Building Hybrid and Multi-Cloud Architectures for Analytics and AI

HAOYUAN LI | October 17, 2019

Building Hybrid and Multi-Cloud Architectures for Analytics and AI
We are in the early stage of the data revolution. Today’s conventional wisdom states that hybrid latency prevents you from running analytic and machine learning workloads in the cloud with the data on-prem. As a result, most companies either do not burst their workloads into the cloud or have to copy their data into a cloud environment and maintain that duplicate data—and even this can take days or weeks. Counter to conventional wisdom, you can create a high performance hybrid/multi-cloud data platform for analytics and machine learning by using data orchestration technology. Container orchestration technology (e.g. Kubernetes) enables containers to run in any environment agnostic to the hardware that is running the application, similarly data orchestration technology enables active working sets and data to be orchestrated on demand. This enables enterprises to create a compute agnostic, storage agnostic and cloud agnostic stack to serve all their data driven workloads, including analytics (e.g. Apache Spark, Presto, Apache Hive) and AI (e.g. Tensorflow, PyTorch).An enterprise hybrid cloud analytics strategy should burst compute that spans on-prem and cloud data stores. By bringing the hot data to the analytics and machine learning applications, the performance is the same as having the data co-located in the cloud. Also, the on-prem data stores will have offloaded the computation and minimized the additional I/O overhead.

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