A Big Data Financial Information Management Architecture for Global Banking
Antoni Munar, Esteban Chiner, Ignacio Sales · 2014
Global investment banks and financial institutions are facing growing data processing demands. These originate not only from increasing regulatory requirements and an expanding variety and disparity of data sources, but also from ongoing pressures in cost reduction without compromising system scalability and flexibility. In this context, the ability to apply promising state-of-the-art big data technologies to extract the maximum value from the vast amounts of the data generated is generating a lot of interest in the financial services industry. In this paper we present a Big Data architecture system design, based in open distributed computing paradigms like Hadoop map-reduce, offering horizontal scalability and no-SQL flexibility while at the same time meeting the stringent quality and resilience requirements of the banking software standards. The proposed architecture is able to consolidate, validate, enrich and process with different Big Data analytics techniques the data gathered from the different source systems as encountered in the banking practice, while at the same time supporting the different data integration, transmission and process orchestration requirements traditionally encountered in a global financial institution.