Tossing the old historian over the IT fence and running it on a bigger computer or cluster will not meet the requirements of the users and applications at the enterprise.

Reservoir Data Sheet
Mtell Reservoir is an ultimate high performance, scalable, general purpose enterprise historian ready for third-party data and client applications.

The Mtell Reservoir leverages the Apache Hadoop and OpenTSDB (time-series database) software technology.The Apache Hadoop software library allows for load-sharing by distributing processing of large data sets across clusters of computers. Hadoop scales from a single server to thousands, each offering local computation and input/output storage.

Mtell Reservoir provides Extreme improvements over traditional plant historians for retrieval and display of very large (BIG) data sets.

The Mtell Reservoir enables BIG data – scalable to thousands of sites, millions of assets, with billions of sensors, and trillions of sensor readings. Open API’s assure placement of data into Mtell Reservoir from any time series data source. Prepare that data for “collective” analysis. Mtell Reservoir is a key repository enabling the recording of events and trending and analysis of data values that lead up to them.

  • Storage for all sensor time-series data
  • Local and remote data center synchronization
  • Handles data and event streams for linking and correlation
  • Power to process large datasets
  • Foundation for any predictive analytics on time-series data with third-party analysis tools including R, Mathematica, etc.
  • Scalability to multi-CPU clusters for:
    – Increase data processing requirements
    – Faster disk I/O operations

Mtell CloudSync

Data loading and ingestion into Mtell Reservoir occurs across multiple sites using third- party tools. Data are ingested in real-time streams, batch uploads, or import of comma-separated (CSV) files. Reservoir intrinsically accepts data from the Mtell CloudSync service attached to distributed Previse systems. Its elegant and sophisticated bi-directional architecture ensures CloudSync performs stream-based processing across challenging and bandwidth limited network connections such as satellite links. Transmitted streams include sensor data values, alerts, events, and maintenance activities. Automatic, lossless data compression means more efficient data transfers, and dynamic throttling keeps transfer within configured bandwidth limits. Signal prioritization assures the most pertinent data are received first, and the system will recover older data as bandwidth becomes available. CloudSync also delivers machine learning signatures from Mtell Summit into monitoring Agents at remote sites.

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