Suren is a serial entrepreneur and leader with over 21 years’ experience in large scale systems, cloud delivery models, business intelligence, big data analytics and, visualization. He currently leads the big data analytics and platform team at Synchronoss. Suren’s expertise is to solve problems by using technology and data analytics by organizing and implementing solutions with global teams for fortune 1000 companies. Suren’s passion is to evangelize and advocate use of new big data technologies to deliver advanced analytics (descriptive and predictive) to directly impact the business via an intuitive set of use cases. Prior to this role, Suren was the CTO at Razorsight (acquired by Synchronoss in 2015) a leading analytics provider for the telecommunications industry where he was responsible for overseeing the development of new products and frameworks to leverage analytics. Suren was also a founding partner at SingleTusk solutions where he was responsible for the overall telecom practice that served many large and small service providers with innovative cost, revenue and margin analytics solutions. Prior to that he was a co-founder and CTO of Step 9 Software, a venture backed OSS product company that offered order management and process centric solutions to the CLEC industry. Suren has extensive expertise in diverse disciplines that include Software Architecture and Design, Global Operations, Product Management, and Professional Services. He has a BS degree in Electrical Engineering from the Indian Institute of Science (IISc), Bangalore, and an MS degree in Biomedical Engineering from Johns Hopkins University, Baltimore.
Come to this keynote to learn how Synchronoss, a predictive analytics provider for the telecommunications industry, leverages Spark to build a data profiling application which serves as a critical component in their overall framework for data pipelining. You'll learn how the data profiler is enabling analysts to be more productive and agile and deliver faster time-to-value for their end clients. The application automatically identifies the data elements in the underlying data lake, identifies data types, creates meta data and statistics, and delivers this in an easily consumable web application.