Data warehouse systems design and implementation / [electronic resource] :

• 作者： Vaisman, Alejandro.
• 其他作者：
  • Zimanyi, Esteban.
  • SpringerLink (Online service)
• 其他題名：
  • Data-centric systems and applications.
• 出版： Berlin, Heidelberg : Springer Berlin Heidelberg :Imprint: Springer
• 版本：Second edition.
• 叢書名： Data-centric systems and applications,
• 主題： Data warehousing. , Database Management. , Information Storage and Retrieval. , Business IT Infrastructure. , Computer Application in Administrative Data Processing.
• ISBN： 9783662651674 (electronic bk.) 、 9783662651667 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Part I: Fundamental Concepts -- 1. Introduction -- 2. Database Concepts -- 3. Data Warehouse Concepts -- 4. Conceptual Data Warehouse Design -- 5. Logical Data Warehouse Design -- 6. Data Analysis in Data Warehouses -- 7. Data Analysis in the Northwind Data Warehouse -- Part II: Implementation and Deployment -- 9. Physical Data Warehouse Design -- 9. Extraction, Transformation, and Loading -- 10. A Method for Data Warehouse Design -- Part III: Advanced Topics -- 11. Temporal and Multiversion Data Warehouses -- 12. Spatial and Mobility Data Warehouses -- 13. Graph Data Warehouses -- 14. Semantic Web Data Warehouses -- 15. Recent Developments in Big Data Warehouses.
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• 摘要註: With this textbook, Vaisman and Zima?nyi deliver excellent coverage of data warehousing and business intelligence technologies ranging from the most basic principles to recent findings and applications. To this end, their work is structured into three parts. Part I describes "Fundamental Concepts" including conceptual and logical data warehouse design, as well as querying using MDX, DAX and SQL/OLAP. This part also covers data analytics using Power BI and Analysis Services. Part II details "Implementation and Deployment," including physical design, ETL and data warehouse design methodologies. Part III covers "Advanced Topics" and it is almost completely new in this second edition. This part includes chapters with an in-depth coverage of temporal, spatial, and mobility data warehousing. Graph data warehouses are also covered in detail using Neo4j. The last chapter extensively studies big data management and the usage of Hadoop, Spark, distributed, in-memory, columnar, NoSQL and NewSQL database systems, and data lakes in the context of analytical data processing. As a key characteristic of the book, most of the topics are presented and illustrated using application tools. Specifically, a case study based on the well-known Northwind database illustrates how the concepts presented in the book can be implemented using Microsoft Analysis Services and Power BI. All chapters have been revised and updated to the latest versions of the software tools used. KPIs and Dashboards are now also developed using DAX and Power BI, and the chapter on ETL has been expanded with the implementation of ETL processes in PostgreSQL. Review questions and exercises complement each chapter to support comprehensive student learning. Supplemental material to assist instructors using this book as a course text is available online and includes electronic versions of the figures, solutions to all exercises, and a set of slides accompanying each chapter. Overall, students, practitioners and resear
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