Data warehousing in the age of big data /
"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 discus...
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| Format: | |
| Language: | eng |
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Amsterdam : Morgan Kaufmann is an imprint of Elsevier,
2013
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| _version_ | 1826458149134008320 |
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| author | Krishnan, Krish, author |
| author_facet | Krishnan, Krish, author |
| author_sort | Krishnan, Krish, author |
| collection | OCEAN |
| description | "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"--provided by publisher |
| first_indexed | 2024-03-05T13:47:32Z |
| format | |
| id | KOHA-OAI-TEST:505779 |
| institution | Universiti Teknologi Malaysia - OCEAN |
| language | eng |
| last_indexed | 2024-03-05T13:47:32Z |
| publishDate | 2013 |
| publisher | Amsterdam : Morgan Kaufmann is an imprint of Elsevier, |
| record_format | dspace |
| spelling | KOHA-OAI-TEST:5057792020-12-19T17:18:44ZData warehousing in the age of big data / Krishnan, Krish, author Amsterdam : Morgan Kaufmann is an imprint of Elsevier,2013eng"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"--provided by publisherIncludes bibliographical references and index"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"--provided by publisherPSZJBLData warehousingBig dataURN:ISBN:9780124058910 (pbk.) |
| spellingShingle | Data warehousing Big data Krishnan, Krish, author Data warehousing in the age of big data / |
| title | Data warehousing in the age of big data / |
| title_full | Data warehousing in the age of big data / |
| title_fullStr | Data warehousing in the age of big data / |
| title_full_unstemmed | Data warehousing in the age of big data / |
| title_short | Data warehousing in the age of big data / |
| title_sort | data warehousing in the age of big data |
| topic | Data warehousing Big data |
| work_keys_str_mv | AT krishnankrishauthor datawarehousingintheageofbigdata |