Apache Flink, and Dremio Discover why Apache Iceberg is a foundational technology for implementing an open data lakehouse.

The Align > Refine > Design series covers conceptual, logical, and physical data modeling (schema design and patterns) for leading technologies, combining proven data modeling practices with database-specific features to produce better applications. Read Elasticsearch Data Modeling and Schema Design if you are a data professional who needs to expand your modeling skills to include Elasticsearch or a technologist who knows Elasticsearch but needs to grow your schema design skills.The book's introduction and three chapters cover the Align, Refine, and Design approach. We include what the level does in the name by rebranding Conceptual, Logical, and Physical into Align, Refine, and Design. The introduction covers the three modeling characteristics of precise, minimal, and visual; the three model components of entities, relationships, and attributes (including keys); the three model levels of conceptual (align), logical (refine), and physical (design); and the three modeling perspectives of relational, dimensional, and query. Chapter 1, Align, is about agreeing on the common business vocabulary so everyone is aligned on terminology and general initiative scope. Chapter 2, Refine, is about capturing the business requirements. That is, refining our knowledge of the initiative to focus on what is essential. Chapter 3, Design, is about the technical requirements. That is, designing to accommodate our model's unique software and hardware needs.Align, Refine, and Design-that's the approach followed in this book and reinforced through an animal shelter case study. If you are interested in learning how to build multiple database solutions, read all the books in the Align > Refine > Design series. Since each book uses the same template, you can quickly skill up on additional database technologies.

Build and deploy your AI models successfully by exploring model governance, fairness, bias, and potential pitfallsPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn ethical AI principles, frameworks, and governanceUnderstand the concepts of fairness assessment and bias mitigationIntroduce explainable AI and transparency in your machine learning modelsBook DescriptionResponsible AI in the Enterprise is a comprehensive guide to implementing ethical, transparent, and compliant AI systems in an organization. With a focus on understanding key concepts of machine learning models, this book equips you with techniques and algorithms to tackle complex issues such as bias, fairness, and model governance.Throughout the book, you'll gain an understanding of FairLearn and InterpretML, along with Google What-If Tool, ML Fairness Gym, IBM AI 360 Fairness tool, and Aequitas. You'll uncover various aspects of responsible AI, including model interpretability, monitoring and management of model drift, and compliance recommendations. You'll gain practical insights into using AI governance tools to ensure fairness, bias mitigation, explainability, privacy compliance, and privacy in an enterprise setting. Additionally, you'll explore interpretability toolkits and fairness measures offered by major cloud AI providers like IBM, Amazon, Google, and Microsoft, while discovering how to use FairLearn for fairness assessment and bias mitigation. You'll also learn to build explainable models using global and local feature summary, local surrogate model, Shapley values, anchors, and counterfactual explanations.By the end of this book, you'll be well-equipped with tools and techniques to create transparent and accountable machine learning models.What you will learnUnderstand explainable AI fundamentals, underlying methods, and techniquesExplore model governance, including building explainable, auditable, and interpretable machine learning modelsUse partial dependence plot, global feature summary, individual condition expectation, and feature interactionBuild explainable models with global and local feature summary, and influence functions in practiceDesign and build explainable machine learning pipelines with transparencyDiscover Microsoft FairLearn and marketplace for different open-source explainable AI tools and cloud platformsWho this book is forThis book is for data scientists, machine learning engineers, AI practitioners, IT professionals, business stakeholders, and AI ethicists who are responsible for implementing AI models in their organizations.Table of ContentsA Primer on Explainable and Ethical AIAlgorithms Gone Wild - Bias's Greatest HitsOpening the Algorithmic BlackboxOperationalizing Model MonitoringModel Governance - Audit, and Compliance Standards & RecommendationsEnterprise Starter Kit for Fairness, Accountability and TransparencyInterpretability Toolkits and Fairness MeasuresFairness in AI System with Microsoft FairLearnFairness Assessment and Bias Mitigation with FairLearn and Responsible AI ToolboxFoundational Models and Azure OpenAI

