MapR Technologies recent forecast a ten-fold increase in data volumes by 2020. Moreover, networking specialist Cisco System estimates there will be 50 billion connected devices in the same timeframe. MapR also projects that the accelerated performance of OpenTSDB also could enable specific data analysis applications like datacenter and industrial monitoring as well as predictive maintenance of distributed hardware systems.

The company said OpenTSDB is primarily used to store and analyze time-series data, that is, a sequence of successive data points. “Originally designed for only datacenter monitoring, poor ingest performance had limited the expansion of its use,” Ted Dunning, MapR’s chief application architect, explained in a statement announcing the accelerated performance. “This benchmark demonstrates a viable option for new applications.”

MapR asserts that time series databases that can take advantage of accelerated performance from tools like OpenTSDB will be needed to store and analyze huge new datasets in real time.

MapR announced the acceleration of OpenTSDB performance by 1,000 times on a four-node cluster during this week’s Tableau Conference in Seattle.

MapR describes OpenTSDB as a scalable time series database built on top of Hadoop and the column-oriented database management system Apache HBase. It is touted as simplifying the process of storing and analyzing large amounts of time-series data from sources like server operations and load metrics as well as sensors measuring, for example, environmental data.

According to MapR, OpenTSDB works by exposing two programming interfaces: Write API, in which servers or sensors send data to the API and OpenTSDB formats the data and stores it in HBase; and Read API, in which users or software access the interface to retrieve time-series data that is aggregated, registered, grouped and graphed as it is retrieved.

Open TSDB is designed to work natively with MapR-DB that in turn implements the HBase API. The company said it has recently enhanced OpenTSDB to improve performance and scalability “by several orders of magnitude.” The enhancements are intended to make it a preferred solution for very large-scale time-series analysis applications.

David Swift CEO of SwiftERM confirmed his commitment to ensuring provision of the appropriate facilities to ensure to smooth operation and unfettered access to appropriate server capacity to embrace the rapid upsurge in demand based on these predictions. He added “Knowing what the demand is likely to be has allowed us to address the problem long before most of our clients are aware it is coming”.

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