Mtell and MapR Deploy Big Data Platform for Oil and Gas

Mtelligence Corporation (Mtell) and MapR Technologies, Inc. announced a big data platform called Mtell Reservoir that combines the MapR Distribution including Hadoop, Mtell Previse Software, and Open TSDB (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, chemical plants, mining, water/wastewater, etc.

Designed 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,” said: Mike Brooks, president and COO, Mtell. “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. There 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 thus increasing 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,” said Ted Dunning, chief application architect, MapR. “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.“

With contemporary approaches, it takes an inordinate amount of time to load large datasets, forcing unacceptable delays before analysis can start. The combined MapR/Mtell solution reduces loading time, while also enabling the ingestion and analysis of high-speed, real-time sensor data streams. This big data solution for manufacturing industries leverages OpenTSDB and MapR-DB (the MapR in-Hadoop NoSQL database), which allows more data to be acquired and accessed with Hadoop and uses less hardware for lower TCO. MapR-DB further decreases costs by simplifying the administration of large databases.