Big data architectures like Hadoop and Spark solve the distributed database problem well but have as an article of faith that moving compute closer to data is important for performance. They also assume your code is written in Java or another JVM-based language like Scala.
The big problem? Data science, predictive analytics, and ML don’t happen in JVM-based languages. They happen in Python, R, and to a lesser extent C/C++. Secondly, today’s data center networks have 1,000x the bandwidth at a lower total cost versus 2005, when Hadoop was first conceived, meaning that data locality doesn’t matter so much. Lastly, all the major players—AWS, Microsoft, Google, IBM, Red Hat, and Docker—are lined up behind Kubernetes. Containers and Kubernetes make great language-agnostic distributed computing clusters.
Mathew Lodge demonstrates that it’s just as easy to deploy Python as it is Java, walking you through doing cloud-native data science with Anaconda, Docker, and Kubernetes. Welcome to the future.
This session is sponsored by Anaconda.
Mathew Lodge is senior vice president of product and marketing at Anaconda. Mathew has well over 20 years’ diverse experience in cloud computing and product leadership. Previously, he was chief operating officer at container and microservices networking and management startup Weaveworks; vice president of VMware’s Cloud Services Group and cofounder of what became VMware’s vCloud Air IaaS service; and senior director of Symantec’s $1B+ Information Management Group. Early in his career, Mathew built compilers and distributed systems for projects like the International Space Station, helped connect six countries to the internet for the first time, managed a $630M router product line at Cisco, and attempted to do SDN 10 years too early at CPlane.
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