Abstract
In modern analytics deployments, latency is the fatal flaw that limits the efficacy of the overall system. Solutions move at the speed of decision, and microseconds could mean the difference between success and failure against competitive offerings. Artificial Intelligence, Machine Learning, and In-Memory Analytics solutions have significantly reduced latency, but the sheer volume of data and its potential broad distribution across the globe prevents a single analytics node from efficiently harvesting and processing data.This panel discussion will feature industry experts discussing the different approaches to distributed analytics in the network and storage nodes. How does the storage providers of HDDs and SSD view the data creation and movement between the edge compute and the cloud? And how can computational storage be a solution to reduce data movement?