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008			220204s2021 sz | s |||| 0|eng d
020			_a9783030819798
024	7		_a10.1007/978-3-030-81979-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .D37 _b2022 EB
100	1		_aTaniar, David _4http://id.loc.gov/vocabulary/relators/aut _9683316
245	1	0	_aData Warehousing and Analytics : _bFueling the Data Engine _cby David Taniar, Wenny Rahayu
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publising _c2021
300			_a1 recurso en línea (XVIII, 435 páginas) _b381 ilustraciones, 262 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aData-Centric Systems and Applications _x2197-974X
505	0		_a1. Introduction -- Part I: Star Schema -- 2. Simple Star Schemas -- 3. Creating Facts and Dimensions: More Complex Processes -- Part II: Snowflake and Bridge Tables -- 4. Hierarchies -- 5. Bridge Tables -- 6. Temporal Data Warehousing -- Part III: Advanced Dimension -- 7. Determinant Dimensions -- 8. Junk Dimensions -- 9. Dimension Keys -- 10. One-Attribute Dimensions -- Part IV: Multi-Fact and Multi-Input -- 11. Multi-Fact Star Schemas -- 12. Slicing a Fact -- 13. Multi-Input Operational Databases -- Part V: Data Warehousing Granularity and Evolution -- 14. Data Warehousing Granularity and Levels of Aggregation -- 15. Designing Lowest-Level Star Schemas -- 16. Levels of Aggregation: Adding and Removing Dimensions -- 17. Levels of Aggregation and Bridge Tables -- 18. Active Data Warehousing -- Part VI: OLAP, Business Intelligence, and Data Analytics -- 19. Online Analytical Processing (OLAP) -- 20. Pre- and Post-Data Warehousing -- 21. Data Analytics for Data Warehousing.
520			_aThis textbook covers all central activities of data warehousing and analytics, including transformation, preparation, aggregation, integration, and analysis. It discusses the full spectrum of the journey of data from operational/transactional databases, to data warehouses and data analytics; as well as the role that data warehousing plays in the data processing lifecycle. It also explains in detail how data warehouses may be used by data engines, such as BI tools and analytics algorithms to produce reports, dashboards, patterns, and other useful information and knowledge. The book is divided into six parts, ranging from the basics of data warehouse design (Part I - Star Schema, Part II - Snowflake and Bridge Tables, Part III - Advanced Dimensions, and Part IV - Multi-Fact and Multi-Input), to more advanced data warehousing concepts (Part V - Data Warehousing and Evolution) and data analytics (Part VI - OLAP, BI, and Analytics). This textbook approaches data warehousing from the case study angle. Each chapter presents one or more case studies to thoroughly explain the concepts and has different levels of difficulty, hence learning is incremental. In addition, every chapter has also a section on further readings which give pointers and references to research papers related to the chapter. All these features make the book ideally suited for either introductory courses on data warehousing and data analytics, or even for self-studies by professionals. The book is accompanied by a web page that includes all the used datasets and codes as well as slides and solutions to exercises.
988			_aSpringer_Computer_2022
650		7	_2embne _9683315 _aAlmacenes de datos
700	1		_aRahayu, Wenny _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030819781
776	0	8	_iPrinted edition: _z9783030819804
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-81979-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2022 _dz _eh _zSI