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Mtell, MapR Release Mtell Reservoir

Freedom Industrial Hydraulics

Mtelligence Corp. (Mtell) and MapR Technologies Inc. have released a big data platform, called Mtell Reservoir, which uses the MapR Distribution, including Hadoop, Mtell Previse Software, and Open time-series database software technology.

The system ingests and analyzes real-time sensor and historical data alongside maintenance data that are generated from industrial equipment for oil rigs. Designed specifically for data center user needs, Mtell Reservoir is an enterprise historian that distributes disk access and CPU processing across MapR clusters of computers to provide orders of magnitude improvements over contemporary plant historians. The solution has proven loading of over 100 million data points per second on four servers, with performance that scales linearly with the number of servers.

“With a predictable, low-cost scaling methodology, MapR offers a top performing Hadoop distribution that can deliver the performance levels required when it comes to handling massive amounts of sensor and maintenance data,” Mike Brooks, president and COO, Mtell, said. “We discovered that other Hadoop distributions require far more hardware to accomplish what Mtell has deployed.”

The new solution from Mtell and MapR enables subject matter experts within organizations to perform remote monitoring and analysis from a central repository, where they can act on volumes of data retrieved from many assets at many locations to enable new levels of predictive maintenance.

With Mtell Previse, the system proactively learns patterns of normal and errant behavior across fleets of equipment to provide warnings of minor degradation. Early problem mitigation can prevent equipment failure and increase net product output at any plant. The platform also enables new insights into machine and process operations efficiency, quality, and utilization.

“Mtell holds a unique position in the oil and gas space as one of the only companies with an advanced machine learning platform for predictive maintenance,” Ted Dunning, chief application architect, MapR Technologies, said. “Their expertise in the oil and gas space has been invaluable and played a key role in the success of applying the MapR Distribution in this demanding environment for reliably ingesting and analyzing data.”

The combined MapR/Mtell solution drastically reduces loading time for large datasets, while also enabling the ingestion and analysis of high-speed, real-time sensor data streams.

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