Databasedesign og databaseteori

Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization.

As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, youll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption.Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed.Examine data management trends, including technological developments, regulatory requirements, and privacy concernsGo deep into the Scaled Architecture and learn how the pieces fit togetherExplore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata

Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for integrating breakthrough privacy-enhancing technologies into production systems. Practical Data Privacy answers important questions such as: What do privacy regulations like GDPR and CCPA mean for my data workflows and data science use cases? What does "anonymized data" really mean? How do I actually anonymize data? How does federated learning and analysis work? Homomorphic encryption sounds great, but is it ready for use? How do I compare and choose the best privacy-preserving technologies and methods? Are there open-source libraries that can help? How do I ensure that my data science projects are secure by default and private by design? How do I work with governance and infosec teams to implement internal policies appropriately?

Harness the power of Microsoft Fabric to develop data analytics solutions for various use cases guided by step-by-step instructionsKey FeaturesExplore Microsoft Fabric and its features through real-world examplesBuild data analytics solutions for lakehouses, data warehouses, real-time analytics, and data scienceMonitor, manage, and administer your Fabric platform and analytics system to ensure flexibility, performance, security, and controlPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionDiscover the capabilities of Microsoft Fabric, the premier unified solution designed for the AI era, seamlessly combining data integration, OneLake, transformation, visualization, universal security, and a unified business model. This book provides an overview of Microsoft Fabric, its components, and the wider analytics landscape.In this book, you'll explore workloads such as Data Factory, Synapse Data Engineering, data science, data warehouse, real-time analytics, and Power BI. You'll learn how to build end-to-end lakehouse and data warehouse solutions using the medallion architecture, unlock the real-time analytics, and implement machine learning and AI models. As you progress, you'll build expertise in monitoring workloads and administering Fabric across tenants, capacities, and workspaces. The book also guides you step by step through enhancing security and governance practices in Microsoft Fabric and implementing CI/CD workflows with Azure DevOps or GitHub. Finally, you'll discover the power of Copilot, an AI-driven assistant that accelerates your analytics journey.By the end of this book, you'll have unlocked the full potential of AI-driven data analytics, gaining a comprehensive understanding of the analytics landscape and mastery over the essential concepts and principles of Microsoft Fabric.What you will learnGet acquainted with the different services available in Microsoft FabricBuild end-to-end data analytics solution to scale and manage high performanceIntegrate data from different types of data sourcesApply transformation with Spark, Notebook, and T-SQLUnderstand and implement real-time stream processing and data science capabilitiesPerform end-to-end processes for building data analytics solutions in the AI eraDrive insights by leveraging Power BI for reporting and visualizationImprove productivity with AI assistance and Copilot integrationWho this book is forThis book is for data professionals, including data analysts, data engineers, data scientists, data warehouse developers, ETL developers, business analysts, AI/ML professionals, software developers, and Chief Data Officers who want to build a future-ready data analytics solution for long-term success in the AI era.For PySpark and SQL students entering the data analytics field, this book offers a broad foundation for developing the skills to build end-to-end analytics systems for various use cases. Basic knowledge of SQL and Spark is assumed.Table of ContentsOverview of Microsoft Fabric and Understanding Its Different ConceptsUnderstanding Different Workloads and Getting Started with Microsoft Fabric Building an End-to-End Analytics System - LakehouseBuilding an End-to-End Analytics System - Data WarehouseBuilding an End-to-End Analytics System - Real-Time AnalyticsBuilding an End-to-End Analytics System - Data ScienceMonitoring Overview and Monitoring Different WorkloadsAdministering FabricSecurity and Governance Overview(N.B. Please use the Look Inside option to see further chapters)