Become a data wrangling expert and make well-informed decisions by effectively utilizing and analyzing raw unstructured data in a systematic mannerPurchase of the print or Kindle book includes a free PDF eBookKey Features:Implement query optimization during data wrangling using the SQL language with practical use casesMaster data cleaning, handle the date function and null value, and write subqueries and window functionsPractice self-assessment questions for SQL-based interviews and real-world case study roundsBook Description:The amount of data generated continues to grow rapidly, making it increasingly important for businesses to be able to wrangle this data and understand it quickly and efficiently. Although data wrangling can be challenging, with the right tools and techniques you can efficiently handle enormous amounts of unstructured data.The book starts by introducing you to the basics of SQL, focusing on the core principles and techniques of data wrangling. You'll then explore advanced SQL concepts like aggregate functions, window functions, CTEs, and subqueries that are very popular in the business world. The next set of chapters will walk you through different functions within SQL query that cause delays in data transformation and help you figure out the difference between a good query and bad one. You'll also learn how data wrangling and data science go hand in hand. The book is filled with datasets and practical examples to help you understand the concepts thoroughly, along with best practices to guide you at every stage of data wrangling.By the end of this book, you'll be equipped with essential techniques and best practices for data wrangling, and will predominantly learn how to use clean and standardized data models to make informed decisions, helping businesses avoid costly mistakes.What You Will Learn:Build time series models using data wranglingDiscover data wrangling best practices as well as tips and tricksFind out how to use subqueries, window functions, CTEs, and aggregate functionsHandle missing data, data types, date formats, and redundant dataBuild clean and efficient data models using data wrangling techniquesRemove outliers and calculate standard deviation to gauge the skewness of dataWho this book is for:This book is for data analysts looking for effective hands-on methods to manage and analyze large volumes of data using SQL. The book will also benefit data scientists, product managers, and basically any role wherein you are expected to gather data insights and develop business strategies using SQL as a language. If you are new to or have basic knowledge of SQL and databases and an understanding of data cleaning practices, this book will give you further insights into how you can apply SQL concepts to build clean, standardized data models for accurate analysis.

Revamp your data landscape and implement highly effective data pipelines in AWS with this hands-on guidePurchase of the print or Kindle book includes a free PDF eBookKey Features:Execute extract, transform, and load (ETL) tasks on data lakes, data warehouses, and databasesImplement effective Pandas data operation with data wranglerIntegrate pipelines with AWS data servicesBook Description:Data wrangling is the process of cleaning, transforming, and organizing raw, messy, or unstructured data into a structured format. It involves processes such as data cleaning, data integration, data transformation, and data enrichment to ensure that the data is accurate, consistent, and suitable for analysis. Data Wrangling on AWS equips you with the knowledge to reap the full potential of AWS data wrangling tools.First, you'll be introduced to data wrangling on AWS and will be familiarized with data wrangling services available in AWS. You'll understand how to work with AWS Glue DataBrew, AWS data wrangler, and AWS Sagemaker. Next, you'll discover other AWS services like Amazon S3, Redshift, Athena, and Quicksight. Additionally, you'll explore advanced topics such as performing Pandas data operation with AWS data wrangler, optimizing ML data with AWS SageMaker, building the data warehouse with Glue DataBrew, along with security and monitoring aspects.By the end of this book, you'll be well-equipped to perform data wrangling using AWS services.What You Will Learn:Explore how to write simple to complex transformations using AWS data wranglerUse abstracted functions to extract and load data from and into AWS datastoresConfigure AWS Glue DataBrew for data wranglingDevelop data pipelines using AWS data wranglerIntegrate AWS security features into Data Wrangler using identity and access management (IAM)Optimize your data with AWS SageMakerWho this book is for:This book is for data engineers, data scientists, and business data analysts looking to explore the capabilities, tools, and services of data wrangling on AWS for their ETL tasks. Basic knowledge of Python, Pandas, and a familiarity with AWS tools such as AWS Glue, Amazon Athena is required to get the most out of this book.

This book covers various design problems related to the VLSI modulation circuits and their applications in the areas of signal processing, data conversion, and power management. It provides a good reference for learning through problem solving of PWM modulation, sigma-delta modulation, and special VLSI circuit techniques, such as class-D amplifier circuits, lock-in amplifier circuits, switched-capacitor charge pump circuits, DC/DC converter circuits, chopper circuits, mixer circuit, and fractional-N PLL circuits.

