Leveraging large-scale analytics in banking with Hadoop has been saved
Leveraging large-scale analytics in banking with Hadoop
Part of the For Cloud Professionals podcast series
Financial services companies have to meet complex regulatory requirements and satisfy customer demands–all with aging platforms. However, moving to the cloud, and using tools like Hadoop, enable them to perform the large-scale analytics needed to satisfy multiple stakeholder demands.
Leveraging Hadoop to power large-scale analytics in banking
Financial services companies face regulatory requirements on one hand, and ever-increasing customer demands on the other–all while operating with legacy platforms that often can't handle the workloads efficiently. However, banks that have embraced the cloud have found success, especially when they use tools like Hadoop that enable them to leverage large-scale analytics and AI tools such as machine learning and natural language processing. Use cases like fraud prevention, gaining a holistic view of customers and sentiment, and compliance and risk management have shown Hadoop's value. Join David Linthicum and guest, Deloitte's Sandra Bauer, as they discuss how Hadoop, especially when it's integrated with DevOps, has transformed analytics in banking and helped banks scale to meet regulatory requirements and grow their business.
Apurva is an experienced engineering executive with a combination of technology delivery experience, integration and management of distributed teams including overseas teams. He currently serves as Director of Cloud Engineering at Google. Apurva leads the Dataproc, Composer, and CDAP products in the data analytics team. Prior to Google, Apurva was at Lenovo/Motorola leading their mobile cloud team. Apurva also spent time at Pivotal Software (EMC) where he built and commercialized Pivotal's Hadoop distribution and at Yahoo where he led various search and display advertising efforts as well as the Hadoop solutions team.
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