標(biāo)題: Titlebook: Big Data Application Architecture Q&A; A Problem - Solution Nitin Sawant,Himanshu Shah Book 2013 Nitin Sawant and Himanshu Shah 2013 [打印本頁(yè)] 作者: Stimulant 時(shí)間: 2025-3-21 19:00
書目名稱Big Data Application Architecture Q&A影響因子(影響力)
書目名稱Big Data Application Architecture Q&A影響因子(影響力)學(xué)科排名
書目名稱Big Data Application Architecture Q&A網(wǎng)絡(luò)公開度
書目名稱Big Data Application Architecture Q&A網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Big Data Application Architecture Q&A被引頻次
書目名稱Big Data Application Architecture Q&A被引頻次學(xué)科排名
書目名稱Big Data Application Architecture Q&A年度引用
書目名稱Big Data Application Architecture Q&A年度引用學(xué)科排名
書目名稱Big Data Application Architecture Q&A讀者反饋
書目名稱Big Data Application Architecture Q&A讀者反饋學(xué)科排名
作者: ASSET 時(shí)間: 2025-3-21 20:14
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作者: EVICT 時(shí)間: 2025-3-22 00:27
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作者: A保存的 時(shí)間: 2025-3-22 07:26 作者: atopic 時(shí)間: 2025-3-22 12:47 作者: Ganglion-Cyst 時(shí)間: 2025-3-22 15:20 作者: 草率女 時(shí)間: 2025-3-22 18:14 作者: GULLY 時(shí)間: 2025-3-22 21:28
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.作者: 有害 時(shí)間: 2025-3-23 01:32
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作者: Pcos971 時(shí)間: 2025-3-23 05:51
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作者: Hypomania 時(shí)間: 2025-3-23 10:47
Resources, References, and Tools,cs and NoSQL databases, while some have a . ecosystem with a combination of infrastructure, visualization, and analytical capabilities. This chapter gives a broad overview of many of the products you will need to implement the architecture and patterns described in this book.作者: Dorsal 時(shí)間: 2025-3-23 16:35
Book 2013 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 ente作者: 領(lǐng)導(dǎo)權(quán) 時(shí)間: 2025-3-23 18:33 作者: 知道 時(shí)間: 2025-3-24 00:57 作者: convert 時(shí)間: 2025-3-24 04:47 作者: gait-cycle 時(shí)間: 2025-3-24 08:37 作者: 流眼淚 時(shí)間: 2025-3-24 14:39 作者: 文字 時(shí)間: 2025-3-24 16:27
Big Data Application Architecture,h are based on structured relational databases. This dilemma applies not only to businesses, but to research organizations, governments, and educational institutions that need less expensive computing and storage power to analyze complex scenarios and models involving images, video, and other data, as well as textual data.作者: 谷類 時(shí)間: 2025-3-24 21:25 作者: expeditious 時(shí)間: 2025-3-25 02:09
Big Data Visualization Patterns,alytics churns out. This chapter introduces commercial visualization tools in the big data domain, the use-cases that utilize the power of the Internet to derive meaningful insights, the marriage of machine-learning and visualization, and observations about how it is changing the landscape for business analysts and data scientists.作者: LOPE 時(shí)間: 2025-3-25 05:55 作者: detach 時(shí)間: 2025-3-25 09:01
Big Data Ingestion and Streaming Patterns,structured (a.k.a. .) data in the form of social media and audio/video, there has to be a change in the way data is ingested, preprocessed, validated, and/or cleansed and integrated or co-related with nontextual formats. This chapter deals with the following topics:作者: ERUPT 時(shí)間: 2025-3-25 13:49
Data Discovery and Analysis Patterns,port it. Big data analysis methods are used to analyze social media interactions, bank transactions for fraud patterns, customer sentiments for online product purchases, etc. Let’s look at some patterns that may help discover and analyze this unstructured data.作者: TOM 時(shí)間: 2025-3-25 16:10
Introduction to Analytic Number Theorystructured (a.k.a. .) data in the form of social media and audio/video, there has to be a change in the way data is ingested, preprocessed, validated, and/or cleansed and integrated or co-related with nontextual formats. This chapter deals with the following topics:作者: 吞吞吐吐 時(shí)間: 2025-3-25 20:06 作者: 出處 時(shí)間: 2025-3-26 01:47
