ICONICS announces the integration and support of its Hyper Historian™ data historian with Microsoft Azure Data Lake for more data storage, archiving and retrieval.
When real-time “hot” data is collected at the edge by IoT devices and other remote collectors, it can then be securely transmitted to “warm” data historians for mid-term archiving and replay. Hyper Historian now features the ability to archive to “cold” long-term data storage systems such as data lakes, Hadoop or Azure HD Insight. These innovations help to make the best use of historical data at any stage in the process for further analysis and for use with machine learning.
“This is the next critical evolution of high-speed plant data historians,” said Russ Agrusa, President and CEO of ICONICS. “With the ability to successfully archive real-time Big Data from sources such as the Internet of Things (IoT) and remote data collectors, our customers can now rapidly recall that information for use with multiple analytical solutions, such as ICONICS Smart Energy AnalytiX® and our suite of AnalytiX products, Microsoft’s Cortana Analytics Suite, Microsoft Power BI, and Azure Machine Learning as well as others.”
ICONICS Hyper Historian is a high speed, reliable and robust industrial plant historian solution designed for mission critical applications. ICONICS’ exciting new cloud-enabled technology makes Hyper Historian one of the first industrial plant historians available on the cloud. It is now more scalable than ever, as clients can utilize Microsoft Corp.’s cloud application platform to access their big data from any desktop, web browser or mobile device. This feature reduces IT costs with simple setup and minimal maintenance requirements, allowing customers to infinitely grow their application based on the changing needs of their business.
This new hot, warm, and cold storage capability is at the core of ICONICS’ suite of analytics solutions. Its new Smart Energy AnalytiX and Smart Alarm AnalytiX products can ingest this big data from multiple IIoT and remote collectors to provide predictive analytics.
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