The new edition of the classic bestseller that launched the data warehousing industry covers new approaches and technologies, many of which have been pioneered by Inmon himself. In addition to explaining the fundamentals of data warehouse systems, the book covers new topics such as methods for handling unstructured data in a data warehouse and storing data across multiple storage media, and discusses the pros and cons of relational versus multidimensional design and how to measure return on investment in planning data warehouse projects. It covers advanced topics, including data monitoring and testing. Although the book includes an extra 100 pages worth of valuable content, the price has actually been reduced from $65 to $55.

This book constitutes the refereed proceedings of the 6th International Conference on E-Technologies, MCETECH 2015, held in Montreal, Canada, in May 2015. The 18 papers presented in this volume were carefully reviewed and selected from 42 submissions. They have been organized in topical sections on process adaptation; legal issues; social computing; eHealth; and eBusiness, eEducation and eLogistics.

Build and design multiple types of applications that are cross-language, platform, and cost-effective by understanding core Azure principles and foundational concepts Key Features Get familiar with the different design patterns available in Microsoft Azure Develop Azure cloud architecture and a pipeline management system Get to know the security best practices for your Azure deployment Book DescriptionThanks to its support for high availability, scalability, security, performance, and disaster recovery, Azure has been widely adopted to create and deploy different types of application with ease. Updated for the latest developments, this third edition of Azure for Architects helps you get to grips with the core concepts of designing serverless architecture, including containers, Kubernetes deployments, and big data solutions. You'll learn how to architect solutions such as serverless functions, you'll discover deployment patterns for containers and Kubernetes, and you'll explore large-scale big data processing using Spark and Databricks. As you advance, you'll implement DevOps using Azure DevOps, work with intelligent solutions using Azure Cognitive Services, and integrate security, high availability, and scalability into each solution. Finally, you'll delve into Azure security concepts such as OAuth, OpenConnect, and managed identities. By the end of this book, you'll have gained the confidence to design intelligent Azure solutions based on containers and serverless functions. What you will learn Understand the components of the Azure cloud platform Use cloud design patterns Use enterprise security guidelines for your Azure deployment Design and implement serverless and integration solutions Build efficient data solutions on Azure Understand container services on Azure Who this book is forIf you are a cloud architect, DevOps engineer, or a developer looking to learn about the key architectural aspects of the Azure cloud platform, this book is for you. A basic understanding of the Azure cloud platform will help you grasp the concepts covered in this book more effectively.

Learn essential techniques from data warehouse legend Bill Inmon on how to build the reporting environment your business needs now! Answers for many valuable business questions hide in text. How well can your existing reporting environment extract the necessary text from email, spreadsheets, and documents, and put it in a useful format for analytics and reporting? Transforming the traditional data warehouse into an efficient unstructured data warehouse requires additional skills from the analyst, architect, designer, and developer. This book will prepare you to successfully implement an unstructured data warehouse and, through clear explanations, examples, and case studies, you will learn new techniques and tips to successfully obtain and analyse text. Master these ten objectives: Build an unstructured data warehouse using the 11-step approach; Integrate text and describe it in terms of homogeneity, relevance, medium, volume, and structure; Overcome challenges including blather, the Tower of Babel, and lack of natural relationships; Avoid the Data Junkyard and combat the "Spiders Web"; Reuse techniques perfected in the traditional data warehouse and Data Warehouse 2.0 , including iterative development; Apply essential techniques for textual Extract, Transform, and Load (ETL) such as phrase recognition, stop word filtering, and synonym replacement; Design the Document Inventory system and link unstructured text to structured data; Leverage indexes for efficient text analysis and taxonomies for useful external categorisation; Manage large volumes of data using advanced techniques such as backward pointers; Evaluate technology choices suitable for unstructured data processing, such as data warehouse appliances.

Questions of privacy, borders, and nationhood are increasingly shaping the way we think about all things digital. Data Centers brings together essays and photographic documentation that analyze recent and ongoing developments. Taking Switzerland as an example, the book takes a look at the country's data centers, law firms, corporations, and government institutions that are involved in the creation, maintenance, and regulation of digital infrastructures. Beneath the official storyline- Switzerland's moderate climate, political stability, and relatively clean energy mix-the book uncovers a much more varied and sometimes contradictory set of narratives.