Use easy-to-apply patterns in SQL and Python to adopt modern analytics engineering to build agile platforms with dbt that are well-tested and simple to extend and runPurchase of the print or Kindle book includes a free PDF eBookKey Features- Build a solid dbt base and learn data modeling and the modern data stack to become an analytics engineer- Build automated and reliable pipelines to deploy, test, run, and monitor ELTs with dbt Cloud- Guided dbt + Snowflake project to build a pattern-based architecture that delivers reliable datasetsBook Descriptiondbt Cloud helps professional analytics engineers automate the application of powerful and proven patterns to transform data from ingestion to delivery, enabling real DataOps. This book begins by introducing you to dbt and its role in the data stack, along with how it uses simple SQL to build your data platform, helping you and your team work better together. You'll find out how to leverage data modeling, data quality, master data management, and more to build a simple-to-understand and future-proof solution. As you advance, you'll explore the modern data stack, understand how data-related careers are changing, and see how dbt enables this transition into the emerging role of an analytics engineer. The chapters help you build a sample project using the free version of dbt Cloud, Snowflake, and GitHub to create a professional DevOps setup with continuous integration, automated deployment, ELT run, scheduling, and monitoring, solving practical cases you encounter in your daily work. By the end of this dbt book, you'll be able to build an end-to-end pragmatic data platform by ingesting data exported from your source systems, coding the needed transformations, including master data and the desired business rules, and building well-formed dimensional models or wide tables that'll enable you to build reports with the BI tool of your choice.What you will learn- Create a dbt Cloud account and understand the ELT workflow- Combine Snowflake and dbt for building modern data engineering pipelines- Use SQL to transform raw data into usable data, and test its accuracy- Write dbt macros and use Jinja to apply software engineering principles- Test data and transformations to ensure reliability and data quality- Build a lightweight pragmatic data platform using proven patterns- Write easy-to-maintain idempotent code using dbt materializationWho this book is forThis book is for data engineers, analytics engineers, BI professionals, and data analysts who want to learn how to build simple, futureproof, and maintainable data platforms in an agile way. Project managers, data team managers, and decision makers looking to understand the importance of building a data platform and foster a culture of high-performing data teams will also find this book useful. Basic knowledge of SQL and data modeling will help you get the most out of the many layers of this book. The book also includes primers on many data-related subjects to help juniors get started.Table of Contents- Basics of SQL to transform data- Setting up your dbt Cloud development environment- Data modelling for data engineering- Analytics Engineering as the New Core of Data Engineering- Transforming data with dbt- Writing Maintainable Code- Working with Dimensional Data- Delivering Consistency In Your Code- Delivering Reliability In Your Data- Agile development- Collaboration- Deployment, Execution and Documentation Automation- Moving beyond basics- Enhancing Software Quality- Patterns for frequent use cases

"This practical book shows you how to ensure that the data your organization relies on is trustworthy ... [and] explain[s] how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on issues, and resolve problems immediately"--

Unleash the full potential of Power BI with the integration of AI and machine learning techniques using OpenAIPurchase of the print or Kindle book includes a free PDF eBookKey Features:Take flight with Power BI machine learning and OpenAI using hands-on examples from the FAA airline dataUnlock the full potential of Power BI for advanced analytics using OpenAIDesign stunning data presentations, seamless integration of machine learning tools and technologies with OpenAIBook Description:Microsoft Power BI is the ultimate solution for businesses looking to make data-driven decisions and unlock the full potential of their data. Unleashing Your Data with Power BI Machine Learning and OpenAI is designed for data scientists and BI professionals seeking to improve their existing solutions and workloads using AI.The book explains the intricacies of the subject by using a workshop-style data story for data ingestion, data modeling, analytics, and predictive analytics with Power BI machine learning. Along the way,you'll learn about AI features, AI visuals, R/Python integration, and OpenAI integration.The workshop-style content allows you to practice all your learnings in real-life challenges and gain hands-on experience. Additionally, you'll gain an understanding of AI/ML, step by step, with replicable examples and references. From enhancing data visualizations to building SaaS Power BI ML models, and integrating Azure OpenAI, this book will help you unlock new capabilities in Power BI.By the end of this book, you'll be well-equipped to build ML models in Power BI, plan projects for both BI and ML, understand R/Python visuals with Power BI, and introduce OpenAI to enhance your analytics solutions.What You Will Learn:Discover best practices for implementing AI and ML capabilities in Power BI along with integration of OpenAI into the solutionUnderstand how to integrate OpenAI and cognitive services into Power BIExplore how to build a SaaS auto ML model within Power BIGain an understanding of R/Python integration with Power BIEnhance data visualizations for ML feature discoveryDiscover how to improve existing solutions and workloads using AI and ML capabilities in Power BI with OpenAIAcquire tips and tricks for successfully using AI and ML capabilities in Power BI along with integration of OpenAI into the solutionWho this book is for:This book is for data science and BI professionals looking to expand their skill sets into Power BI machine learning and OpenAI. This book is also useful for data scientists, data analysts, and IT professionals who want to learn how to incorporate OpenAI into Power BI for advanced experience.