Il lettore viene guidato nelle diverse fasi della progettazione e realizzazione di un database relazionale.Nelle numerose esemplificazioni pratiche viene utilizzato MySQL come software di gestione database.Viene poi trattato il linguaggio SQL per interrogare ed aggiornare il database.Infine vengono presentate le tecniche e gli strumenti per realizzare una applicazione gestionale con il linguaggio C#.Vengono trattati sia l'approccio tradizionale con ADO.NET e anche un approccio moderno con Entity Framework che è un software ORM (Object Relational Mapping).Il lettore è invitato a svolgere degli esercizi per verificare la comprensione dei concetti e poi confrontare la soluzione proposta con la propria.L'autore è stato tutor di Basi di Dati per 5 anni nei corsi di Ingegneria Informatica dell'Università di Padova e da 20 anni insegna Informatica all'ITT "Barsanti" di Castelfranco Veneto.Il suo curriculum dettagliato è visibile nel sito http://robertobandiera.altervista.org/

Take your data preparation, machine learning, and GenAI skills to the next level by learning a range of Python algorithms and tools for data labelingKey FeaturesGenerate labels for regression in scenarios with limited training dataApply generative AI and large language models (LLMs) to explore and label text dataLeverage Python libraries for image, video, and audio data analysis and data labelingPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionData labeling is the invisible hand that guides the power of artificial intelligence and machine learning. In today's data-driven world, mastering data labeling is not just an advantage, it's a necessity. Data Labeling in Machine Learning with Python empowers you to unearth value from raw data, create intelligent systems, and influence the course of technological evolution.With this book, you'll discover the art of employing summary statistics, weak supervision, programmatic rules, and heuristics to assign labels to unlabeled training data programmatically. As you progress, you'll be able to enhance your datasets by mastering the intricacies of semi-supervised learning and data augmentation. Venturing further into the data landscape, you'll immerse yourself in the annotation of image, video, and audio data, harnessing the power of Python libraries such as seaborn, matplotlib, cv2, librosa, openai, and langchain. With hands-on guidance and practical examples, you'll gain proficiency in annotating diverse data types effectively.By the end of this book, you'll have the practical expertise to programmatically label diverse data types and enhance datasets, unlocking the full potential of your data.What you will learnExcel in exploratory data analysis (EDA) for tabular, text, audio, video, and image dataUnderstand how to use Python libraries to apply rules to label raw dataDiscover data augmentation techniques for adding classification labelsLeverage K-means clustering to classify unsupervised dataExplore how hybrid supervised learning is applied to add labels for classificationMaster text data classification with generative AIDetect objects and classify images with OpenCV and YOLOUncover a range of techniques and resources for data annotationWho this book is forThis book is for machine learning engineers, data scientists, and data engineers who want to learn data labeling methods and algorithms for model training. Data enthusiasts and Python developers will be able to use this book to learn data exploration and annotation using Python libraries. Basic Python knowledge is beneficial but not necessary to get started.Table of ContentsExploring Data for Machine LearningLabeling Data for ClassificationLabeling Data for RegressionExploring Image DataLabeling Image Data Using RulesLabeling Image Data Using Data AugmentationLabeling Text DataExploring Video DataLabeling Video DataExploring Audio DataLabeling Audio DataHands-On Exploring Data Labeling Tools

Level up your career by learning best practices for managing the data quality and integrity of your financial dataKey Features:Accelerate data integrity management using artificial intelligence-powered solutionsLearn how business intelligence tools, ledger databases, and database locks solve data integrity issuesFind out how to detect fraudulent transactions affecting financial report integrityBook Description:Data integrity management plays a critical role in the success and effectiveness of organizations trying to use financial and operational data to make business decisions. Unfortunately, there is a big gap between the analysis and management of finance data along with the proper implementation of complex data systems across various organizations.The first part of this book covers the important concepts for data quality and data integrity relevant to finance, data, and tech professionals. The second part then focuses on having you use several data tools and platforms to manage and resolve data integrity issues on financial data. The last part of this the book covers intermediate and advanced solutions, including managed cloud-based ledger databases, database locks, and artificial intelligence, to manage the integrity of financial data in systems and databases.After finishing this hands-on book, you will be able to solve various data integrity issues experienced by organizations globally.What You Will Learn:Develop a customized financial data quality scorecardUtilize business intelligence tools to detect, manage, and resolve data integrity issuesFind out how to use managed cloud-based ledger databases for financial data integrityApply database locking techniques to prevent transaction integrity issues involving finance dataDiscover the methods to detect fraudulent transactions affecting financial report integrityUse artificial intelligence-powered solutions to resolve various data integrity issues and challengesWho this book is for:This book is for financial analysts, technical leaders, and data professionals interested in learning practical strategies for managing data integrity and data quality using relevant frameworks and tools. A basic understanding of finance concepts, accounting, and data analysis is expected. Knowledge of finance management is not a prerequisite, but it'll help you grasp the more advanced topics covered in this book.

