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MapR unveils platform for IoT analytics at the edge

MapR unveils platform for IoT analytics at the edge

MapR Edge is a new small footprint edition of the MapR Converged Data Platform geared for capturing, processing and analyzing data from internet of things devices at the edge.

At Strata + Hadoop World in San Jose, Calif., Tuesday, MapR Technologies took the wraps off a new small footprint edition of its Converged Data Platform geared for capturing, processing and analyzing data from internet of things (IoT) devices at the edge.

MapR Edge, designed to work in conjunction with the core MapR Converged Enterprise Edition, provides local processing, aggregation of insights at the core and the ability to then push intelligence back to the edge.

"You can think of it as a mini-cluster that's close to the source and can do analytics where the data resides, but then send data back to the core," says Dale Kim, senior director, Industry Solution, at MapR Technologies.

[ Related: MapR helps developers leverage analytics with microservices ]

"The use cases for IoT continue to grow, and in many situations, the volume of data generated at the edge requires bandwidth levels that overwhelm the available resources," Jason Stamper, analyst, Data Platforms & Analytics, 451 Research, added in a statement. "MapR is pushing the computation and analysis of IoT data close to the sources, allowing more efficient and faster decision-making locally, while also allowing subsets of the data to be reliably transported to a central analytics deployment.

Many core IoT use cases, like vehicles and oil rigs, operate in conditions with limited connectivity, making sending massive streams of data back to a central analytics core impractical. The idea behind MapR Edge is to capture and process most of that data at the edge, where the data is created, then send summarized data back to the core, which then aggregates that summarized data from hundreds or thousands of edge IoT devices.

MapR Technologies calls this concept "Act Locally, Learn Globally," which means that IoT applications leverage local data from numerous sources for constructing machine learning or deep learning models with global knowledge. These models are then deployed to the edge to enable real-time decisions based on local events.

[ Related: MapR and Outscale partner on big data PaaS ]

To make it work, MapR Edge integrates a globally distributed elastic data fabric that supports distributed processing and geo-distributed database applications.

MapR Edge capabilities include:

  • Distributed data aggregation. Provides high-speed local processing, useful for location-restricted or sensitive data such as personally identifiable information (PII), and consolidates IoT data from edge sites.
  • Bandwidth awareness. Adjusts throughput from the edge to the cloud and/or data center, even with environments that are only occasionally connected.
  • Global data plane. Provides global view of all distributed clusters in a single namespace, simplifying application development and deployment.
  • Converged analytics. Combines operational decision-making with real-time analysis of data at the edge.
  • Unified security. End-to-end IoT security provides authentication, authorization and access control from the edge to the central clusters. MapR Edge also delivers secure encryption on the wire for data communicated between the edge and the main data center.
  • Standards based. MapR Edge adheres to standards including POSIX and HDFS API for file access, ANSI SQL for querying, Kafka API for event streams and HBase and OJAI API for NoSQL database.
  • Enterprise-grade reliability. Delivers a reliable computing environment to tolerate multiple hardware failures that can occur in remote, isolated deployments.

MapR Edge deployments are intended to be used in conjunction with central analytics and operational clusters running on the MapR Converged Enterprise Edition. It is available in 3-5 node configurations and optimized for small form-factor commodity hardware like the Intel NUC Mini PC. MapR Edge deployments can store up to 50TB per cluster.

Jack Norris, senior vice president, Data and Applications, MapR Technologies, notes that MapR has all the data protection capabilities of MapR Converged Data Platform.

"There's redundancy built in," he says. "High, availability, self-healing, all the capabilities of the MapR technology are extended to the edge device."

"Our customers have pioneered the use of big data and want to continuously stay ahead of the competition," Ted Dunning, chief application architect, MapR Technologies, said in a statement Tuesday. "Working in real-time at the edge presents unique challenges and opportunities to digitally transform an organization. Our customers want to act locally, but learn globally, and MapR Edge lets them do that more efficiently, reliably, securely and with much more impact."

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