MapR Edge Released at
Strata + Hadoop World
March 14, 2017
Edge is 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
“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,
• 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.
analytics: Combines operational decision-making with real-time analysis
of data at the edge.
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.
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.
reliability: Delivers a reliable computing environment to tolerate
multiple hardware failures that can occur in remote, isolated
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.”*
MapR Edge deployments are used in conjunction with central analytics and
operational clusters running on the MapR Converged Enterprise Edition.
MapR Edge is available in 3-5 node configurations. The new offering is