Автор: Brackett Michael Название: Data Resource Design ISBN: 1935504339 ISBN-13(EAN): 9781935504337 Издательство: Gazelle Book Services Рейтинг: Цена: 10937.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Are you struggling with the formal design of your organisations data resource? Do you find yourself forced into generic data architectures and universal data models? Do you find yourself warping the business to fit a purchased application? Do you find yourself pushed into developing physical databases without formal logical design? Do you find disparate data throughout the organisation? If the answer to any of these questions is Yes, then you need to read Data Resource Design to help guide you through a formal design process that produces a high quality data resource within a single common data architecture. Most public and private sector organisations do not consistently follow a formal data resource design process that begins with the organisations perception of the business world, proceeds through logical data design, through physical data design, and into implementation. Most organisations charge ahead with physical database implementation, physical package implementation, and other brute-force-physical approaches. The result is a data resource that becomes disparate and does not fully support the organisation in its business endeavours. This book describes how to formally design an organisations data resource to meet its current and future business information demand. It builds on "Data Resource Simplexity", which described how to stop the burgeoning data disparity, and on "Data Resource Integration", which described how to understand and resolve an organisations disparate data resource. It describes the concepts, principles, and techniques for building a high quality data resource based on an organisations perception of the business world in which they operate. Like "Data Resource Simplexity" and "Data Resource Integration", Michael Brackett draws on five decades of data management experience building and managing data resources, and resolving disparate data in both public and private sector organisations. He leverages theories, concepts, principles, and techniques from a wide variety of disciplines, such as human dynamics, mathematics, physics, chemistry, philosophy, and biology, and applies them to properly designing data as a critical resource of an organisation. He shows how to understand the business environment where an organisation operates and design a data resource that supports the organisation in that business environment.
Описание: Organizations invest incredible amounts of time and money obtaining and then storing big data in data stores called data lakes. But how many of these organizations can actually get the data back out in a useable form? Very few can turn the data lake into an information gold mine. Most wind up with garbage dumps. Data Lake Architecture will explain how to build a useful data lake, where data scientists and data analysts can solve business challenges and identify new business opportunities. Learn how to structure data lakes as well as analog, application, and text-based data ponds to provide maximum business value. Understand the role of the raw data pond and when to use an archival data pond. Leverage the four key ingredients for data lake success: metadata, integration mapping, context, and metaprocess. Bill Inmon opened our eyes to the architecture and benefits of a data warehouse, and now he takes us to the next level of data lake architecture.
Автор: Biehl Название: Data Warehousing for Biomedical Informatics ISBN: 1482215217 ISBN-13(EAN): 9781482215212 Издательство: Taylor&Francis Рейтинг: Цена: 19906.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Data Warehousing for Biomedical Informatics is a step-by-step how-to guide for designing and building an enterprise-wide data warehouse across a biomedical or healthcare institution, using a four-iteration lifecycle and standardized design pattern. It enables you to quickly implement a fully-scalable generic data architecture that supports your organization's clinical, operational, administrative, financial, and research data. By following the guidelines in this book, you will be able to successfully progress through the Alpha, Beta, and Gamma versions, plus fully implement your first production release in about a year.
The Alpha version allows you to implement just enough of the basic design pattern to illustrate its core capabilities while loading a small sampling of limited data for demonstration purposes. This provides an easy way for everyone involved to visualize the new warehouse paradigm by actually examining a core subset of the working system. You can finish the Alpha version, also referred to as the proof-of-concept, in as little as 3-4 weeks.
The Beta version, which can be completed in about 2-3 months, adds required functionality and much more data. It allows you to get the full warehouse up and running quickly, in order to facilitate longer-term planning, user and support team training, and setup of the operational environment. The Gamma version, which is a fully-functional system-though still lacking data-can be implemented in about 3-4 months. About one year after starting, you will be ready to launch Release 1.0 as a complete and secure data warehouse.
Автор: Ralph Hughes Название: Agile Data Warehousing Project Management, ISBN: 0123964636 ISBN-13(EAN): 9780123964632 Издательство: Elsevier Science Рейтинг: Цена: 6230.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Offers an introduction to the method as you would practice it in the project room to build a data mart. This title helps to prepare you to join or lead a team in visualizing, building, and validating a single component to an enterprise data warehouse. It includes strategies for getting actionable requirements from a team`s business partner.
Автор: Jose Carlos Ramalho, Alberto Simoes, Ricardo Queiros Название: Innovations in XML Applications and Metadata Management: Advancing Technologies ISBN: 1466626690 ISBN-13(EAN): 9781466626690 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 28413.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: As new concepts such as virtualisation, cloud computing, and web applications continue to emerge, XML has begun to assume the role as the universal language for communication among contrasting systems that grow throughout the internet. <br><br><em>Innovations in XML Applications and Metadata Management: Advancing Technologies</em> addresses the functionality between XML and its related technologies towards application development based on previous concepts. This book aims to highlights the variety of purposes for XML applications and how the technology development brings together advancements in the virtual world.
Описание: Data science has a huge impact on how companies conduct business, and those who don`t learn about this revolutionaryfield could be left behind. You see, data science will help you make better decisions, know what products and services to release, and how to provide better service to your customers.
