%0 Book %1 MohantyJagadeeshSrivatsa13 %A Mohanty, Soumendra %A Jagadeesh, Madhu %A Srivatsa, Harsha %C Berkeley, CA %D 2013 %I Apress %K 01841 103 springer book ai enterprise application data pattern recognition analysis processing optimize information retrieval zzz.big %R 10.1007/978-1-4302-4873-6 %T Big Data Imperatives: Enterprise Big Data Warehouse, BI Implementations and Analytics %X 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 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. %@ 978-1-4302-4872-9

@book{MohantyJagadeeshSrivatsa13, abstract = {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 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.}, added-at = {2016-11-29T07:48:07.000+0100}, address = {Berkeley, CA}, author = {Mohanty, Soumendra and Jagadeesh, Madhu and Srivatsa, Harsha}, biburl = {https://www.bibsonomy.org/bibtex/2732e871ba5c89a9ba6ac223103bb9cc9/flint63}, doi = {10.1007/978-1-4302-4873-6}, file = {SpringerLink:2013/MohantyJagadeeshSrivatsa13.pdf:PDF;Springer Product page:http\://www.springer.com/978-1-4302-4872-9:URL;Amazon Search inside:http\://www.amazon.de/gp/reader/1430248726/:URL;Springer for Professionals:http\://www.springerprofessional.de/978-1-4302-4873-6---big-data-imperatives/4637430.html:URL}, groups = {public}, interhash = {0a06c9941755b8981ea845a3d7f8da1a}, intrahash = {732e871ba5c89a9ba6ac223103bb9cc9}, isbn = {978-1-4302-4872-9}, keywords = {01841 103 springer book ai enterprise application data pattern recognition analysis processing optimize information retrieval zzz.big}, publisher = {Apress}, timestamp = {2018-04-16T12:07:24.000+0200}, title = {Big Data Imperatives: Enterprise Big Data Warehouse, BI Implementations and Analytics}, username = {flint63}, year = 2013 }