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 Cassandra Data Modeling and Schema Design if you are a data professional who needs to expand your modeling skills to include Cassandra or a technologist who knows Cassandra 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.

Learn to create, index and query spatial databases using SQL that applies to PostgreSQL/PostGIS and more.

Build faster and efficient real-world applications on the cloud with a fitting database model that's perfect for your needsKey FeaturesFamiliarize yourself with business and technical considerations involved in modeling the right databaseTake your data to applications, analytics, and AI with real-world examplesLearn how to code, build, and deploy end-to-end solutions with expert advicePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn the age of lightning-speed delivery, customers want everything developed, built, and delivered at high speed and at scale. Knowledge, design, and choice of database is critical in that journey, but there is no one-size-fits-all solution. This book serves as a comprehensive and practical guide for data professionals who want to design and model their databases efficiently. The book begins by taking you through business, technical, and design considerations for databases. Next, it takes you on an immersive structured database deep dive for both transactional and analytical real-world use cases using Cloud SQL, Spanner, and BigQuery. As you progress, you'll explore semi-structured and unstructured database considerations with practical applications using Firestore, cloud storage, and more. You'll also find insights into operational considerations for databases and the database design journey for taking your data to AI with Vertex AI APIs and generative AI examples. By the end of this book, you will be well-versed in designing and modeling data and databases for your applications using Google Cloud.What you will learnUnderstand different use cases and real-world applications of data in the cloudWork with document and indexed NoSQL databasesGet to grips with modeling considerations for analytics, AI, and MLUse real-world examples to learn about ETL servicesDesign structured, semi-structured, and unstructured data for your applications and analyticsImprove observability, performance, security, scalability, latency SLAs, SLIs, and SLOsWho this book is forThis book is for database developers, data engineers, and architects looking to design, model, and build database applications on the cloud with an extended focus on operational consideration and taking their data to AI. Data scientists, as well ML and AI engineers who want to use Google Cloud services in the data to AI journey will also find plenty of useful information in this book. It will also be useful to data analysts and BI developers who want to use SQL impactfully to generate ML and generative AI insights from their data. Table of ContentsData, Databases, and DesignHandling Data on the CloudDatabase Modeling for Structured DataSetting Up a Fully Managed RDBMSDesigning an Analytical Data WarehouseDesigning for Semi-structured DataUnstructured Data ManagementDevOps and DatabasesData to AI - Modeling Your Databases for Analytics and MLLooking Ahead - Designing for LLM Applications

Harness the power of deep learning to drive productivity and efficiency using this practical guide covering techniques and best practices for the entire deep learning life cycleKey Features:Interpret your models' decision-making process, ensuring transparency and trust in your AI-powered solutionsGain hands-on experience in every step of the deep learning life cycleExplore case studies and solutions for deploying DL models while addressing scalability, data drift, and ethical considerationsPurchase of the print or Kindle book includes a free PDF eBookBook Description:Deep learning enables previously unattainable feats in automation, but extracting real-world business value from it is a daunting task. This book will teach you how to build complex deep learning models and gain intuition for structuring your data to accomplish your deep learning objectives.This deep learning book explores every aspect of the deep learning life cycle, from planning and data preparation to model deployment and governance, using real-world scenarios that will take you through creating, deploying, and managing advanced solutions. You'll also learn how to work with image, audio, text, and video data using deep learning architectures, as well as optimize and evaluate your deep learning models objectively to address issues such as bias, fairness, adversarial attacks, and model transparency.As you progress, you'll harness the power of AI platforms to streamline the deep learning life cycle and leverage Python libraries and frameworks such as PyTorch, ONNX, Catalyst, MLFlow, Captum, Nvidia Triton, Prometheus, and Grafana to execute efficient deep learning architectures, optimize model performance, and streamline the deployment processes. You'll also discover the transformative potential of large language models (LLMs) for a wide array of applications.By the end of this book, you'll have mastered deep learning techniques to unlock its full potential for your endeavors.What You Will Learn:Use neural architecture search (NAS) to automate the design of artificial neural networks (ANNs)Implement recurrent neural networks (RNNs), convolutional neural networks (CNNs), BERT, transformers, and more to build your modelDeal with multi-modal data drift in a production environmentEvaluate the quality and bias of your modelsExplore techniques to protect your model from adversarial attacksGet to grips with deploying a model with DataRobot AutoMLWho this book is for:This book is for deep learning practitioners, data scientists, and machine learning developers who want to explore deep learning architectures to solve complex business problems. Professionals in the broader deep learning and AI space will also benefit from the insights provided, applicable across a variety of business use cases. Working knowledge of Python programming and a basic understanding of deep learning techniques is needed to get started with this book.