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. The definitive guide to dimensional design for your data warehouseLearn the best practices of dimensional design. Star Schema: The Complete Reference offers in-depth coverage of design principles and their underlying rationales. Organized around design concepts and illustrated with detailed examples, this is a step-by-step guidebook for beginners and a comprehensive resource for experts. This all-inclusive volume begins with dimensional design fundamentals and shows how they fit into diverse data warehouse architectures, including those of W.H. Inmon and Ralph Kimball. The book progresses through a series of advanced techniques that help you address real-world complexity, maximize performance, and adapt to the requirements of BI and ETL software products. You are furnished with design tasks and deliverables that can be incorporated into any project, regardless of architecture or methodology. Master the fundamentals of star schema design and slow change processing Identify situations that call for multiple stars or cubes Ensure compatibility across subject areas as your data warehouse grows Accommodate repeating attributes, recursive hierarchies, and poor data quality Support conflicting requirements for historic data Handle variation within a business process and correlation of disparate activities Boost performance using derived schemas and aggregates Learn when it's appropriate to adjust designs for BI and ETL tools

Big Data Imperatives, focuses on resolving the key questions on every one's mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications? Big data is emerging from the realm of one-off projects to mainstream business adoption; however the real value of big data is not in the overwhelming size of it, but more in its effective use. Your goal may be to obtain insight from voluminous data, with billions of loosely-structured bytes of data coming from different channels spread across different locations, which needs to be processed until the needle in the haystack is found.This book addresses the following big data characteristics: * Very large, distributed aggregations of loosely structured data -- often incomplete and inaccessible * Petabytes/Exabytes of data * Millions/billions of people providing/contributing to the context behind the data * Flat schema's with few complex interrelationships * Involves time-stamped events * Made up of incomplete data * Includes connections between data elements that must be probabilistically inferred Big data imperatives, explains 'what big data can do'. It can batch process millions and billions of records both unstructured and structured much faster and cheaper. Big data analytics provide a platform, to merge all analysis which enables data analysis to be more accurate, well-rounded, reliable and focused on a specific business capability. Big data imperatives, describes the complementary nature of traditional data warehouses and big-data analytics platforms and how they feed each other.This book aims to bring the big data and analytics realms together with a greater focus on architectures that leverage the scale and power of big data and the ability to integrate and apply analytics principles to data which earlier was not accessible. This book, can also be used as a handbook for practitioners; helping them on methodology, technical architecture, analytics techniques and best practices. At the same time, this book intends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data. What you'll learn * Understanding the technology, implementation of big data platforms and their usage for analytics * Big data architectures * Big data design patterns * Implementation best practices Who this book is for This book is designed for IT professionals, data warehousing, business intelligence professionals, data analysis professionals, architects, developers and business users

The two-volume set LNCS 6496 and 6497 constitutes the refereed proceedings of the 9th International Semantic Web Conference, ISWC 2010, held in Shanghai, China, during November 7-11, 2010. Part I contains 51 papers out of 578 submissions to the research track. Part II contains 18 papers out of 66 submissions to the semantic Web in-use track, 6 papers out of 26 submissions to the doctoral consortium track, and also 4 invited talks. Each submitted paper were carefully reviewed. The International Semantic Web Conferences (ISWC) constitute the major international venue where the latest research results and technical innovations on all aspects of the Semantic Web are presented. ISWC brings together researchers, practitioners, and users from the areas of artificial intelligence, databases, social networks, distributed computing, Web engineering, information systems, natural language processing, soft computing, and human computer interaction to discuss the major challenges and proposed solutions, the success stories and failures, as well the visions that can advance research and drive innovation in the Semantic Web.

