LEADER 04265nam 2200613 a 450 001 9910458863903321 005 20200520144314.0 010 $a0-12-405920-1 035 $a(CKB)2660000000011126 035 $a(EBL)1191051 035 $a(OCoLC)843860813 035 $a(SSID)ssj0000872952 035 $a(PQKBManifestationID)11479192 035 $a(PQKBTitleCode)TC0000872952 035 $a(PQKBWorkID)10851202 035 $a(PQKB)10618172 035 $a(MiAaPQ)EBC1191051 035 $a(CaSebORM)9780124058910 035 $a(PPN)174662815 035 $a(Au-PeEL)EBL1191051 035 $a(CaPaEBR)ebr10698608 035 $a(CaONFJC)MIL490441 035 $a(EXLCZ)992660000000011126 100 $a20130412d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aData warehousing in the age of big data$b[electronic resource] /$fKrish Krishnan 205 $a1st edition 210 $aAmsterdam $cMorgan Kaufmann$d2013 215 $a1 online resource (371 p.) 225 1 $aThe Morgan Kaufmann Series on Business Intelligence 300 $aDescription based upon print version of record. 311 $a0-12-405891-4 320 $aIncludes bibliographical references and index. 327 $aMachine generated contents note: Part 1 - Big Data Chapter 1 - Introduction to Big Data Chapter 2 - Complexity of Big Data Chapter 3 - Big Data Processing Architectures Chapter 4 - Big Data Technologies Chapter 5 - Big Data Business Value Part 2 - The Data Warehouse Chapter 6 - Data Warehouse Chapter 7 - Re-Engineering the Data Warehouse Chapter 8 -Workload Management in the Data Warehouse Chapter 9 - New Technology Approaches Part 3 - Extending Big Data into the Data Warehouse Chapter 10 - Integration of Big Data and Data Warehouse Chapter 11 - Data Driven Architecture Chapter 12 - Information Management and Lifecycle Chapter 13 - Big Data Analytics, Visualization and Data Scientist Chapter 14 - Implementing The "Big Data" Data Warehouse Appendix A - Customer Case Studies From Vendors Appendix B - Building The HealthCare Information Factory. 330 $a"In conclusion as you come to the end of this book, the concept of a Data Warehouse and its primary goal of serving the enterprise version of truth, and being the single platform for all the source of information will continue to remain intact and valid for many years to come. As we have discussed across many chapters and in many case studies, the limitations that existed with the infrastructures to create, manage and deploy Data Warehouses have been largely eliminated with the availability of Big Data technologies and infrastructure platforms, making the goal of the single version of truth a feasible reality. Integrating and extending Big Data into the Data Warehouse, and creating a larger decision support platform will benefit businesses for years to come. This book has touched upon governance and information lifecycle management aspects of Big Data in the larger program, however you can reuse all the current program management techniques that you follow for the Data Warehouse for this program and even implement agile approaches to integrating and managing data in the Data Warehouse. Technologies will continue to evolve in this spectrum and there will be more additions of solutions, which can be integrated if you follow the modular integration approaches to building and managing the Data Warehouse. The Appendix sections contain many more case studies and a special section on Healthcare Information Factory based on Big Data approaches. These are more guiding posts to help you align your thoughts and goals to building and integrating Big Data in your Data Warehouse"--$cProvided by publisher. 410 0$aMorgan Kaufmann Series on Business Intelligence 606 $aData warehousing 606 $aBig data 608 $aElectronic books. 615 0$aData warehousing. 615 0$aBig data. 676 $a005.74/5 700 $aKrishnan$b Krish$0856917 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910458863903321 996 $aData warehousing in the age of big data$91913697 997 $aUNINA