Real-Time Data Analytics Saves Lives

May 28 2019

Scott Shadley

There was a time when a look to the sky was the only indicator of the day’s weather. Today, weather forecasts protect life and property. They are used in agriculture, by utility companies to prepare for demand surges and area outages, and by cities to prepare for the safety of residents and plan for road closures, mud slides, and potential traffic accidents. In the US we spend billions each year just on weather forecasting. The amount of data gathered and assimilated is astronomical.

And, when the weather patterns indicate imminent, potentially life-threatening weather, meteorologists use all their resources to focus primarily on that specific threat, to issue warnings, to track storm movement, to work with municipalities for quick and efficient response. Just last week, severe weather across the central and southern plains resulted in thunderstorms, lightning strikes, and nearly sixty reports of tornadoes.

Computational Storage is a means to manage data without moving all data at once, but instead sort and focus on the relevant data via in-situ processing. The amount of data moved to memory is a manageable sub-set of the entire dataset which increases processing speed. This saves time, power, infrastructure costs. But more importantly for weather forecasting, saving time may mean saving lives. When experts are looking at storms moving at record speed, real-time data analytics is a critical tool to provide warnings to people to take shelter, board up windows, and even to evacuate. Questions about Bringing Intelligence to Storage – contact us at NGD Systems to hear about our Newport Computational Storage Platform.

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