MapR Edge is a small footprint edition of the MapR Converged Data Platform that addresses the need to capture, process, and analyze IoT data close to the source.
In many IoT environments today, a tremendous volume of data is created at the sources. Historically, organizations wanting to immediately analyze all that data from their IoT sources had few options - each with major drawbacks. For example, organizations could deploy a full-scale, standalone cluster at each IoT site. This option is clearly not viable in space-constrained environments, such as automobiles. It also fails to take full advantage of data from other IoT sites that, when taken in aggregate, could have yielded deeper insights. Alternatively, organizations could send IoT data directly to a central cluster for processing. But this option is not well-suited to IoT environments with limited connectivity or bandwidth, and also limits the possibility of analyzing IoT data directly at the source.
MapR Edge offers a better, more optimal solution. With a small footprint and reliable replication capabilities, MapR Edge is ideally suited for space- and bandwidth-constrained environments. Used in combination with a core MapR Enterprise deployment (on-premises or in the cloud), MapR Edge empowers organizations to securely process data locally, quickly aggregate insights on global basis, and ultimately push intelligence back to the edge for faster and more significant business impact.
MapR Edge is a fully-functional MapR cluster that can be run on small form-factor commodity hardware, such as Intel NUCs. Edge clusters are supported in three- to five-node configurations, with each boasting converged enterprise data services (e.g., files, tables, streams, Drill, Spark, Hive), along with related data management and protection capabilities (e.g., security, snapshots, mirroring, replication, and compression).
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.
*Only for 5-node Edge cluster configurations.
MapR Edge is a fully functional MapR cluster that can be run on small form-factor commodity hardware (such as Intel NUCs). Edge clusters are supported in three- to five-node configurations, with each boasting converged enterprise data services (e.g., files, tables, streams, Drill, Spark), along with related data management and protection capabilities (e.g., security, snapshots, mirroring, replication, and compression).