Best practices and invaluable advice from world-renowned data warehouse experts

In this book, leading data warehouse experts from the Kimball Group share best practices for using the upcoming "Business Intelligence release" of SQL Server, referred to as SQL Server 2008 R2. In this new edition, the authors explain how SQL Server 2008 R2 provides a collection of powerful new tools that extend the power of its BI toolset to Excel and SharePoint users and they show how to use SQL Server to build a successful data warehouse that supports the business intelligence requirements that are common to most organizations. Covering the complete suite of data warehousing and BI tools that are part of SQL Server 2008 R2, as well as Microsoft Office, the authors walk you through a full project lifecycle, including design, development, deployment and maintenance.Features more than 50 percent new and revised material that covers the rich new feature set of the SQL Server 2008 R2 release, as well as the Office 2010 releaseIncludes brand new content that focuses on PowerPivot for Excel and SharePoint, Master Data Services, and discusses updated capabilities of SQL Server Analysis, Integration, and Reporting ServicesShares detailed case examples that clearly illustrate how to best apply the techniques described in the bookThe accompanying Web site contains all code samples as well as the sample database used throughout the case studies

"The Microsoft Data Warehouse Toolkit, Second Edition" provides you with the knowledge of how and when to use BI tools such as Analysis Services and Integration Services to accomplish your most essential data warehousing tasks.

Data warehousing and knowledge discovery are increasingly becoming mission-critical technologies for most organizations, both commercial and public, as it becomes incre- ingly important to derive important knowledge from both internal and external data sources. With the ever growing amount and complexity of the data and information available for decision making, the process of data integration, analysis, and knowledge discovery continues to meet new challenges, leading to a wealth of new and exciting research challenges within the area. Over the last decade, the International Conference on Data Warehousing and Knowledge Discovery (DaWaK) has established itself as one of the most important international scientific events within data warehousing and knowledge discovery. DaWaK brings together a wide range of researchers and practitioners working on these topics. The DaWaK conference series thus serves as a leading forum for discu- ing novel research results and experiences within data warehousing and knowledge th discovery. This year's conference, the 11 International Conference on Data Wa- housing and Knowledge Discovery (DaWaK 2009), continued the tradition by d- seminating and discussing innovative models, methods, algorithms, and solutions to the challenges faced by data warehousing and knowledge discovery technologies.

Business intelligence (BI) used to be so simple -- in theory anyway. Integrate and copy data from your transactional systems into a specialised relational database, apply BI reporting and query tools and add business users. Job done. No longer. Analytics, big data and an array of diverse technologies have changed everything. More importantly, business is insisting on ever more, ever faster from information and from IT in general. An emerging biz-tech ecosystem demands that business and IT work together. This book reflects the new reality that in todays socially complex and rapidly changing world, business decisions must be based on a combination of rational and intuitive thinking. Integrating cues from diverse information sources and tacit knowledge, decision makers create unique meaning to innovate heuristically at the speed of thought. This book provides a wealth of new models that business and IT can use together to design support systems for tomorrows successful organisations. Dr Barry Devlin, one of the earliest proponents of data warehousing, goes back to basics to explore how the modern trinity of information, process and people must be reinvented and restructured to deliver the value, insight and innovation required by modern businesses. From here, he develops a series of novel architectural models that provide a new foundation for holistic information use across the entire business. From discovery to analysis and from decision making to action taking, he defines a fully integrated, closed-loop business environment. Covering every aspect of business analytics, big data, collaborative working and more, this book takes over where BI ends to deliver the definitive framework for information use in the coming years.

Cutting-edge content and guidance from a data warehousing expert--now expanded to reflect field trends

Data warehousing has revolutionized the way businesses in a wide variety of industries perform analysis and make strategic decisions. Since the first edition of "Data Warehousing Fundamentals," numerous enterprises have implemented data warehouse systems and reaped enormous benefits. Many more are in the process of doing so. Now, this new, revised edition covers the essential fundamentals of data warehousing and business intelligence as well as significant recent trends in the field.

The author provides an enhanced, comprehensive overview of data warehousing together with in-depth explanations of critical issues in planning, design, deployment, and ongoing maintenance. IT professionals eager to get into the field will gain a clear understanding of techniques for data extraction from source systems, data cleansing, data transformations, data warehouse architecture and infrastructure, and the various methods for information delivery.

