The Data Vault was invented by Dan Linstedt at the U.S. Department of Defense, and the standard has been successfully applied to data warehousing projects at organizations of different sizes, from small to large-size corporations. Due to its simplified design, which is adapted from nature, the Data Vault 2.0 standard helps prevent typical data warehousing failures. "Building a Scalable Data Warehouse" covers everything one needs to know to create a scalable data warehouse end to end, including a presentation of the Data Vault modeling technique, which provides the foundations to create a technical data warehouse layer. The book discusses how to build the data warehouse incrementally using the agile Data Vault 2.0 methodology. In addition, readers will learn how to create the input layer (the stage layer) and the presentation layer (data mart) of the Data Vault 2.0 architecture including implementation best practices. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Dan Linstedt and Michael Olschimke discuss: How to load each layer using SQL Server Integration Services (SSIS), including automation of the Data Vault loading processes. Important data warehouse technologies and practices. Data Quality Services (DQS) and Master Data Services (MDS) in the context of the Data Vault architecture. Provides a complete introduction to data warehousing, applications, and the business context so readers can get-up and running fast Explains theoretical concepts and provides hands-on instruction on how to build and implement a data warehouse Demystifies data vault modeling with beginning, intermediate, and advanced techniques Discusses the advantages of the data vault approach over other techniques, also including the latest updates to Data Vault 2.0 and multiple improvements to Data Vault 1.0
Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can’t be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You’ll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big Data Understand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using it Shows how to turn textual information into a form that can be analyzed by standard tools Explains how Big Data fits within an existing systems environment Presents new opportunities that are afforded by the advent of Big Data Demystifies the murky waters of repetitive and non-repetitive data in Big Data
Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together. New case studies include expanded coverage of textual management and analytics New chapters on visualization and big data Discussion of new visualizations of the end-state architecture
The Data Vault was invented by Dan Linstedt at the U.S. Department of Defense, and the standard has been successfully applied to data warehousing projects at organizations of different sizes, from small to large-size corporations. Due to its simplified design, which is adapted from nature, the Data Vault 2.0 standard helps prevent typical data warehousing failures. "Building a Scalable Data Warehouse" covers everything one needs to know to create a scalable data warehouse end to end, including a presentation of the Data Vault modeling technique, which provides the foundations to create a technical data warehouse layer. The book discusses how to build the data warehouse incrementally using the agile Data Vault 2.0 methodology. In addition, readers will learn how to create the input layer (the stage layer) and the presentation layer (data mart) of the Data Vault 2.0 architecture including implementation best practices. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Dan Linstedt and Michael Olschimke discuss: How to load each layer using SQL Server Integration Services (SSIS), including automation of the Data Vault loading processes. Important data warehouse technologies and practices. Data Quality Services (DQS) and Master Data Services (MDS) in the context of the Data Vault architecture. Provides a complete introduction to data warehousing, applications, and the business context so readers can get-up and running fast Explains theoretical concepts and provides hands-on instruction on how to build and implement a data warehouse Demystifies data vault modeling with beginning, intermediate, and advanced techniques Discusses the advantages of the data vault approach over other techniques, also including the latest updates to Data Vault 2.0 and multiple improvements to Data Vault 1.0
Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can’t be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You’ll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big Data Understand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using it Shows how to turn textual information into a form that can be analyzed by standard tools Explains how Big Data fits within an existing systems environment Presents new opportunities that are afforded by the advent of Big Data Demystifies the murky waters of repetitive and non-repetitive data in Big Data
Chronicles grassroots efforts to recover, rebuild, and enjoy architecturally iconic but economically obsolete places in the American Rust Belt. A pioneering Detroit automobile factory. A legendary iron mill at the edge of Pittsburgh. A campus of concrete grain elevators in Buffalo. Two monumental train stations, one in Buffalo, the other in Detroit. These once-noble sites have since fallen from their towering grace. As local elected leaders did everything they could to destroy what was left of these places, citizens saw beauty and utility in these industrial ruins and felt compelled to act. Postindustrial DIY tells their stories. The culmination of more than a dozen years of on-the-ground investigation, ethnography, and historical analysis, author and urbanist Daniel Campo immerses the reader in this postindustrial landscape, weaving the perspectives of dozens of DIY protagonists as well as architects, planners, and preservationists. Working without capital, expertise, and sometimes permission in a milieu dominated by powerful political and economic interests, these do-it-yourself actors are driven by passion and a sense of civic duty rather than by profit or political expediency. They have craftily remade these sites into collective preservation projects and democratic grounds for arts and culture, environmental engagement, regional celebrations, itinerant play, and in-the-moment constructions. Their projects are generating excitement about the prospect of Rust Belt life, even as they often remain invisible to the uninformed passerby and fall short of professional preservation or environmental reclamation standards. Demonstrating that there is no such thing as a site that is “too far gone” to save or reuse, Postindustrial DIY is rich with case studies that demonstrate how great architecture is not simply for the elites or the wealthy. The citizen preservationists and urbanists described in this book offer looser, more playful, and often more publicly satisfying alternatives to the development practices that have transformed iconic sites into expensive real estate or a clean slate for the next profitable endeavor. Transcending the disciplinary boundaries of architecture, historic preservation, city planning, and landscape architecture, Postindustrial DIY suggests new ways to engage, adapt, and preserve architecturally compelling sites and bottom-up strategies for Rust Belt revival.
The Comprehensive Sourcebook of Bacterial Protein Toxins, Fourth Edition, contains chapters written by internationally known and well-respected specialists. This book contains chapters devoted to individual toxins, as well as chapters that consider the different applications of these toxins. Considerable progress has been made in understanding the structure, function, interaction and trafficking into cells, as well as mechanism of action of toxins. Bacterial toxins are involved in the pathogenesis of many bacteria, some of which are responsible for severe diseases in human and animals, but can also be used as tools in cell biology to dissect cellular processes or used as therapeutic agents. Novel recombinant toxins are already proposed in the treatment of some diseases, as well as new vaccines. Alternatively, certain toxins are also considered as biological weapons or bioterrorism threats. Given the multifaceted aspects of toxin research and the multidisciplinary approaches adopted, toxins are of great interest in many scientific areas from microbiology, virology, cell biology to biochemistry and protein structure. This new edition is written with a multidisciplinary audience in mind and contains 5 new chapters that reflect the latest research in this area. Other chapters have been combined, deleted and fully revised as necessary to deliver relevant and valuable content. Descriptions of relevant toxins as well as representative toxins of the main bacterial toxin families to allow for a better comparison between them Focused chapters on toxin applications and common properties or general features of toxins
Presents arguments for and against the existence of five notable cryptids and challenges the pseudoscience that furthers their legendary statuses, while providing an exploration of the nature and subculture of cryptozoology.
Metabolic inhibitors and receptor antagonists are indispensable tools for the molecular life scientist. By blocking specific enzymes or receptor-mediated signal transduction cascades, they simplify the analysis of complex cellular processes especially when it is essential to demonstrate that a process of interest is functionally linked to a particular enzyme or receptor. From antibiotics to statins, modern medicine relies on the reliability and ease-of-use of enzyme- and receptor-directed inhibitors and antagonists.The Inhibitor Index is a comprehensive, curated compendium of over 7,800 enzyme inhibitors and receptor antagonists, including many toxins, poisons, and metabolic uncouplers.
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.