Save time analyzing volumes of data using a structured method to extract, model, and create insights from your dataKey FeaturesAcquire expertise in using Excel's Data Model and Power Pivot to connect and analyze multiple sources of dataCreate key performance indicators for decision making using DAX and Cube functionsApply your knowledge of Data Model to build an interactive dashboard that delivers key insights to your usersPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMicrosoft Excel's BI solutions have evolved, offering users more flexibility and control over analyzing data directly in Excel. Features like PivotTables, Data Model, Power Query, and Power Pivot empower Excel users to efficiently get, transform, model, aggregate, and visualize data.Data Modeling with Microsoft Excel offers a practical way to demystify the use and application of these tools using real-world examples and simple illustrations.This book will introduce you to the world of data modeling in Excel, as well as definitions and best practices in data structuring for both normalized and denormalized data. The next set of chapters will take you through the useful features of Data Model and Power Pivot, helping you get to grips with the types of schemas (snowflake and star) and create relationships within multiple tables. You'll also understand how to create powerful and flexible measures using DAX and Cube functions.By the end of this book, you'll be able to apply the acquired knowledge in real-world scenarios and build an interactive dashboard that will help you make important decisions.What you will learnImplement the concept of data modeling within and beyond ExcelGet, transform, model, aggregate, and visualize data with Power QueryUnderstand best practices for data structuring in MS ExcelBuild powerful measures using DAX from the Data ModelGenerate flexible calculations using Cube functionsDesign engaging dashboards for your usersWho this book is forThis book is for Excel users looking for hands-on and effective methods to manage and analyze large volumes of data within Microsoft Excel using Power Pivot. Whether you're new or already familiar with Excel's data analytics tools, this book will give you further insights on how you can apply Power Pivot, Data Model, DAX measures, and Cube functions to save time on routine data management tasks. An understanding of Excel's features like tables, PivotTable, and some basic aggregating functions will be helpful but not necessary to make the most of this book.Table of ContentsGetting Started with Data Modeling - Overview and ImportanceData Structuring for Data ModelsPreparing your Data for the Data ModelData Modeling with Power Pivot Creating DAX Calculations from your Data ModelCreating Cube Functions from your Data ModelCommunicating insights from your Data Model using DashboardsVisualization Elements for your DashboardChoosing the right Design ThemesPublication and Deployment

