Data warehousing in the age of big data Krish Krishnan

By:

Krishan, Krish

Publication details: Amsterdam Morgan Kaufmann is an imprint of Elsevier 2013.Description: xxiii, 346 p. PaperISBN:

978-0-12-405891-0

Subject(s):

DDC classification:

005.74

Contents:

Contents: 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.

Summary: "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"-

Tags from this library:

must read (5) ,
worth reading (7)

Average rating: 0.0 (0 votes)

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Reference	Main Library	REFERENCE	005.71/ Kri/ 29995 (Browse shelf(Opens below))	Not For Loan		11129995

Total holds: 0

Contents: 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.

"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"-

There are no comments on this title.

to post a comment.

IES Management College And Research Centre

Data warehousing in the age of big data Krish Krishnan