Databaser

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 relational databases, 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 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

Using Power Query, you can import, reshape, and cleanse any data from a simple interface, so you can mine that data for all of its hidden insights. Power Query is embedded in Excel, Power BI, and other Microsoft products, and leading Power Query expert Gil Raviv will help you make the most of it. Discover how to eliminate time-consuming manual data preparation, solve common problems, avoid pitfalls, and more. Then, walk through several complete analytics challenges, and integrate all your skills in a realistic chapter-length final project. By the time you're finished, you'll be ready to wrangle any data-and transform it into actionable knowledge. Prepare and analyse your data the easy way, with Power Query Quickly prepare data for analysis with Power Query in Excel (also known as Get & Transform) and in Power BI Solve common data preparation problems with a few mouse clicks and simple formula edits Combine data from multiple sources, multiple queries, and mismatched tables Master basic and advanced techniques for unpivoting tables Customise transformations and build flexible data mashups with the M formula language Address collaboration challenges with Power Query Gain crucial insights into text feeds Streamline complex social network analytics so you can do it yourself

Du får en grundig introduktion til både Access 2016 og databaser generelt. Du lærer at bygge din egen database helt fra bunden. Du lærer selve kernen i databaser: at lave relationer i databasen, så du kan koble forskellige tabeller i databasen sammen. Du lærer også teorien bag, så du undgår databasens mange faldgruber. Du lærer at oprette indtastningsformularer, at importere data fra f.eks. txt- og csv-filer og Excel-dokumenter, og du lærer at danne avancerede rapporter med mulighed for at lave sum- og andre beregninger.For at komme godt i gang med Access, introducerer vi i starten af hæftet grundbegreber inden for databaser, ligesom vi gennemgår "normalisering", som er den proces, du skal igennem, for at transformere rådata eller ustrukturerede data til at være data i en form, som egner sig til en database.Access 2016 er Microsofts databaseprogram, og programmet retter sig mod brugere, der har behov for en mindre til mellemstor database med op til 100.000 poster.

This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.

Dette hæfte giver en hurtig og letlæst introduktion til SQL.SQL anvendes til at håndtere data og du vil lære at hente, sortere og filtrere data, oprette beregninger, gruppere data, samt kombinere data fra forskellige tabeller. Når du har læst hæftet vil du også kunne indsætte, opdatere og slette data.Hæftet er rettet mod dig som på en hurtig og nem måde vil lære SQL og ønsker at kunne anvende det i dagligdagen til løsning af database relaterede opgaver. Hvis du har egen virksomhed kan SQL bl.a. bruges til at holde styr på dine kunders ordrer eller dit lager.Hæftet er primært rettet mod brugere af Microsoft SQL Server, men en stor del af indholdet kan også anvendes på andre databaseplatforme som f.eks. ORACLE.

Agile Data Warehouse Design is a step-by-step guide for capturing data warehousing/business intelligence (DW/BI) requirements and turning them into high performance dimensional models in the most direct way: by modelstorming (data modeling + brainstorming) with BI stakeholders.This book describes BEAM¿, an agile approach to dimensional modeling, for improving communication between data warehouse designers, BI stakeholders and the whole DW/BI development team. BEAM¿ provides tools and techniques that will encourage DW/BI designers and developers to move away from their keyboards and entity relationship based tools and model interactively with their colleagues. The result is everyone thinks dimensionally from the outset! Developers understand how to efficiently implement dimensional modeling solutions. Business stakeholders feel ownership of the data warehouse they have created, and can already imagine how they will use it to answer their business questions. Within this book, you will learn:¿ Agile dimensional modeling using Business Event Analysis & Modeling (BEAM¿) ¿ Modelstorming: data modeling that is quicker, more inclusive, more productive, and frankly more fun!¿ Telling dimensional data stories using the 7Ws (who, what, when, where, how many, why and how)¿ Modeling by example not abstraction; using data story themes, not crow's feet, to describe detail¿ Storyboarding the data warehouse to discover conformed dimensions and plan iterative development¿ Visual modeling: sketching timelines, charts and grids to model complex process measurement - simply¿ Agile design documentation: enhancing star schemas with BEAM¿ dimensional shorthand notation¿ Solving difficult DW/BI performance and usability problems with proven dimensional design patternsLawrence Corr is a data warehouse designer and educator. As Principal of DecisionOne Consulting, he helps clients to review and simplify their data warehouse designs, and advises vendors on visual data modeling techniques. He regularly teaches agile dimensional modeling courses worldwide and has taught dimensional DW/BI skills to thousands of students.Jim Stagnitto is a data warehouse and master data management architect specializing in the healthcare, financial services, and information service industries. He is the founder of the data warehousing and data mining consulting firm Llumino.

