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Big data imperatives : enterprise big data warehouse, BI implementations and analytics /
Big Data Imperatives, focuses on resolving the key questions on everyones mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications? Big d...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[Berkeley, California] :
Apress,
[2013]
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| Colección: | The expert's voice in big data
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Mohanty, Soumendra, |e author. | |
| 245 | 1 | 0 | |a Big data imperatives : |b enterprise big data warehouse, BI implementations and analytics / |c Soumendra Mohanty, Madhu Jagadeesh, Harsha Srivatsa. |
| 264 | 1 | |a [Berkeley, California] : |b Apress, |c [2013] | |
| 264 | 4 | |c ©2013 | |
| 300 | |a 1 online resource (xxii, 296 pages) : |b illustrations | ||
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| 490 | 0 | |a The expert's voice in big data | |
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed November 29, 2017). | |
| 505 | 0 | |a "Big Data" in the enterprise -- The new information management paradigm -- Big data implications for industry -- Emerging database landscape -- Application architectures for big data and analytics solutions -- Big data analytics methodology -- Extracting value from big data: in-memory solutions, real time analytics, and recommendation systems -- Data scientist. | |
| 520 | |a Big Data Imperatives, focuses on resolving the key questions on everyones mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications? Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its effective use. This book addresses the following big data characteristics: Very large, distributed aggregations of loosely structured data often incomplete and inaccessible Petabytes/Exabytes of data Millions/billions of people providing/contributing to the context behind the data Flat schema's with few complex interrelationships Involves time-stamped events Made up of incomplete data Includes connections between data elements that must be probabilistically inferred Big Data Imperativesexplains 'what big data can do'. It can batch process millions and billions of records both unstructured and structured much faster and cheaper. Big data analytics provide a platform to merge all analysis which enables data analysis to be more accurate, well-rounded, reliable and focused on a specific business capability. Big Data Imperativesdescribes the complementary nature of traditional data warehouses and big-data analytics platforms and how they feed each other. This book aims to bring the big data and analytics realms together with a greater focus on architectures that leverage the scale and power of big data and the ability to integrate and apply analytics principles to data which earlier was not accessible. This book can also be used as a handbook for practitioners; helping them on methodology, technical architecture, analytics techniques and best practices. At the same time, this book intends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 546 | |a English. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Business intelligence. | |
| 650 | 0 | |a Information technology. | |
| 650 | 0 | |a Electronic data processing. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Strategic planning. | |
| 650 | 2 | |a Data Mining | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 6 | |a Technologie de l'information. | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Planification stratégique. | |
| 650 | 7 | |a information technology. |2 aat | |
| 650 | 7 | |a COMPUTERS |x Databases |x Data Mining. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Databases |x Data Warehousing. |2 bisacsh | |
| 650 | 7 | |a Big data. |2 fast |0 (OCoLC)fst01892965 | |
| 650 | 7 | |a Business intelligence. |2 fast |0 (OCoLC)fst00842723 | |
| 650 | 7 | |a Data mining. |2 fast |0 (OCoLC)fst00887946 | |
| 650 | 7 | |a Electronic data processing. |2 fast |0 (OCoLC)fst00906956 | |
| 650 | 7 | |a Information technology. |2 fast |0 (OCoLC)fst00973089 | |
| 650 | 7 | |a Strategic planning. |2 fast |0 (OCoLC)fst01134371 | |
| 655 | 0 | |a Electronic books. | |
| 700 | 1 | |a Jagadeesh, Madhu, |e author. | |
| 700 | 1 | |a Srivatsa, Harsha, |e author. | |
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