000			03533cam a2200337 i 4500
001			17695534
003			OSt
005			20210930193256.0
008			130412s2013 ne a b 001 0 eng
010			_a 2013004151
020			_a9780124058910 (pbk.)
040			_aDLC _beng _cDLC _erda _dDLC
042			_apcc
050	0	0	_aQA76.9.D37 _bK75 2013
082	0	0	_a005.74/5 _223
100	1		_aKrishnan, Krish.
245	1	0	_aData warehousing in the age of big data / _cKrish Krishnan.
264		1	_aAmsterdam : _bMorgan Kaufmann is an imprint of Elsevier, _c2013.
300			_axxiii, 346 pages : _billustrations ; _c24 cm
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
504			_aIncludes bibliographical references and index.
505	8		_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 .
520			_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.
650		0	_aData warehousing.
650		0	_aBig data.
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2lcc _cBK
999			_c3355 _d3355