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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

Data warehousing. --- Information warehousing --- Warehousing, Data --- Database management --- Management information systems --- Multidimensional databases

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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 a

Data warehousing. --- Big data. --- Data sets, Large --- Large data sets --- Data sets --- Information warehousing --- Warehousing, Data --- Database management --- Management information systems --- Multidimensional databases

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Data warehousing --- Engineering & Applied Sciences --- Computer Science --- Information warehousing --- Warehousing, Data --- Database management --- Management information systems --- Multidimensional databases --- Business policy --- Information systems --- data warehousing --- bedrijfsbeleid --- datawarehousing

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"This book should satisfy those who want a different perspective than the official Oracle documentation. It will cover all important aspects of a data warehouse while giving the necessary examples to make the reading a lively experience.? - Tim Donar, Author and Systems Architect for Enterprise Data WarehousesTuning a data warehouse database focuses on large transactions, mostly requiring what is known as throughput. Throughput is the passing of large amounts of information through a server, network and Internet environment, backwards and forwards, constantly! The ultim

Data warehousing. --- Information warehousing --- Warehousing, Data --- Database management --- Management information systems --- Multidimensional databases --- Oracle (Computer file) --- Oracle7.3 --- Oracle 7.3 --- Personal Oracle7 --- Personal Oracle 7

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Intranets (Computer networks) --- Data warehousing --- 005.758 --- Information warehousing --- Warehousing, Data --- Database management --- Management information systems --- Multidimensional databases --- Internal internets (Computer networks) --- Business enterprises --- Wide area networks (Computer networks) --- Computer networks --- MET Methods & Techniques --- computer networks --- computer science