MapR Technologies, Inc. has released MapR Edge, a small footprint edition of the MapR Converged Data Platform. Addressing the need to capture, process, and analyze data generated by Internet-of-Things (IoT) devices close to the source, MapR Edge provides secure local processing, quick aggregation of insights on a global basis, and the ability push intelligence back to the edge for faster and more significant business impact.
“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,” said Jason Stamper, analyst, Data Platforms & Analytics, 451 Research. “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.”
The new MapR Edge is optimized for data collection, processing, streaming and analytics at the edge. MapR Edge integrates a globally distributed elastic data plane that not only supports distributed file processing but also strongly consistent geo-distributed database applications.
“Our customers have pioneered the use of big data and want to continuously stay ahead of the competition,” said Ted Dunning, chief application architect, MapR Technologies. “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.”
The ability to act locally, learn globally describes how IoT applications leverage local data from numerous sources in many locations but often require machine learning or deep learning models with global knowledge. These models must then be deployed back to the edge to enable real-time decisions based on local events.
MapR Edge provides several benefits for deploying IoT/edge applications, including:
- Distributed data aggregation: Provides high-speed local processing, especially 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 occasionally-connected environments.
- 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.
Existing solutions were not designed for seamless, large-scale distributed global processing. MapR Edge leverages the advanced, global-distribution and real-time synchronization capabilities of the patented MapR Converged Data Platform to deliver a end-to-end platform from the edge to the cloud. Its proven, mission-critical features allow the delivery of compute power close to the data sources while also allowing efficient aggregation to one or more centralized clusters for large-scale analytics and processing on all data.
According to Gartner, “Proliferation of IoT devices and the need for real-time insights are the greatest drivers of computing at the edge of the network. Technology strategic planners should extend value propositions to edge computing and accelerate product portfolios to address market expectations for edge analytics.”*
, Published: 24 February 2017ID: G00322891
*From Gartner’s Market Trends: The Need for Real-Time Insights Offers Market Opportunities in Edge Computing