This practical "Second Edition" highlights the areas of data warehousing and business intelligence where high-impact technological progress has been made. Discussions on developments include data marts, real-time information delivery, data visualization, requirements gathering methods, multi-tier architecture, OLAP applications, Web clickstream analysis, data warehouse appliances, and data mining techniques. The book also contains review questions and exercises for each chapter, appropriate for self-study or classroom work, industry examples of real-world situations, and several appendices with valuable information.

Specifically written for professionals responsible for designing, implementing, or maintaining data warehousing systems, "Data Warehousing Fundamentals" presents agile, thorough, and systematic development principles for the IT professional and anyone working or researching in information management.

This book offers comprehensive coverage of information retrieval by considering both Text Based Information Retrieval (TBIR) and Content Based Image Retrieval (CBIR), together with new research topics. The approach to TBIR is based on creating a thesaurus, as well as event classification and detection. N-gram thesaurus generation for query refinement offers a new method for improving the precision of retrieval, while event classification and detection approaches aid in the classification and organization of information using web documents for domain-specific retrieval applications. In turn, with regard to content based image retrieval (CBIR) the book presents a histogram construction method, which is based on human visual perceptions of color. The book's overarching goal is to introduce readers to new ideas in an easy-to-follow manner.

Problemloesungen fur das Top-Management: Das Buch stellt speziell fur Entscheidungstrager die Nutzungsmoeglichkeiten von Data-Warahouse-Konzepten vor. Neben den Grundlagen werden vor allem die Einsatzgebiete, verfugbare Loesungen und praktische Erfahrungen beschrieben. Das Management speziell aus Konsumguterindustrie und -handel erhalt so die Moeglichkeit, fur das eigene Unternehmen die optimale Entscheidung zu treffen.

Wettbewerbsvorteile werden in Zukunft nur noch die Unternehmen erlangen, denen es gelingt, Informationen in Wissen zu verwandeln. Die zwei Welten Business Intelligence und Knowledge Management wachsen vor diesem Hintergrund zusammen. Der Herausgeber, Leiter des Instituts fur Managementinformationssysteme und des Instituts fur Knowledge Management, zeigt in diesem Buch die zunehmende Integration der beiden Bereiche. Das Buch bringt damit Transparenz in einen der groessten IT-Wachstumsmarkte. Mehrere Studien, etwa des Fraunhofer Instituts, beleuchten den relevanten Markt und geben wichtige Orientierungshilfen. Anhand einer Vielzahl von Beispielen wird gezeigt, welchen Nutzen der Einsatz hochentwickelter Analysewerkzeuge und die Entwicklung von Loesungen fur das Wissensmanagement heute bereits erbringen. Ebenfalls sehr hilfreich fur Praktiker ist die umfangreiche Anbieterliste. Einen raschen UEberblick uber die wichtigsten KM- und BI-Begriffe bietet ferner das integrierte Glossar.

Supercharge and deploy Amazon Redshift Serverless, train and deploy Machine learning Models using Amazon Redshift ML and run inference queries at scale. Key Features * Learn to build Multi-Class Classification Models * Create a model, validate a model and draw conclusion from K-means clustering * Learn to create a SageMaker endpoint and use that to create a Redshift ML Model for remote inference Book Description Amazon Redshift Serverless enables organizations to run PetaBytes scales Cloud data warehouses in minutes and in most cost effective way Developers, data analysts and BI analysts can deploy cloud data warehouses and use easy-to-use tools to train models and run predictions. Developers working with Amazon Redshift data warehouses will be able to put their SQL knowledge to work with this practical guide to train and deploy Machine Learning Models. The book provides a hands-on approach to implementation and associated methodologies that will have you up-and-running, and productive in no time. Complete with step-by-step explanations of essential concepts, practical examples and self-assessment questions, you will begin Deploying and Using Amazon Redshift Serverless and then dive into learning and deploying various types of Machine learning projects using familiar SQL Code. You will learn how to configure and deploy Amazon Redshift Serverless, understand the foundations of data analytics and types of data machine learning. Then you will deep dive into Redshift ML By the end of this book, you will be able to configure and deploy Amazon Redshift Serverless, train and deploy Machine learning Models using Amazon Redshift ML and run inference queries at scale. What you will learn * Learn how to implement an end-to-end serverless architecture for ingestion, analytics and machine learning using Redshift Serverless and Redshift ML * Learn how to create supervised and unsupervised models, and various techniques to influence your model * Learn how to run inference queries at scale in Redshift to solve a variety of business problems using models created with Redshift ML or natively in Amazon SageMaker * Learn how to optimize your Redshift data warehouse for extreme performance * Learn how to ensure you are using proper security guidelines with Redshift ML * Learn how to use model explainability in Amazon Redshift ML, to help understand how each attribute in your training data contributes to the predicted result. Who This Book Is For Data Scientists and Machine Learning developers who work with Amazon Redshift and want to explore it's machine learning capabilities will find this definitive guide helpful. Basic understanding of machine learning techniques and working knowledge of Amazon Redshift is needed to get the best from this book.

