IoTillustration

2016 is the year that we’ve finally entered the era of the Internet of Things (IoT). Since the beginning of this year, I’ve seen and heard more and more customers and industry leaders discuss technologies that can store, process, and analyze large amounts of real-time streaming data from sensors and IoT devices.

Organizations within the Industrial IoT especially are seeking new IoT technologies to solve their technical challenges and add significant business value. Industries, such as manufacturing, logistics, telecommunications, and oil and gas, have been successfully building IoT applications for configuration management, predictive maintenance, supply chain optimization, and many other critical use cases.

Even though the “Internet of Things” seems like the latest buzzword, many organizations have already been leveraging massive amounts of sensor data to improve decision-making and gain competitive advantage.

Objectivity has been the leader in graph analytics and real-time relationship discovery for several decades. With many years of experience developing and advancing our technologies, we were proud last fall to launch ThingSpan, the only highly distributed graph analytics platform that integrates with open technologies, such as Apache Spark, HDFS, YARN and Kafka. Data is obviously the world’s most important asset, but even more powerful are the links between that data, and ThingSpan enables businesses to connect the dots at enormous scale.

Next Thursday, April 28 at 10:30am PT, Brian Clark, VP of Products at Objectivity, and Jason Stamper, Analyst of Data Management and Analytics at 451 Research, will be discussing some of the key technical challenges within the Industrial Internet of Things in the webinar: “Optimize Your Infrastructure with Open Technologies in the Industrial IoT.” Click the link to reserve your seat today, and learn more about us at www.objectivity.com.

We look forward to seeing you next Thursday!

 

 

Ann Shih

Marketing Coordinator

Ann Shih - Marketing Coordinator

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