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Building upon his earlier book that detailed agile data warehousing programming techniques for the Scrum master, Ralph's latest work;
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Data Modeling for Agile Data Warehouse using Data Vault Modeling Approach. Includes Enterprise Data Warehouse Architecture. This is a;
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You have to make sense of enormous amounts of data, and while the notion of "agile data warehousing" might sound tricky, it can yield;
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director for the BigQuery team, provide best practices for modern data warehousing within an autoscaled, serverless public cloud. Whether you;
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Using Agile methods, you can bring far greater innovation, value, and quality to any data warehousing (DW), business intelligence (BI), or;
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describes BEAM, an agile approach to dimensional modeling, for improving communication between data warehouse designers, BI stakeholders and the;
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Since the first edition of Data Warehousing Fundamentals, there has been an increase in enterprises either already having or in the process;
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suppliers of hardware, databases and tools. Data Warehousing in the Real World provides comprehensive guidelines and techniques for the delivery;
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, it is the only one on the market that provides a sophisticated, directly executable, i.e., copy-paste-run, bitemporal generic data warehouse;
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of this data in a system that allows for rapid retrieval of customized information, is the solution. This book, written by a data warehousing;
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data warehousing (DW), and very often appoint a single senior-level individual to serve as the Enterprise BI/DW Program Manager. This book;
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of these systems depends on the ability to construct, maintain, and extract information from data warehouses. While many still perceive data;
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, the process of retrieval and knowledge discovery from this huge amount of heterogeneous complex data forms the litmus test for research in the;
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The concept of a big data warehouse appeared in order to store moving data objects and temporal data information. Moving objects are;
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suitable for data mining and analytics. The Analytical Puzzle describes an unbiased, practical, and comprehensive approach to building a data;
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Delivers real-world solutions for the most time- and labor-intensive portion of data warehousing-data staging, or the extract, transform, load (ETL;
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Data Warehousing ist seit einigen Jahren in vielen Branchen ein zentrales Thema. Die anfangliche Euphorie tauschte jedoch daruber hinweg;
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, the International Conference on Data Warehousing and Knowledge Discovery (DaWaK) has established itself as one of the most important;
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This book constitutes the refereed proceedings of the 13th International Conference on Data Warehousing and Knowledge Discovery, DaWak 2011;
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of undergraduate students of computer science, engineering and information technology for a course on data mining and data warehousing. The text simplifies;
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, and new architecture options, workloads, and integration techniques for Big Data and the data warehouse. Part 3 deals with data governance;
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, catalogued, transformed and used for data mining, market research, decision support and online analytical processing. The ways to analyze or retrieve;
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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;
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significant extension to the robust OLAP functionality of SQL 7.0 and provide a complete, end-to-end platform for analysis. Features include: data;
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Business Intelligence and Data Warehousing deals with the main components of a data warehouse for business intelligence applications;
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Intermediate-Advanced user level;...
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for writing this book was the need to build an organized framework for E Governance using Data Mining-a challenging task, owing to the;
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