Engineering Agile Big-Data Systems
To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, th...
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| Formato: | Libro |
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River Publishers
2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1941011&site=ehost-live header: @attributes: shortDbName: nlebk uiTerm: 1941011 longDbName: eBook Collection (EBSCOhost) uiTag: AN controlInfo: bkinfo: btl: Engineering Agile Big-Data Systems aug: au: Kevin Feeney Jim Davies James Welch sertl: River Publishers Series in Software Engineering isbn: 9788770220163 9788770220156 9781000792546 9781000795868 9781003338123 imageinfo: pubinfo: dt: @attributes: year: 2018 month: 01 day: 01 dtAvail: @attributes: year: 2019 month: 02 day: 05 pub: River Publishers pubContract: CRC Press (Unlimited) place: Aalborg price: 0.01 limitsGroup: maxCheckoutDays: 1500 copyPages: -1 pda: N printPagesOffline: 60 printPagesOnline: 60 previewPages: 10000 prePubGroup: dewey: @attributes: class: 004.21 item: 004 .21 lc: @attributes: class: QA76.9.S88 E54 2018 item: QA 76 .9 .S88 E54 2018 artinfo: ui: 1941011 1079008279 formats: fmt: – @attributes: type: EB doid: NL$1941011$PDF caption: PDF download: Y – @attributes: type: EK doid: NL$1941011$EPUB caption: EPUB download: Y tig: atl: Engineering Agile Big-Data Systems ptl: Engineering Agile Big-Data Systems aug: au: Kevin Feeney Jim Davies James Welch su: Big data System design Agile software development sug: subj: COMPUTERS / Software Development & Engineering / General COMPUTERS / Data Science / Data Analytics Big data System design Agile software development ab: To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems. pubtype: eBook doctype: Book ougenre: Book language: English copyright: @attributes: flag: N copyrightText: holdings: @attributes: islocal: N |
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