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Compelling business needs increasingly drives the adoption of the next-generation stream processing & analytics solutions for real-time insights into day-to-day operations. Amongst the several solutions in the market, Apache Flink has emerged as the clear choice for streaming architectures to do low-latency processing of high-volume streaming data. Attend this webinar, led by Dinesh Chandrasekhar and Simon Elliston Ball, to hear them talk about:

  • What is Flink - why do you need it 

  • How Flink handles a high volume of data and addresses low latency challenges

  • How Flink fits into your end-to-end streaming architecture

  • Review practical use cases and when it works the best


Director Product Marketing

Dinesh Chandrasekhar


Dinesh Chandrasekhar is a technology evangelist, a thought leader and a seasoned product marketer with over 24+ years of industry experience. He has an impressive track record of taking new integration/mobile/IoT/Big Data products to market with a clear GTM strategy of pre-and-post launch activities. Dinesh has extensive experience working on enterprise software as well as SaaS products delivering sophisticated solutions for customers with complex architectures. As a Lean Six Sigma Green Belt, he has been the champion for Digital Transformation at companies like Software AG, CA Technologies and IBM. Dinesh’s areas of expertise include IoT, Application/Data integration, BPM, Analytics, B2B, API management, Microservices, and Mobility. He is proficient in use cases across multiple industry verticals like retail, manufacturing, utilities, and healthcare. He is a prolific speaker, blogger, and a weekend coder. He currently works at Cloudera, managing their Data-in-Motion product line. He is fascinated by new technology trends including blockchain and deep learning. Dinesh holds an MBA degree from Santa Clara University and a Master’s degree in Computer Applications from the University of Madras.

Sr. Director, Product Management

Simon Elliston Ball


Simon is the product manager for cybersecurity solutions at Cloudera. His background is in applying data science to streaming data sources, helping people make sense of big data and streaming data building tools to help developers work with data. In the past, he has worked in the data intensive worlds of hedge funds, retail software vendors and large scale network and compute infrastructure management. All this comes together into a data science driven approach to cybersecurity and a realistic approach to machine learning. He lives in Cambridge, where he mainly debates locals in pubs about machine learning and board games.

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