MapR Technologies has added a small footprint edition of the MapR Converged Data Platform to address the need to capture, process, and analyze data generated by IoT devices close to the source. MapR Edge enables secure local processing, quick aggregation of insights on a global basis, and the ability to push intelligence back to the edge for faster business impact.
The new offering is optimized for data collection, processing, streaming, and analytics at the network periphery, and integrates a globally distributed elastic data plane that supports distributed file processing as well as consistent geo-distributed database applications.
Working in real-time at the edge presents challenges and opportunities to digitally transform an organization, said Ted Dunning, chief application architect, MapR Technologies, noting that customers increasingly want to act locally, but learn globally in a more efficient manner. According to MapR, this ability to act locally, and learn globally describes how IoT applications leverage local data from numerous sources but require machine learning or deep learning models with global knowledge that can then be deployed back to the edge to enable real-time decisions based on local events.
MapR Edge is intended to provide several benefits for deploying IoT/edge applications, including distributed data aggregation; bandwith awareness; a global view of all distributed clusters; and the ability to combine operational decision making with real-time analysis of data at the edge. It also supports end-to-end IoT security with authentication, authorization, and access control from the edge to the central clusters, and delivers secure encryption on the wire for data communicated between the edge and the main data center. MapR Edge supports standards such as POSIX and HDFS API for file access, ANSI SQL for querying, Kafka API for event streams, and HBase and OJAI API for NoSQL database.
MapR Edge is available in 3-5 node configurations, and deployments are used in conjunction with central analytics and operational clusters running on the MapR Converged Enterprise Edition.