Learn how to create and profit from NFTsThe NFT Handbook is a detailed guide on how to create, sell and buy non-fungible tokens without the need for a technical background. Learn exactly what NFTs are, how they evolved, and why they have value.We'll delve into the different types and aspects of NFTs and discuss the different NFT marketplaces and the pros and cons of each.* Create Your Own NFTs: Step by step instructions on all aspects of NFT creation, including what types of content to use, where to source content, adding artistic design, writing the NFT's description, adding optional unlockable content and setting an optional ongoing royalty.* Mint Your NFTs: You'll learn the process of how to get your NFTs on the blockchain.* Sell Your NFTs: We'll go through the whole process including creating a collection, and the different options such as setting a price or starting an auction.* Buy NFTs: What you'll need to bid on and purchase NFTs, and how to avoid getting scammed.* You'll also get a primer on blockchain, particularly the Ethereum cryptocurrency and "gas" fees. You'll also be shown step by step how to create, secure and fund your own cryptocurrency wallet, where you'll store your NFTs and cryptocurrency.Whether you're experienced with the blockchain and crypto or a complete noob (beginner), The NFT Handbook will guide you in the process of creating, minting, selling and buying NFTs.

Easy steps to practical databasesPeople who really know how to build, populate, and simplify databases are few and far between. Access 2019 For Dummies is here to help you join the ranks of office heroes who possess these precious skills.This book offers clear and simple advice on how to build and operate databases as well as create simple forms, import data from outside sources, query databases for information, and share knowledge in reports. In short, it's the book that holds all the secrets behind the mysteries of Access!* Build effective databases from the ground up* Simplify your data entry with forms and tables* Write queries that produce answers to your data questions* Simplify input with formsThere's no time like the present to get your hands on the insight that database beginners need to become Access gurus.

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 "e;data-analytic thinking"e; 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. Youll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your companys data science projects. Youll 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 organizationand how you can use it for competitive advantageTreat data as a business asset that requires careful investment if youre to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

If you're an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds.

Master the Shiny web frameworkand take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses. Users will be able to jump between datasets, explore different subsets or facets of the data, run models with parameter values of their choosing, customize visualizations, and much more.Hadley Wickham from RStudio shows data scientists, data analysts, statisticians, and scientific researchers with no knowledge of HTML, CSS, or JavaScript how to create rich web apps from R. This in-depth guide provides a learning path that you can follow with confidence, as you go from a Shiny beginner to an expert developer who can write large, complex apps that are maintainable and performant.Get started: Discover how the major pieces of a Shiny app fit togetherPut Shiny in action: Explore Shiny functionality with a focus on code samples, example apps, and useful techniquesMaster reactivity: Go deep into the theory and practice of reactive programming and examine reactive graph componentsApply best practices: Examine useful techniques for making your Shiny apps work well in production

Computing power has revolutionized the theory and practice of statistical inference. Now in paperback, and fortified with 130 class-tested exercises, this book explains modern statistical thinking from classical theories to state-of-the-art prediction algorithms. Anyone who applies statistical methods to data will value this landmark text.

Discover how data science can help you gain in-depth insight into your business - the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles.

Deep learning is changing everything. This machine-learning method has already surpassed traditional computer vision techniques, and the same is happening with NLP. If you're looking to bring deep learning into your domain, this practical book will bring you up to speed on key concepts using Facebook's PyTorch framework.Once author Ian Pointer helps you set up PyTorch on a cloud-based environment, you'll learn how use the framework to create neural architectures for performing operations on images, sound, text, and other types of data. By the end of the book, you'll be able to create neural networks and train them on multiple types of data.Learn how to deploy deep learning models to productionExplore PyTorch use cases in companies other than FacebookLearn how to apply transfer learning to imagesApply cutting-edge NLP techniques using a model trained on Wikipedia

Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover.Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started.Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learningSet up and manage machine learning projects end-to-endBuild an anomaly detection system to catch credit card fraudClusters users into distinct and homogeneous groupsPerform semisupervised learningDevelop movie recommender systems using restricted Boltzmann machinesGenerate synthetic images using generative adversarial networks

