Morgan Stanley is driven by a relentless pursuit to improve operational efficiency and customer value with deeper insights from big data. A few years ago we started using Hadoop to mature our big data solution and scale our big data analytics projects.
Our team implemented an architecture for a big data design pattern common to a variety of big data problem classes. The architecture needed to support our big data deployments by leveraging commodity hardware, software, and resource skills to cost-effectively scale. To increase developer productivity on Hadoop using readily available skills and meet our corporate governance requirements Morgan Stanley selected a commercial data integration platform for Hadoop that also fit into the rest of our existing infrastructure.
In this session you’ll learn how Morgan Stanley matured their big data solution to adapt to technology trends, leverage crowd innovation, and tackle the challenges associated with big data overload and complexity.
This session is sponsored by Informatica
Michael Dobrovolsky is the Lead Architect of Enterprise Big Data Solutions and Advanced Analytics Services for the Wealth Management business at Morgan Stanley.
Michael brings a wealth of experience in the field of banking and capital markets, data mining, NLP, text mining advanced business analytics and technology strategy.
At his previous job as the Senior Principle and Thought leader in the business intelligence consulting services of the Capital Markets division at Hewlett-Packard, he established a reputation for developing innovative, advanced and strategic solutions in the Financial Services industry.
Michael worked at the capacity of a Strategic Advisor to CTO and Chief Architect for Fortune 500 and Major Information Provider Companies advancing Information Management framework and discipline into realm of new cutting edge technologies.
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