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Titlebook: Big Data Application Architecture Q&A; A Problem - Solution Nitin Sawant,Himanshu Shah Book 2013 Nitin Sawant and Himanshu Shah 2013

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發(fā)表于 2025-3-21 19:00:32 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Big Data Application Architecture Q&A
期刊簡稱A Problem - Solution
影響因子2023Nitin Sawant,Himanshu Shah
視頻videohttp://file.papertrans.cn/186/185629/185629.mp4
發(fā)行地址Use cases for big data specific to architectures like Walmart and Ebay..Covers the end-to-end application architecture required to realize the big data solution covering the analytics and visualizatio
圖書封面Titlebook: Big Data Application Architecture Q&A; A Problem - Solution Nitin Sawant,Himanshu Shah Book 2013 Nitin Sawant and Himanshu Shah 2013
影響因子.Big Data Application Architecture Pattern Recipes. provides an insight into heterogeneous infrastructures, databases, and visualization and analytics tools used for realizing the architectures of big data solutions. Its problem-solution approach helps in selecting the right architecture to solve the problem at hand. In the process of reading through these problems, you will learn harness the power of new big data opportunities which various enterprises use to attain real-time profits. .Big Data Application Architecture Pattern Recipes. answers one of the most critical questions of this time ‘how do you select the best end-to-end architecture to solve your big data problem?‘..The book deals with various mission critical problems encountered by solution architects, consultants, and software architects while dealing with the myriad options available for implementing a typical solution, trying to extract insight from huge volumes of data in real–time and across multiple relational and non-relational data types for clients from industries like retail, telecommunication, banking, and insurance. The patterns in this book provide the strong architectural foundation required to launch your
Pindex Book 2013
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發(fā)表于 2025-3-21 20:14:33 | 只看該作者
Big Data Application Architecture, customers and markets to gain a competitive advantage has become a very expensive proposition when using the traditional data analytics methods, which are based on structured relational databases. This dilemma applies not only to businesses, but to research organizations, governments, and education
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發(fā)表于 2025-3-22 00:27:04 | 只看該作者
Big Data Ingestion and Streaming Patterns,ca, and others had limited capabilities to handle and manage unstructured data such as text, media, video, and so forth, although they had a data type called CLOB and BLOB; which were used to store large amounts of text, and accessing data from these platforms was a problem. With the advent of multi
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Big Data Deployment Patterns,ling, and the inclusion of the cloud in some scenarios makes it more complex. This chapter illustrates deployment patterns you can use to deal with this complexity up front and align with the other patterns across various layers.
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發(fā)表于 2025-3-23 01:32:37 | 只看該作者
Big Data NFRs,itional data security and privacy concerns, virtualized environments add the challenges of a hybrid environment. Big data also introduces challenges of NoSQL databases, new distributed file systems, evolving ingestion mechanisms, and minimal authorization security provided by the Hadoop-like platfor
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Big Data Case Studies,t the solution of a given business problem, architects apply combinations of patterns across different layers of the entire application architecture as appropriate to the unique business requirements and priorities of the problem at hand. The following case studies exemplify how architects combine p
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