000			02789nam a22002297a 4500
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020			_a9781484284612
082			_a005.74 _bMOH
100			_aMohanty, Soumendra _914211
245			_aBig data imperatives: _benterprise big data warehouse, BI implementations and analytics
260			_bApress _aCalifornia _c2023
300			_axxii, 296 p.
365			_aINR _b999.00
520			_aBig 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. (https://link.springer.com/book/10.1007/978-1-4302-4873-6#about-this-book)
650			_aBig data _913179
650			_aAdministrative data processing, _915612
650			_aBig data analytics _915613
700			_aJagadeesh, Madhu _915614
700			_aSrivatsa, Harsha _915615
942			_cBK _2ddc
999			_c5951 _d5951