| 期刊全稱(chēng) | Big Data Imperatives | | 期刊簡(jiǎn)稱(chēng) | Enterprise Big Data | | 影響因子2023 | Soumendra Mohanty,Madhu Jagadeesh,Harsha Srivatsa | | 視頻video | http://file.papertrans.cn/186/185643/185643.mp4 | | 發(fā)行地址 | Vendors and platforms agnostic there by bringing in deep understanding of key areas viz.,.Big data platforms.Implementation best practices, etc;.Numerous industry use cases about big data and its impl | | 圖書(shū)封面 |  | | 影響因子 | .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 w | | Pindex | Book 2013 |
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