"This book delves into the practical applications of vector search in Elastic and embodies a broader philosophy. It underscores the importance of search in the age of Generative Al and Large Language Models. This narrative goes beyond the 'how' to address the 'why' - highlighting our belief in the transformative power of search and our dedication to pushing boundaries to meet and exceed customer expectations." Shay Banon Founder & CTO at ElasticKey FeaturesInstall, configure, and optimize the ChatGPT-Elasticsearch plugin with a focus on vector dataLearn how to load transformer models, generate vectors, and implement vector search with ElasticDevelop a practical understanding of vector search, including a review of current vector databasesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionWhile natural language processing (NLP) is largely used in search use cases, this book aims to inspire you to start using vectors to overcome equally important domain challenges like observability and cybersecurity. The chapters focus mainly on integrating vector search with Elastic to enhance not only their search but also observability and cybersecurity capabilities.The book, which also features a foreword written by the founder of Elastic, begins by teaching you about NLP and the functionality of Elastic in NLP processes. Here you'll delve into resource requirements and find out how vectors are stored in the dense-vector type along with specific page cache requirements for fast response times. As you advance, you'll discover various tuning techniques and strategies to improve machine learning model deployment, including node scaling, configuration tuning, and load testing with Rally and Python. You'll also cover techniques for vector search with images, fine-tuning models for improved performance, and the use of clip models for image similarity search in Elasticsearch. Finally, you'll explore retrieval-augmented generation (RAG) and learn to integrate ChatGPT with Elasticsearch to leverage vectorized data, ELSER's capabilities, and RRF's refined search mechanism.By the end of this NLP book, you'll have all the necessary skills needed to implement and optimize vector search in your projects with Elastic.What you will learnOptimize performance by harnessing the capabilities of vector searchExplore image vector search and its applicationsDetect and mask personally identifiable informationImplement log prediction for next-generation observabilityUse vector-based bot detection for cybersecurityVisualize the vector space and explore Search.Next with ElasticImplement a RAG-enhanced application using StreamlitWho this book is forIf you're a data professional with experience in Elastic observability, search, or cybersecurity and are looking to expand your knowledge of vector search, this book is for you. This book provides practical knowledge useful for search application owners, product managers, observability platform owners, and security operations center professionals. Experience in Python, using machine learning models, and data management will help you get the most out of this book.Table of ContentsIntroduction to Vectors and EmbeddingsGetting started with Vector Search in ElasticModel Management and Vector Considerations in ElasticPerformance Tuning - Working with dataImage SearchRedacting Personal Identifiable Information Using ElasticsearchNext Generation of Observability Powered by VectorsThe Power of Vectors and Embedding in Bolstering Cybersecurity(N.B. Please use the Look Inside option to see further chapters)

Data modeling is the single most overlooked feature in Power BI Desktop, yet it's what sets Power BI apart from other tools on the market. This practical book serves as your fast-forward button for data modeling with Power BI, Analysis Services tabular, and SQL databases. It serves as a starting point for data modeling, as well as a handy refresher. Author Markus Ehrenmueller-Jensen, founder of Savory Data, shows you the basic concepts of Power BI's semantic model with hands-on examples in DAX, Power Query, and T-SQL. If you're looking to build a data warehouse layer, chapters with T-SQL examples will get you started. You'll begin with simple steps and gradually solve more complex problems. This book shows you how to: Normalize and denormalize with DAX, Power Query, and T-SQL Apply best practices for calculations, flags and indicators, time and date, role-playing dimensions and slowly changing dimensions Solve challenges such as binning, budget, localized models, composite models, and key value with DAX, Power Query, and T-SQL Discover and tackle performance issues by applying solutions in DAX, Power Query, and T-SQL Work with tables, relations, set operations, normal forms, dimensional modeling, and ETL

"Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have solid benefits, but they're also surrounded by a lot of hyperbole and confusion. This practical book provides a guided tour of these architectures to help data professionals understand the pros and cons of each. James Serra, big data and data warehousing solution architect at Microsoft, examines common data architecture concepts, including how data warehouses have had to evolve to work with data lake features. You'll learn what data lakehouses can help you achieve, and how to distinguish data mesh hype from reality. Best of all, you'll be able to determine the most appropriate data architecture for your needs. With this book, you'll: gain a working understanding of several data architectures; learn the strengths and weakness of each approach; distinguish data architecture theory from the reality; pick the best architecture for your use case; understand the differences between data warehouses and data lakes; learn common data architecture concepts to help you build better solutions; explore the historical evolution and characteristics of data architectures; learn essentials of running an architecture design session, team organization, and project success factors."--

