Hadoop is great for analyzing data at rest. But what if your business problem requires the ability to analyze and respond in real-time and without a human in the loop. Getting faster, more relevant insight to take immediate action is really the name of the game with many big data industry use problems found in Telcos, Life Sciences, and Financial Services. The traditional ”store and analyze” approach typically used in data mining is not naturally designed for these problems where the temporal dimension differentiates real-time data from conventional big data. As a result, new technologies such as stream computing are needed that can continuously analyze data in motion to support real-time decision-making. To emphasis these points, we will discuss:
Dr. Nagui Halim’s technical vision and leadership launched the era of stream computing at IBM. In response to a client request in 2003 to create a new architecture for high-speed adaptive stream processing and analytics, Nagui recruited and assumed leadership of a large interdisciplinary research team, working in close and novel collaboration with the client, to undertake this formidable project to develop a new type of computing system able to manage and analyze massive volumes of continuous streams of data, which became known as System S. As the technical lead on System S, Nagui developed the foundational concepts and designed the architecture for this new computing system.
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