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003 OCoLC;

005 20210629035348.0;

006 m o d ;

007 cr unu||||||||;

008 210114s2020 ncua obm 000 0 eng d;

035 a| (Sirsi) o1231501380;

035 a| (OCoLC)1231501380;

049 a| EREE;

090 a| QA76.9.B45;

100 1 a| Clark, William F.,e| author.=| ^A148430;

245 10 a| Software engineering for real-time NoSQL systems-centric big data analytics /c| by William F. Clark.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2020.;

300 a| 63 pages :b| color illustrations;

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 1.934 MB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2020.;

500 a| Presented to the Faculty of the Department of Computer Science;

500 a| Advisor: Venkat Gudivada;

500 a| Title from PDF t.p. (June 24, 2021).;

520 3 a| Recent advances in Big Data Analytics (BDA) have stimulated widespread interest to integrate BDA capabilities into all aspects of a business. Before these advances, companies have spent time optimizing the software development process and best practices associated with application development. These processes include project management structures and how to deliver new features of an application to its customers efficiently. While these processes are significant for application development, they cannot be utilized effectively for the software development of Big Data Analytics. Instead, some practices and technologies enable automation and monitoring across the full lifecycle of productivity from design to deployment and operations of Analytics. This paper builds on those practices and technologies and introduces a highly scalable framework for Big Data Analytics development operations. This framework builds on top of the best-known processes associated with DevOps. These best practices are then shown using a NoSQL cloud-based platform that consumes and processes structured and unstructured real-time data. As a result, the framework produces scalable, timely, and accurate analytics in real-time, which can be easily adjusted or enhanced to meet the needs of a business and its customers.;

504 a| Includes bibliographical references.;

650 0 a| Big data.=| ^A1157100;

650 0 a| Computer softwarex| Development.=| ^A210127;

650 0 a| Machine learning.=| ^A145211;

650 0 a| Cloud computing.=| ^A1012332;

650 0 a| Industrial management.=| ^A6347;

653 a| Big Data Analytics;

653 a| DevOps;

653 a| DataOps;

653 a| NoSQL;

653 a| Containers;

700 1 a| Gudivada, Venkat N.e| degree supervisor.=| ^A1308613;

710 2 a| East Carolina University.b| Department of Computer Science.?| UNAUTHORIZED;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/8810;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 5547848;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5547848-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 1/14/2021x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5547848-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 1/14/2021x| ETDz| HERESOURCE;

Software engineering for real-time NoSQL systems-centric big data analytics / by William F. Clark.

Author/creator	Clark, William F. author.
Other author	Gudivada, Venkat N. degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2020.
Description	63 pages : color illustrations
Supplemental Content	Access via ScholarShip
Subjects	Big data. Computer software--Development. Machine learning. Cloud computing. Industrial management.

Summary	Recent advances in Big Data Analytics (BDA) have stimulated widespread interest to integrate BDA capabilities into all aspects of a business. Before these advances, companies have spent time optimizing the software development process and best practices associated with application development. These processes include project management structures and how to deliver new features of an application to its customers efficiently. While these processes are significant for application development, they cannot be utilized effectively for the software development of Big Data Analytics. Instead, some practices and technologies enable automation and monitoring across the full lifecycle of productivity from design to deployment and operations of Analytics. This paper builds on those practices and technologies and introduces a highly scalable framework for Big Data Analytics development operations. This framework builds on top of the best-known processes associated with DevOps. These best practices are then shown using a NoSQL cloud-based platform that consumes and processes structured and unstructured real-time data. As a result, the framework produces scalable, timely, and accurate analytics in real-time, which can be easily adjusted or enhanced to meet the needs of a business and its customers.
General note	Presented to the Faculty of the Department of Computer Science
General note	Advisor: Venkat Gudivada
General note	Title from PDF t.p. (June 24, 2021).
Dissertation note	M.S. East Carolina University 2020.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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Software engineering for real-time NoSQL systems-centric big data analytics / by William F. Clark.

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