Dieses neuartige Lehrbuch richtet sich an Studierende, die entweder einen Einstieg in den Bereich der Grundrechte suchen oder aber die Grundrechte zwecks Vorbereitung auf die Ubungen bzw. das Staatsexamen wiederholen mochten. Das Werk ist konsequent auf die Anforderungen zugeschnitten, mit denen Studierende in Klausuren und Hausarbeiten konfrontiert werden. Alle Grundrechte sowie die prufungsrelevanten Grundzuge der Verfassungsbeschwerde werden ausgehend vom Verfassungstext systematisch erschlossen. Die allgemeinen Grundrechtslehren, die in ihrer Abstraktheit gerade fur den Einsteiger haufig nur schwer verstandlich sind, werden nicht "e;vor die Klammer"e; gezogen, sondern am Beispiel einzelner Grundrechte behandelt. Zum besseren Verstandnis gibt das Buch auerdem die zentralen Entscheidungen des BVerfG in den relevanten Auszugen wieder. Beispielsfalle, deren Losungen uber das Internet bereitgestellt werden, sowie Zusammenstellungen typischer Klausurprobleme runden das Lehrbuch ab. 

===========================Relationale Datenmodellierung ===========================Eine Modellierungshilfe für DB-DesignerVon einem Praktiker für Studierende===========================Das Heft erklärt von der Sequentiellen über die Direktzugriffsmethoden bis zum Relationalen Datenbankdesign die Schritte zur erfolgreichen eigenen DB-Modellierung.Viel Spass dabei!! Urs Niederhauser

Welcome to "Beginning Data Science in R: Data Analysis, Visualization, and Modeling." In this book, we embark on an exciting journey into the world of data science using the R programming language. Whether you're a novice seeking to explore the fundamentals or an experienced practitioner looking for a comprehensive reference, this book is designed to be your companion.Data science has become an integral part of decision-making processes across various industries. From understanding customer behavior to predicting market trends and making informed business choices, the power of data analysis, visualization, and modeling cannot be overstated. R, with its extensive ecosystem of packages and tools, has emerged as a preferred choice for data scientists due to its versatility and ability to handle complex analytical tasks.Our aim in this book is to provide you with a solid foundation in data science techniques using R. We will guide you through the entire data science workflow, from data acquisition and cleaning to exploratory data analysis, visualization, and building predictive models. Each chapter is carefully crafted to introduce concepts progressively, with hands-on examples and practical exercises to reinforce your understanding.

In an era characterized by an explosion of digital information, the ability to harness, analyze, and derive insights from vast and complex datasets has become a cornerstone of success for businesses, organizations, and researchers alike. The convergence of technology, data, and analytics has given rise to the field of Big Data and Analytics-a field that empowers us to uncover hidden patterns, make informed decisions, and unlock new opportunities across various domains.Essentials of Big Data and Analytics serves as a comprehensive guide for navigating the dynamic landscape of data-driven decision-making. As we stand at the crossroads of innovation, this book offers a roadmap to understand the fundamental concepts, techniques, and applications that drive the Big Data revolution. Whether you are a seasoned professional seeking to deepen your understanding or a newcomer eager to grasp the foundations, this book provides a clear and accessible entry point into the world of Big Data.