Discover how Snowflake's unique objects and features can be used to leverage universal modeling techniques through real-world examples and SQL recipesPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn core modeling techniques tied to practical examples using native Snowflake architectureAdopt a universal modeling language to communicate business value to functional teamsGo beyond physical modeling with SQL recipes to transform and shape your Snowflake dataBook DescriptionThe Snowflake Data Cloud is one of the fastest-growing platforms for data warehousing and application workloads. Snowflake's scalable, cloud-native architecture and expansive set of features and objects enables you to deliver data solutions quicker than ever before. Yet, we must ensure that these solutions are developed using recommended design patterns and accompanied by documentation that's easily accessible to everyone in the organization. This book will help you get familiar with simple and practical data modeling frameworks that accelerate agile design and evolve with the project from concept to code. These universal principles have helped guide database design for decades, and this book pairs them with unique Snowflake-native objects and examples like never before - giving you a two-for-one crash course in theory as well as direct application. By the end of this Snowflake book, you'll have learned how to leverage Snowflake's innovative features, such as time travel, zero-copy cloning, and change-data-capture, to create cost-effective, efficient designs through time-tested modeling principles that are easily digestible when coupled with real-world examples.What you will learnDiscover the time-saving features and applications of data modelingExplore Snowflake's cloud-native architecture and featuresUnderstand and apply modeling concepts, techniques, and language using Snowflake objectsMaster modeling concepts such as normalization and slowly changing dimensionsGet comfortable reading and transforming semi-structured dataWork directly with pre-built recipes and examplesApply modeling frameworks from Star to Data VaultWho this book is forThis book is for developers working with SQL who are looking to build a strong foundation in modeling best practices and gain an understanding of where they can be effectively applied to save time and effort. Whether you're an ace in SQL logic or starting out in database design, this book will equip you with the practical foundations of data modeling to guide you on your data journey with Snowflake. Developers who've recently discovered Snowflake will be able to uncover its core features and learn to incorporate them into universal modeling frameworks.Table of ContentsUnlocking the Power of ModelingAn Introduction to the Four Modeling TypesMastering Snowflake's ArchitectureMastering Snowflake ObjectsSpeaking Modeling through Snowflake ObjectsSeeing Snowflake's Architecture through Modeling NotationPutting Conceptual Modeling into PracticePutting Logical Modeling into PracticeDatabase NormalizationDatabase Naming and StructurePutting Physical Modeling into PracticePutting Transformational Modeling into PracticeModeling Slowly Changing DimensionsModeling Facts for Rapid AnalysisModeling Semi-Structured DataModeling HierarchiesScaling Data Models through Modern TechniquesAppendix

The Align > Refine > Design series covers conceptual, logical, and physical data modeling (schema design and patterns) for leading technologies, combining proven data modeling practices with database-specific features to produce better applications. Read MongoDB Data Modeling and Schema Design if you are a data professional who needs to expand your modeling skills to include MongoDB or a technologist who knows MongoDB but needs to grow your schema design skills.The book's introduction and three chapters cover the Align, Refine, and Design approach. We include what the level does in the name by rebranding Conceptual, Logical, and Physical into Align, Refine, and Design. The introduction covers the three modeling characteristics of precise, minimal, and visual; the three model components of entities, relationships, and attributes (including keys); the three model levels of conceptual (align), logical (refine), and physical (design); and the three modeling perspectives of relational, dimensional, and query. Chapter 1, Align, is about agreeing on the common business vocabulary so everyone is aligned on terminology and general initiative scope. Chapter 2, Refine, is about capturing the business requirements. That is, refining our knowledge of the initiative to focus on what is essential. Chapter 3, Design, is about the technical requirements. That is, designing to accommodate our model's unique software and hardware needs.Align, Refine, and Design-that's the approach followed in this book and reinforced through an animal shelter case study. If you are interested in learning how to build multiple database solutions, read all the books in the Align > Refine > Design series. Since each book uses the same template, you can quickly skill up on additional database technologies.