The Fundamental Theorem of Arithmetic,al as other chapters. The idea is to make sure you have a basic foundation for learning about big data. Other chapters will provide depth of coverage that we hope you will find useful no matter what your background. So let’s get started.作者: nocturia 時(shí)間: 2025-3-26 06:38 作者: senile-dementia 時(shí)間: 2025-3-26 08:57
Introduction to Analytic Number Theoryca, 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作者: Charade 時(shí)間: 2025-3-26 15:21 作者: ALIEN 時(shí)間: 2025-3-26 16:48 作者: 粗鄙的人 時(shí)間: 2025-3-26 21:50
Geometry of Classical Mechanics,ly related to text analytics, natural language processing. Areas like video and image analytics are still evolving. Big data analysis attempts to interpret and find insightful patterns in the customer behavior that perhaps the sales force already had some idea about, but did not have the data to sup作者: 天賦 時(shí)間: 2025-3-27 01:43
M. Isaacson,M. Ohtsuki,M. Utlaut of sample data to a full-fledged analysis of all the data residing within the enterprise and also the “sentiments” that the data from social media analytics churns out. This chapter introduces commercial visualization tools in the big data domain, the use-cases that utilize the power of the Interne作者: Trypsin 時(shí)間: 2025-3-27 08:58 作者: affect 時(shí)間: 2025-3-27 10:39 作者: 無(wú)瑕疵 時(shí)間: 2025-3-27 16:48
https://doi.org/10.1007/978-981-97-2484-0t 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作者: Foment 時(shí)間: 2025-3-27 21:36
Offset Curved Translating Follower,cs and NoSQL databases, while some have a . ecosystem with a combination of infrastructure, visualization, and analytical capabilities. This chapter gives a broad overview of many of the products you will need to implement the architecture and patterns described in this book.作者: llibretto 時(shí)間: 2025-3-27 22:00
Big Data Introduction,al as other chapters. The idea is to make sure you have a basic foundation for learning about big data. Other chapters will provide depth of coverage that we hope you will find useful no matter what your background. So let’s get started.作者: 終止 時(shí)間: 2025-3-28 05:24
Big Data Storage Patterns,. Enterprises can leverage their existing infrastructure and storage licenses in addition to these new solutions for big data. In this chapter, we will cover the various storage mechanisms available, as well as patterns that amalgamate existing application storage frameworks with new big data implementations.作者: 裂隙 時(shí)間: 2025-3-28 06:54 作者: 從容 時(shí)間: 2025-3-28 13:19
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 patterns to solve particular business problems.作者: gratify 時(shí)間: 2025-3-28 18:17
Resources, References, and Tools,cs and NoSQL databases, while some have a . ecosystem with a combination of infrastructure, visualization, and analytical capabilities. This chapter gives a broad overview of many of the products you will need to implement the architecture and patterns described in this book.作者: 被告 時(shí)間: 2025-3-28 20:48
978-1-4302-6292-3Nitin Sawant and Himanshu Shah 2013作者: Asymptomatic 時(shí)間: 2025-3-28 23:48
The Fundamental Theorem of Arithmetic,al as other chapters. The idea is to make sure you have a basic foundation for learning about big data. Other chapters will provide depth of coverage that we hope you will find useful no matter what your background. So let’s get started.作者: 脫落 時(shí)間: 2025-3-29 06:03
https://doi.org/10.1007/978-3-642-46124-8. Enterprises can leverage their existing infrastructure and storage licenses in addition to these new solutions for big data. In this chapter, we will cover the various storage mechanisms available, as well as patterns that amalgamate existing application storage frameworks with new big data implementations.
