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Big Data Imperatives, focuses on resolving the key questions on everyone’s 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 Imperatives describes 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 bookintends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Informazioni sull?autore
Soumendra Mohanty? is a Partner and leads Accenture?s Global Information Management Services practice. He is an expert within the Information Management area, focusing primarily on BI architectures, data warehouse, CRM/Customer Insight, MDM, Analytics and PCM solutions.? He is experienced in leading project teams through the lifecycle of a project, and has successfully helped sell and delivered BI and DW projects in multiple industries, including products, CPG, brokerage, banking, telecommunications, and retail.Soumendra has authored several books on data warehousing and Analytics and published numerous journals in DM Review. He has also presented in numerous international forums. His functional expertise ranges from Big Data Analytics, BI Architectures, Data Warehouse, CRM/Customer Insight, Supply Chain Analytics, Marketing Insights, & MDM.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
- EditoreApress
- Data di pubblicazione2013
- ISBN 10 1430248726
- ISBN 13 9781430248729
- RilegaturaCopertina flessibile
- LinguaInglese
- Numero edizione1
- Numero di pagine320
- Contatto del produttoreSpringer Nature Customer Service Center GmbH; ProductSafety@springernature.com
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Big Data Imperatives, focuses on resolving the key questions on everyone's 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 Imperatives explains '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 Imperatives describes 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. 320 pp. Englisch. Codice articolo 9781430248729
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Taschenbuch. Condizione: Neu. Neuware -Big Data Imperatives, focuses on resolving the key questions on everyone¿s 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 inaccessiblePetabytes/Exabytes of dataMillions/billions of people providing/contributing to the context behind the dataFlat schema's with few complex interrelationshipsInvolves time-stamped eventsMade up of incomplete dataIncludes connections between data elements that must be probabilistically inferredBig 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 bookintends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 320 pp. Englisch. Codice articolo 9781430248729
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Big Data Imperatives, focuses on resolving the key questions on everyone's 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 Imperatives explains '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 Imperatives describes 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 bookintends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data. Codice articolo 9781430248729
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