Major Commodity Producer Uses HPE Shadowbase to Enable Operational Analytics for Big Data

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Eight Billion Pounds of Produce

The growers of a major U.S. commodity deliver about eight billion pounds of produce per year to consumers. The produce is cultivated at thousands of independent farms throughout the country, and samples are used for quality analysis and control are delivered to one of ten regional classification centers run by a large U.S. government agency.

Precision Agriculture

Major commodity producers are shifting towards precision agriculture (also known as satellite agriculture), which takes the guesswork out of growing crops, shifting production from an art to a science. Precision agriculture is achieved through specialized technology including soil sensors, robotic drones, mobile apps, cloud computing and satellites, and leverages real-time data on the status of the crops, soil and air quality, weather conditions, etc.

Predictive Analytics

The commodity quality control system integrates applications to extract meaningful data from a mass of test and product quality data, or Big Data. Extraction is efficient thanks to a series of OLAP processors. Predictive analytics software uses this data to inform the producers about such variables as suggested water intake, crop rotation, and harvesting times. HPE Shadowbase software plays a key role in the system by integrating distributed applications through replication of data between the applications.

Case Study:
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HPE Shadowbase Software Enables Operational Analytics for Commodity Big Data