Most of modern enterprises, institutions, and organizations rely on knowledge-based management systems. In these systems, knowledge is gained from data analysis. Nowadays, knowledge-based management systems include data warehouses as their core components. The purpose of building a data warehouse is twofold. Firstly, to integrate multiple heterogeneous, autonomous, and distributed data sources within an enterprise. Secondly, to provide a platform for advanced, complex, and efficient data analysis. Data integrated in a data warehouse are analyzed by the so-called On-Line Analytical Processing (OLAP) applications designed among others for discovering trends, patterns of behavior, and anomalies as well as for finding dependencies between data. Massive amounts of integrated data and the complexity of integrated data that more and more often come from WEB-based, XML-based, spatio-temporal, object, and multimedia systems, make data integration and processing challenging.

The objective of NEW TRENDS IN DATA WAREHOUSING AND DATA ANALYSIS is fourfold: First, to bring together the most recent research and practical achievements in the DW and OLAP technologies. Second, to open and discuss new, just emerging areas of further development. Third, to provide the up-to-date bibliography of published works and the resource of research achievements for anyone interested in up-to-date data warehouse issues. And, finally, to assist in the dissemination of knowledge in the field of advanced DW and OLAP.

Learn data architecture essentials and prepare for the Salesforce Certified Data Architect exam with the help of tips and mock test questions Key Features * Leverage data modelling, Salesforce database design, and techniques for effective data design * Learn master data management, Salesforce data management, and how to include considerations * Get to grips with large data volumes, performance tuning, and poor performance mitigation techniques Book Description The Salesforce Data Architect is a prerequisite exam for the Application Architect half of the Salesforce Certified Technical Architect credential. This book offers a complete, up-to-date coverage of the Salesforce Data Architect exam so you can take it with confidence. The book is written in a clear, succinct way with self-assessment and practice exam questions, covering all topics necessary to help you pass the exam with ease. You'll understand the theory around Salesforce data modeling, database design, master data management (MDM), Salesforce data management (SDM), and data governance. Additionally, performance considerations associated with large data volumes will be covered. You'll also get to grips with data migration and understand the supporting theory needed to achieve Salesforce Data Architect certification. By the end of this Salesforce book, you'll have covered everything you need to pass the Salesforce Data Architect certification exam and have a handy, on-the-job desktop reference guide to re-visit the concepts. What you will learn * Understand the topics relevant to passing the Data Architect exam * Explore specialist areas such as large data volumes * Test your knowledge with the help of exam-like questions * Pick up useful tips and tricks that can be referred to time and again * Understand the reasons underlying the way Salesforce data management works * Discover the techniques that are available for loading massive amounts of data Who This Book Is For This book is for both aspiring Salesforce data architects and those already familiar with Salesforce data architecture who want to pass the exam and have a reference guide to revisit the material as part of their day-to-day job. Working knowledge of the Salesforce platform is assumed, alongside a clear understanding of Salesforce architectural concepts.