The Align > Refine > Design series covers conceptual, logical, and physical data modeling (schema design and patterns) for leading technologies, combining proven data modeling practices with database-specific features to produce better applications. Read Neo4j Data Modeling if you are a data professional who needs to expand your modeling skills to include Neo4j or a technologist who knows Neo4j but needs to grow your schema design skills.The book's introduction and three chapters cover the Align, Refine, and Design approach. We include what the level does in the name by rebranding Conceptual, Logical, and Physical into Align, Refine, and Design. The introduction covers the three modeling characteristics of precise, minimal, and visual; the three model components of entities, relationships, and attributes (including keys); the three model levels of conceptual (align), logical (refine), and physical (design); and the three modeling perspectives of relational, dimensional, and query. Chapter 1, Align, is about agreeing on the common business vocabulary so everyone is aligned on terminology and general initiative scope. Chapter 2, Refine, is about capturing the business requirements. That is, refining our knowledge of the initiative to focus on what is essential. Chapter 3, Design, is about the technical requirements. That is, designing to accommodate our model's unique software and hardware needs.Align, Refine, and Design-that's the approach followed in this book and reinforced through an animal shelter case study. If you are interested in learning how to build multiple database solutions, read all the books in the Align > Refine > Design series. Since each book uses the same template, you can quickly skill up on additional database technologies.

What does platform thinking mean? It is the ability to put platform-based mechanisms at the core of digital business transformations in business.Digital platforms like Uber, Spotify, Airbnb, Booking.com, and many others completely re-shaped the daily life of millions of users. However, many innovation leaders struggle to conceive the significance of their impact. Platform-based business models are not just the real of geeks' startups, digital services, and Unicorns.This book shows there that their potential in creating value is higher than suspected.Even though platform ecosystems are complex, readers will take on a journey to become platform thinkers. Their eyes will be trained to look beyond what's visible and start reading the platform world around them. Then, they will be guided into a step-by-step process and learn how to write a new platform model from scratch.This book is the result of a decade of research. It offers both a framework and practical instruments to champion digital transformation in any organization. It is specially intended for those who are interested in the glittering platform world and are still trying to figure it out.

Have you ever wondered how the technology that is shaping our world works?From mainstream technologies such as computing and the Internet, to emerging technologies such as artificial intelligence and quantum computing, it is no secret that the world is heavily influenced by technology. Yet, the technology that we depend upon is often not fully understood by those not specialized in the field. You are going to learn:What the internet of things really is (and what it is not)How the internet of things is going to affect our livesHow the iot & smart homes will change our householdsWhat iot means for corporations & their business modelsThe main benefits if iot (this will surprise you)Current shortcomings in iot to watch out forThis book is an essential resource for engineers, developers, and data scientists working in the field of industrial IoT. Whether you're a beginner or an experienced practitioner, this book will provide you with the knowledge and skills you need to build effective predictive maintenance systems for industrial IoT applications

"Mein MySQL Buch" mit Grundlagen, Fortgeschrittene Techniken und Best Practices ist das ultimative Handbuch für jeden, der seine Kenntnisse im Bereich Datenbankmanagement erweitern möchte. "Mein MySQL Buch" bietet eine umfassende Einführung in mySQL sowie zahlreiche fortgeschrittene Techniken und Best Practices, die von Einsteigern, Auszubildenden, Fortgeschrittenen und Experten gleichermaßen genutzt werden können."Mein MySQL Buch" ist in einer Schritt-für-Schritt-Struktur aufgebaut und bietet detaillierte Anleitungen, zahlreiche kommentierte Beispiele, Übungen, Multple Choise Aufgaben nach jedem Kapitel, Fallbeispiele, sowie ein umfangreiches Stichwortregister mit Erklärungen, um das Verständnis des Lesers zu verbessern. Jedes Kapitel baut auf den vorherigen Kapiteln auf, um eine nahtlose Lernumgebung zu schaffen.

