LEADER 04746nam 22006735 450 001 9910544870603321 005 20251202170707.0 010 $a3-030-81979-5 024 7 $a10.1007/978-3-030-81979-8 035 $a(MiAaPQ)EBC6881910 035 $a(Au-PeEL)EBL6881910 035 $a(CKB)21069159700041 035 $a(PPN)260826154 035 $a(BIP)83147381 035 $a(BIP)80709236 035 $a(DE-He213)978-3-030-81979-8 035 $a(EXLCZ)9921069159700041 100 $a20220204d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData Warehousing and Analytics $eFueling the Data Engine /$fby David Taniar, Wenny Rahayu 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (642 pages) 225 1 $aData-Centric Systems and Applications,$x2197-974X 311 08$aPrint version: Taniar, David Data Warehousing and Analytics Cham : Springer International Publishing AG,c2022 9783030819781 327 $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. 330 $aThis textbook covers all central activities of data warehousing and analytics, including transformation, preparation, aggregation, integration, and analysis. It discusses the full spectrum of the journey of data from operational/transactional databases, to data warehouses and data analytics; as well as the role that data warehousing plays in the data processing lifecycle. It also explains in detail how data warehouses may be used by data engines, such as BI tools and analytics algorithms to produce reports, dashboards, patterns, and other useful information and knowledge. The book is divided into six parts, ranging from the basics of data warehouse design (Part I - Star Schema, Part II - Snowflake and Bridge Tables, Part III - Advanced Dimensions, and Part IV - Multi-Fact and Multi-Input), to more advanced data warehousing concepts (Part V - Data Warehousing and Evolution) and data analytics (Part VI - OLAP, BI, and Analytics). This textbook approaches data warehousing from the case study angle. Each chapter presents one or more case studies to thoroughly explain the concepts and has different levels of difficulty, hence learning is incremental. In addition, every chapter has also a section on further readings which give pointers and references to research papers related to the chapter. All these features make the book ideally suited for either introductory courses on data warehousing and data analytics, or even for self-studies by professionals. The book is accompanied by a web page that includes all the used datasets and codes as well as slides and solutions to exercises. 410 0$aData-Centric Systems and Applications,$x2197-974X 606 $aDatabase management 606 $aBig data 606 $aQuantitative research 606 $aInformation storage and retrieval systems 606 $aDatabase Management 606 $aBig Data 606 $aData Analysis and Big Data 606 $aInformation Storage and Retrieval 615 0$aDatabase management. 615 0$aBig data. 615 0$aQuantitative research. 615 0$aInformation storage and retrieval systems. 615 14$aDatabase Management. 615 24$aBig Data. 615 24$aData Analysis and Big Data. 615 24$aInformation Storage and Retrieval. 676 $a005.745 676 $a005.745 700 $aTaniar$b David$0969353 702 $aRahayu$b Johanna Wenny 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910544870603321 996 $aData Warehousing and Analytics$92762711 997 $aUNINA