Business efficiency is a result of incremental improvements in data management; one recent improvement is the ability to make decisions in real time when events occur. Companies need applications that offer a low-latency processing to impact events as they happen. A good example is fraud detection, where the ability to analyze data and make decision in real time allows companies to capture fraudulent activities before large losses occur.
Stream-based technologies allow big data applications to deal with low-latency decisions and provide a more agile way to develop and deploy applications. Drawing on concrete examples from retail, financial services, and telecommunications, Tugdual Grall details the various elements of a stream-based application and outlines the key capabilities of modern messaging layers like Apache Kafka and MapR Streams.
This session is sponsored by MapR Technologies.
Tugdual Grall is an open source advocate, a passionate developer, and a chief technical evangelist EMEA at MapR, where he works to ease MapR, Hadoop, and NoSQL adoption within European developer communities. Before joining MapR, Tug was a technical evangelist at MongoDB and Couchbase. Tug has also worked as CTO at eXo Platform and JavaEE product manager and software engineer at Oracle. Tugdual is a cofounder of the Nantes JUG (Java user group) and also writes a blog.
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