Автор: McKnight William, Dolezal Jake Название: Integrating Hadoop ISBN: 1634621522 ISBN-13(EAN): 9781634621526 Издательство: Gazelle Book Services Рейтинг: Цена: 5575.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Integrating Hadoop leverages the discipline of data integration and applies it to the Hadoop open-source software framework for storing data on clusters of commodity hardware. It is packed with the need-to-know for managers, architects, designers, and developers responsible for populating Hadoop in the enterprise, allowing you to harness big data and do it in such a way that the solution:
Complies with (and even extends) enterprise standards
Integrates seamlessly with the existing information infrastructure
Fills a critical role within enterprise architecture.
Integrating Hadoop covers the gamut of the setup, architecture and possibilities for Hadoop in the organization, including:
Supporting an enterprise information strategy
Organizing for a successful Hadoop rollout
Loading and extracting of data in Hadoop
Managing Hadoop data once it's in the cluster
Utilizing Spark, streaming data, and master data in Hadoop processes - examples are provided to reinforce concepts.
Автор: Taniar David, Rahayu Wenny Название: Emerging Perspectives in Big Data Warehousing ISBN: 1522555161 ISBN-13(EAN): 9781522555162 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 35897.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The concept of a big data warehouse appeared in order to store moving data objects and temporal data information. Moving objects are geometries that change their position and shape continuously over time. In order to support spatio-temporal data, a data model and associated query language is needed for supporting moving objects. Emerging Perspectives in Big Data Warehousing is an essential research publication that explores current innovative activities focusing on the integration between data warehousing and data mining with an emphasis on the applicability to real-world problems. Featuring a wide range of topics such as index structures, ontology, and user behavior, this book is ideally designed for IT consultants, researchers, professionals, computer scientists, academicians, and managers.
Описание: The final edition of the incomparable data warehousing and business intelligence reference, updated and expanded
The Kimball Group Reader, Remastered Collection is the essential reference for data warehouse and business intelligence design, packed with best practices, design tips, and valuable insight from industry pioneer Ralph Kimball and the Kimball Group. This Remastered Collection represents decades of expert advice and mentoring in data warehousing and business intelligence, and is the final work to be published by the Kimball Group. Organized for quick navigation and easy reference, this book contains nearly 20 years of experience on more than 300 topics, all fully up-to-date and expanded with 65 new articles. The discussion covers the complete data warehouse/business intelligence lifecycle, including project planning, requirements gathering, system architecture, dimensional modeling, ETL, and business intelligence analytics, with each group of articles prefaced by original commentaries explaining their role in the overall Kimball Group methodology.
Data warehousing/business intelligence industry's current multi-billion dollar value is due in no small part to the contributions of Ralph Kimball and the Kimball Group. Their publications are the standards on which the industry is built, and nearly all data warehouse hardware and software vendors have adopted their methods in one form or another. This book is a compendium of Kimball Group expertise, and an essential reference for anyone in the field.
Learn data warehousing and business intelligence from the field's pioneers
Get up to date on best practices and essential design tips
Gain valuable knowledge on every stage of the project lifecycle
Dig into the Kimball Group methodology with hands-on guidance
Ralph Kimball and the Kimball Group have continued to refine their methods and techniques based on thousands of hours of consulting and training. This Remastered Collection of The Kimball Group Reader represents their final body of knowledge, and is nothing less than a vital reference for anyone involved in the field.
Автор: Mukesh K. Mohania; A Min Tjoa Название: Data Warehousing and Knowledge Discovery ISBN: 3642037291 ISBN-13(EAN): 9783642037290 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 11th International Conference DaWaK 2009 Linz Austria August 31September 2 2009 Proceedings. .
As data holdings get bigger and questions get harder, data scientists and analysts must focus on the systems, the tools and techniques, and the disciplined process to get the correct answer, quickly Whether you work within industry or government, this book will provide you with a foundation to successfully and confidently process large amounts of quantitative data.
Here are just a dozen of the many questions answered within these pages:
What does quantitative analysis of a system really mean?
What is a system?
What are big data and analystics?
How do you know your numbers are good?
What will the future data science environment look like?
How do you determine data provenance?
How do you gather and process information, and then organize, store, and synthesize it?
How does an organization implement data analytics?
Do you really need to think like a Chief Information Officer?
What is the best way to protect data?
What makes a good dashboard?
What is the relationship between eating ice cream and getting attacked by a shark?
The nine chapters in this book are arranged in three parts that address systems concepts in general, tools and techniques, and future trend topics. Systems concepts include contrasting open and closed systems, performing data mining and big data analysis, and gauging data quality. Tools and techniques include analyzing both continuous and discrete data, applying probability basics, and practicing quantitative analysis such as descriptive and inferential statistics. Future trends include leveraging the Internet of Everything, modeling Artificial Intelligence, and establishing a Data Analytics Support Office (DASO).
Many examples are included that were generated using common software, such as Excel, Minitab, Tableau, SAS, and Crystal Ball. While words are good, examples can sometimes be a better teaching tool. For each example included, data files can be found on the companion website. Many of the data sets are tied to the global economy because they use data from shipping ports, air freight hubs, largest cities, and soccer teams. The appendices contain more detailed analysis including the 10 T's for Data Mining, Million Row Data Audit (MRDA) Processes, Analysis of Rainfall, and Simulation Models for Evaluating Traffic Flow.
Описание: This textbook gives an in-depth discussion of basic principles and practical techniques of data mining and data warehousing. Theoretical concepts are discussed in detail with the help of practical examples. It covers data mining tools and language such as Weka and R language.
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