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020 _a9783030819798
_9978-3-030-81979-8
024 7 _a10.1007/978-3-030-81979-8
_2doi
050 4 _aQA76.9.D3
072 7 _aUN
_2bicssc
072 7 _aCOM021000
_2bisacsh
072 7 _aUN
_2thema
082 0 4 _a005.74
_223
100 1 _aTaniar, David.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aData Warehousing and Analytics
_h[electronic resource] :
_bFueling the Data Engine /
_cby David Taniar, Wenny Rahayu.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXVIII, 635 p. 381 illus., 262 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aDatabase management.
650 0 _aBig data.
650 0 _aQuantitative research.
650 0 _aInformation storage and retrieval systems.
650 1 4 _aDatabase Management.
650 2 4 _aBig Data.
650 2 4 _aData Analysis and Big Data.
650 2 4 _aInformation Storage and Retrieval.
700 1 _aRahayu, Wenny.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030819781
776 0 8 _iPrinted edition:
_z9783030819804
830 0 _aData-Centric Systems and Applications,
_x2197-974X
856 4 0 _uhttps://doi.org/10.1007/978-3-030-81979-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c178958
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