Im Mittelpunkt dieses anwendungsbezogenen Lehrbuchs stehen Architekturen, Methoden und Werkzeuge entscheidungsunterstutzender Systeme. Beispiele und Aufgaben ermoglichen die Entwicklung von Anwendungen mit der Demonstrationssoftware der CD ROM. Eine interaktive Foliensammlung veranschaulicht den Buchtext und verweist auf zusatzliches Lernmaterial. Der erste Teil stellt mit der Nutzwertanalyse (AHP) und Was-Wenn-Analysen traditionelle entscheidungsunterstutzende Ansatze dar und fuhrt anhand regelbasierter Systeme in wissensbasierte Systeme ein. Der zweite und dritte Teil behandeln das Schwerpunktthema Data Warehousing und Data Mining. Data Warehousing und OLAP bereiten die Inhalte von Produktionsdatenbanken fur Abfragen und Analysen durch Endbenutzer auf. Nach einem Uberblick uber die wichtigsten Data Mining-Verfahren konzentriert sich der dritte Teil auf zwei der verbreitesten Methoden, die Regelinduktion und neuronale Netze."

Das anhaltende Interesse an der Theorie monetArer Integration ist einerseits dem europAischen EinigungsprozeA zu verdanken, andererseits der InstabilitAt des WeltwAhrungssystems seit dem Zusammenbruch der Bretton-Woods-Vereinbarung. Die vorherrschende Theorie des optimalen WAhrungsraumes hat sich jedoch angesichts neuerer Entwicklungen in der Theorie der Wirtschaftspolitik sowie in der Theorie des Wechselkurses als zu eng und methodisch fragwA1/4rdig erwiesen. Das Buch gibt einen umfassenden, auch fA1/4r AngehArige anderer sozialwissenschaftlichen Disziplinen gut lesbaren Aoeberblick A1/4ber den Stand der Forschung zur monetAren Integration im allgemeinen und zur europAischen WAhrungsintegration im besonderen. Es gibt darA1/4ber hinaus Anregungen fA1/4r weiterfA1/4hrende Untersuchungen, z.B. zur Rolle der Arbeitsmarktverfassungen oder der Fiskal- und der Sozialpolitik.

Develop the must-have skills required for any data scientist to get the best results from Azure Databricks. Key Features * Learn to develop and productionize ML pipelines using the Databricks Unified Analytics platform * See how to use AutoML, Feature Stores, and MLOps with Databricks * Get a complete understanding of data governance and model deployment Book Description In this book, you'll get to grips with Databricks, enabling you to power-up your organization's data science applications. We'll walk through applying the Databricks AI and ML stack to real-world use cases for natural language processing, computer vision, time series data, and more. We'll dive deep into the complete model development life cycle for data ingestion and analysis, and get familiar with the latest offerings of AutoML, Feature Store, and MLStudio, on the Databricks platform. You'll get hands-on experience implementing repeatable ML operations (MLOps) pipeline using MLFlow, track model training and key metrics, and explore real-time ML, anomaly detection, and streaming analytics with Delta lake and Spark Structured Streaming. Starting with an overview of Data Science use cases across different organizations and industries, you will then be introduced to feature stores, feature tables, and how to access them. You will see why AutoML is important and how to create a baseline model with AutoML within Databricks. Utilizing the ML Flow model registry to manage model versioning and transition to production will be covered, along with detecting and protecting against model drift in production environments. By the end of the book, you will know how to set up your Databricks ML development and deployment as a CI/CD pipeline. What you will learn * Perform natural language processing, computer vision, and more * Explore AutoML, Feature Store, and MLStudio on Databricks * Dive deep into the complete model development life cycle * Experience implementing repeatable MLOps pipelines using MLFlow * Track model training and key metrics * Explore real-time ML, anomaly detection, and streaming analytics * Learn how to handle model drift Who This Book Is For In this book we are going to specifically focus on the tools catering to the Data Scientist persona. Readers who want to learn how to successfully build and deploy end-end Data Science projects using the Databricks cloud agnostic unified analytics platform will benefit from this book, along with AI and Machine Learning practitioners.