本文選題：大數(shù)據(jù) + 數(shù)據(jù)遷移��； 參考：《哈爾濱理工大學(xué)學(xué)報》2015年04期

【摘要】：針對目前國內(nèi)銀行的OLAP(Online Analytical Processing)技術(shù)滯后于海量數(shù)據(jù)處理需求的情況,研究了海量數(shù)據(jù)存儲、處理的關(guān)鍵技術(shù),設(shè)計并實(shí)現(xiàn)了用Hadoop平臺和關(guān)系型數(shù)據(jù)庫架構(gòu)的銀行海量數(shù)據(jù)處理系統(tǒng).介紹了系統(tǒng)架構(gòu)、文件存儲層次設(shè)計、數(shù)據(jù)遷移方案、任務(wù)調(diào)度算法等關(guān)鍵技術(shù)的實(shí)現(xiàn).通過實(shí)驗(yàn)驗(yàn)證,系統(tǒng)能夠?qū)崿F(xiàn)數(shù)據(jù)的高可靠性與數(shù)據(jù)的高可用性目標(biāo),既能滿足業(yè)務(wù)數(shù)據(jù)實(shí)時處理的需求,也能滿足海量數(shù)據(jù)統(tǒng)計分析的需求.
[Abstract]:In view of the fact that the OLAP (online Analytical processing) technology of domestic banks lags behind the demand for massive data processing, the key technologies of mass data storage and processing are studied. A bank mass data processing system based on Hadoop platform and relational database architecture is designed and implemented. The system architecture, file storage hierarchy design, data migration scheme, task scheduling algorithm and other key technologies are introduced. The experimental results show that the system can achieve the goal of high reliability and high availability of data, which can not only meet the requirements of real-time processing of business data, but also meet the needs of statistical analysis of massive data.
【作者單位】： 哈爾濱金融學(xué)院網(wǎng)絡(luò)信息中心;
【基金】：黑龍江省科技攻關(guān)項目(GC12A307)
【分類號】：TP311.13;TP333

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本文編號：2071985