Der Standardkommentar zum Haftungsrecht des Straßenverkehrs erscheint in der Neuauflage in der bewährten systematischen Form, die Handbuch und Kommentar verbindet. Das Werk behandelt sämtliche Tatbestände einer verschuldensunabhängigen Haftung, erläutert die Haftung aus unerlaubter Handlung sowie alle sonstigen Haftungstatbestände und enthält alle Regelungskomplexe, aus denen sich eine Beschränkung oder ein Ausschluss der Haftung herleiten lässt. Jeweils gesonderte Abschnitte sind dem Ersatz des Sachschadens und des Personenschadens in all ihren Ausprägungen gewidmet. Ausführlich behandelt wird der Forderungsübergang auf Privatversicherer, Sozialversicherungs- und Versorgungsträger. Erläuterungen zu den prozessualen, insbesondere beweisrechtlichen Fragen der Verkehrsunfallhaftung schließen das Werk ab. In der Neuauflage wird die Rechtsprechung zum Schadensersatzrecht weiter ausdifferenziert, das neue Versicherungsvertragsgesetz eingearbeitet und der verstärkte Einfluss des internationalen und europäischen Rechts auf das Verkehrshaftungsrecht berücksichtigt.

Take your Power BI reports to the next level by learning various data modeling techniques and leveraging the latest features of Power BI effectivelyPurchase of the print or Kindle book includes a free eBook in PDF format.Key Features:Get an understanding of data modeling techniques using Power BI with this up-to-date guideLearn how to define the relationships between data sets to extract valuable insightsExplore best practices for data preparation and modeling and build optimal data models to solve a wide variety of real-world business challengesBook Description:This book is a comprehensive guide to understanding the ins and outs of data modeling and how to create full-fledged data models using Power BI confidently.In this new, fully updated edition, you'll learn how to connect data from multiple sources, understand data, define and manage relationships between data, and shape data models to gain deep and detailed insights about your organization. As you advance through the chapters, the book will demonstrate how to prepare efficient data models in the Power Query Editor and use simpler DAX code with new data modeling features. You'll explore how to use the various data modeling and navigation techniques and perform custom calculations using the modeling features with the help of real-world examples. Finally, you'll learn how to use some new and advanced modeling features to enhance your data models to carry out a wide variety of complex tasks. Additionally, you'll learn valuable best practices and explore common data modeling complications and the solutions to supercharge the process of creating a data model in Power BI and build better-performing data models.By the end of this Power BI book, you'll have gained the skills you need to structure data coming from multiple sources in different ways to create optimized data models that support high-performing reports and data analytics.What You Will Learn:Implement virtual tables and time intelligence functionalities in DAX to build a powerful modelIdentify Dimension and Fact tables and implement them in Power Query EditorDeal with advanced data preparation scenarios while building Star SchemaDiscover different hierarchies and their common pitfallsUnderstand complex data models and how to decrease the level of model complexity with different approachesLearn advanced data modeling techniques such as calculation groups, aggregations, incremental refresh, RLS/OLS, and moreGet well-versed with datamarts and dataflows in PowerBIWho this book is for:This MS Power BI book is for BI users, data analysts, and analysis developers who want to become well-versed with data modeling techniques to make the most of Power BI. Basic working knowledge of Power BI and the Star Schema functionality are required to help you to understand the concepts covered in this book.

This concise but comprehensive book engagingly summarizes both the broad themes and specific tenets of First Amendment law, and the strongest arguments for and against protecting controversial speech such as hate speech and disinformation. It explains the many speech-protective legal rules that emerged during the Civil Rights era, demonstrating how essential free speech is for other human rights.

"The Art of Randomness teaches readers to harness the power of randomness (and Python code) to solve real-world problems in programming, science, and art through hands-on experiments-from simulating evolution to encrypting messages to making machine-learning algorithms. Each chapter describes how randomness plays into the given topic area, then proceeds to demonstrate its problem-solving role with hands-on experiments to work through using Python code"--