Deep learning is one of today's hottest fields. This approach to machine learning is achieving breakthrough results in some of today's highest profile applications, in organizations ranging from Google to Tesla, Facebook to Apple. Thousands of technical professionals and students want to start leveraging its power, but previous books on deep learning have often been non-intuitive, inaccessible, and dry. In Deep Learning Illustrated, three world-class instructors and practitioners present a uniquely visual, intuitive, and accessible high-level introduction to the techniques and applications of deep learning. Packed with vibrant, full-color illustrations, it abstracts away much of the complexity of building deep learning models, making the field more fun to learn and accessible to a far wider audience. Part I's high-level overview explains what Deep Learning is, why it has become so ubiquitous, and how it relates to concepts and terminology such as Artificial Intelligence, Machine Learning, Artificial Neural Networks, and Reinforcement Learning. These opening chapters are replete with vivid illustrations, easy-to-grasp analogies, and character-focused narratives. Building on this foundation, the authors then offer a practical reference and tutorial for applying a wide spectrum of proven deep learning techniques. Essential theory is covered with as little mathematics as possible and is illuminated with hands-on Python code. Theory is supported with practical "run-throughs" available in accompanying Jupyter notebooks, delivering a pragmatic understanding of all major deep learning approaches and their applications: machine vision, natural language processing, image generation, and videogaming. To help readers accomplish more in less time, the authors feature several of today's most widely used and innovative deep learning libraries, including TensorFlow and its high-level API, Keras; PyTorch; and the recently released, high-level Coach, a TensorFlow API that abstracts away the complexity typically associated with building Deep Reinforcement Learning algorithms. Ideal for software developers, data scientists, and analysts at all levels of experience Teaches through simple visuals, accessible Python code examples, character-driven narratives, and intuitive analogies Covers today's leading applications, including machine vision, natural language processing, image generation, and videogames Introduces four powerful Deep Learning libraries: TensorFlow, Keras, PyTorch, and Coach Carefully designed to minimize mathematical formulae and avoid unnecessary complexity The first full-color, illustrated, hands-on guide to the fundamentals of modern, deep-learning AI: simply the most intuitive, practical way to get started Ideal for software developers, data scientists, and analysts at all levels of experience Teaches through simple visuals, accessible Python code examples, character-driven narratives, and intuitive analogies Covers today's leading applications, including machine vision, natural language processing, image generation, and videogames Introduces four powerful Deep Learning libraries: TensorFlow, Keras, PyTorch, and Coach Carefully designed to minimize mathematical formulae and avoid unnecessary complexity

The real-life trades and strategies of a successful cryptocurrency trader.

The most thorough SQL reference, now updated for SQL:2023 SQL All-in-One For Dummies has everything you need to get started with the SQL programming language, and then to level up your skill with advanced applications. This relational database coding language is one of the most used languages in professional software development. And, as it becomes ever more important to take control of data, there's no end in sight to the need for SQL know-how. You can take your career to the next level with this guide to creating databases, accessing and editing data, protecting data from corruption, and integrating SQL with other languages in a programming environment. Become a SQL guru and turn the page on the next chapter of your coding career. Get 7 mini-books in one, covering basic SQL, database development, and advanced SQL concepts Read clear explanations of SQL code and learn to write complex queries Discover how to apply SQL in real-world situations to gain control over large datasets Enjoy a thorough reference to common tasks and issues in SQL developmentThis Dummies All-in-One guide is for all SQL users--from beginners to more experienced programmers. Find the info and the examples you need to reach the next stage in your SQL journey.

Do your product dashboards look funky? Are your quarterly reports stale? Is the data set you're using broken or just plain wrong? These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner. If you answered yes to these questions, this book is for you.Many data engineering teams today face the "e;good pipelines, bad data"e; problem. It doesn't matter how advanced your data infrastructure is if the data you're piping is bad. In this book, Barr Moses, Lior Gavish, and Molly Vorwerck, from the data observability company Monte Carlo, explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some of the world's most innovative companies.Build more trustworthy and reliable data pipelinesWrite scripts to make data checks and identify broken pipelines with data observabilityLearn how to set and maintain data SLAs, SLIs, and SLOsDevelop and lead data quality initiatives at your companyLearn how to treat data services and systems with the diligence of production softwareAutomate data lineage graphs across your data ecosystemBuild anomaly detectors for your critical data assets

This book explains data quality management in practical terms, focusing on three key areas - the nature of data in enterprises, the purpose and scope of data quality management, and implementing a data quality management system, in line with ISO 8000-61.

Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack.You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions.You'll learn:What a data pipeline is and how it worksHow data is moved and processed on modern data infrastructure, including cloud platformsCommon tools and products used by data engineers to build pipelinesHow pipelines support analytics and reporting needsConsiderations for pipeline maintenance, testing, and alerting