Get to grips with the fundamental concepts of data engineering, and solve mock interview questions while building a strong resume and a personal brand to attract the right employersKey FeaturesDevelop your own brand, projects, and portfolio with expert help to stand out in the interview roundGet a quick refresher on core data engineering topics, such as Python, SQL, ETL, and data modelingPractice with 50 mock questions on SQL, Python, and more to ace the behavioral and technical roundsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionPreparing for a data engineering interview can often get overwhelming due to the abundance of tools and technologies, leaving you struggling to prioritize which ones to focus on. This hands-on guide provides you with the essential foundational and advanced knowledge needed to simplify your learning journey.The book begins by helping you gain a clear understanding of the nature of data engineering and how it differs from organization to organization. As you progress through the chapters, you'll receive expert advice, practical tips, and real-world insights on everything from creating a resume and cover letter to networking and negotiating your salary. The chapters also offer refresher training on data engineering essentials, including data modeling, database architecture, ETL processes, data warehousing, cloud computing, big data, and machine learning. As you advance, you'll gain a holistic view by exploring continuous integration/continuous development (CI/CD), data security, and privacy. Finally, the book will help you practice case studies, mock interviews, as well as behavioral questions.By the end of this book, you will have a clear understanding of what is required to succeed in an interview for a data engineering role.What you will learnCreate maintainable and scalable code for unit testingUnderstand the fundamental concepts of core data engineering tasksPrepare with over 100 behavioral and technical interview questionsDiscover data engineer archetypes and how they can help you prepare for the interviewApply the essential concepts of Python and SQL in data engineeringBuild your personal brand to noticeably stand out as a candidateWho this book is forIf you're an aspiring data engineer looking for guidance on how to land, prepare for, and excel in data engineering interviews, this book is for you. Familiarity with the fundamentals of data engineering, such as data modeling, cloud warehouses, programming (python and SQL), building data pipelines, scheduling your workflows (Airflow), and APIs, is a prerequisite.Table of ContentsThe Roles and Responsibilities of a Data EngineerMust-Have Data Engineering Portfolio ProjectsBuilding Your Data Engineering Brand on LinkedInPreparing for Behavioral InterviewsEssential Python for Data EngineersUnit TestingDatabase FundamentalsEssential SQL for Data EngineersDatabase Design and OptimizationData Processing and ETLData Pipeline Design for Data EngineersData Warehouses and Data LakesEssential Tools You Should Know AboutContinuous Integration/Continuous Development for Data EngineersData Security and PrivacyAdditional Interview Questions

Looking to revolutionize your data transformation game with AWS? Look no further! From strong foundations to hands-on building of data engineering pipelines, our expert-led manual has got you covered.Key FeaturesDelve into robust AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelinesStay up to date with a comprehensive revised chapter on Data GovernanceBuild modern data platforms with a new section covering transactional data lakes and data meshBook DescriptionThis book, authored by a seasoned Senior Data Architect with 25 years of experience, aims to help you achieve proficiency in using the AWS ecosystem for data engineering. This revised edition provides updates in every chapter to cover the latest AWS services and features, takes a refreshed look at data governance, and includes a brand-new section on building modern data platforms which covers; implementing a data mesh approach, open-table formats (such as Apache Iceberg), and using DataOps for automation and observability.You'll begin by reviewing the key concepts and essential AWS tools in a data engineer's toolkit and getting acquainted with modern data management approaches. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how that transformed data is used by various data consumers. You'll learn how to ensure strong data governance, and about populating data marts and data warehouses along with how a data lakehouse fits into the picture. After that, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. Then, you'll explore how the power of machine learning and artificial intelligence can be used to draw new insights from data. In the final chapters, you'll discover transactional data lakes, data meshes, and how to build a cutting-edge data platform on AWS.By the end of this AWS book, you'll be able to execute data engineering tasks and implement a data pipeline on AWS like a pro!What you will learnSeamlessly ingest streaming data with Amazon Kinesis Data FirehoseOptimize, denormalize, and join datasets with AWS Glue StudioUse Amazon S3 events to trigger a Lambda process to transform a fileLoad data into a Redshift data warehouse and run queries with easeVisualize and explore data using Amazon QuickSightExtract sentiment data from a dataset using Amazon ComprehendBuild transactional data lakes using Apache Iceberg with Amazon AthenaLearn how a data mesh approach can be implemented on AWSWho this book is forThis book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts, while gaining practical experience with common data engineering services on AWS, will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book, but it's not a prerequisite. Familiarity with the AWS console and core services will also help you follow along.Table of ContentsAn Introduction to Data EngineeringData Management Architectures for AnalyticsThe AWS Data Engineer's ToolkitData Governance, Security, and CatalogingArchitecting Data Engineering PipelinesIngesting Batch and Streaming DataTransforming Data to Optimize for AnalyticsIdentifying and Enabling Data ConsumersA Deeper Dive into Data Marts and Amazon RedshiftOrchestrating the Data Pipeline(N.B. Please use the Look Inside option to see further chapters)

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.