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Apache Hadoop is emerging as a distributed platform for handling large and fast incoming streams of data. Predictive maintenance, supply chain optimization, and internet-of-things analysis are examples where Hadoop provides the scalable storage, processing, and analytics platform to gain meaningful insights from granular data that is typically only valuable from a large-scale, aggregate view. One architecture useful for capturing and analyzing streaming data is the Lambda Architecture, representing a model of how to analyze real-time and historical data together.
This presentation will cover the practice of capturing canonical data “as it lands” as a baseline for accommodating future analytics requirements.
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North America. Please refer to theMapR Talks Directory for specific countries.
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