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The leading edge of big data architectural practice is rapidly moving to flow-based computing using streaming architectures as opposed to state-based computing based on batch programs plus workflow schedulers. This transition is part of the larger movement towards micro-services and devOps oriented development and maintenance of large systems.
This fashion is spreading quickly, but the understanding of why flow-based computing is different from state-based computing and what this means is practice is lagging behind. In fact, there is a huge difference and this has the potential of massively simplifying big data systems, thereby improving reliability and time to market.
I will describe the necessary key concepts and illustrate them with practical examples. I will also describe why this matters in the real world.
KEYWORDS: Flow-based computing, streaming, best practices, optimization, use cases.
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North America. Please refer to the MapR Talks Directory for specific countries.
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