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and reliability; and now with event time-
stamping, a single historical event can
be retrieved instantly. But perhaps more
importantly, a single stream can be logically
divided into hundreds-of-thousands of topics
with no impact on performance. No other
streaming approach provides this scale,
flexibility and performance.”
With release 6.0.1, MapR exposed new
functionality through multiple APIs. The
MapR-ES API adds support for an event-
time timestamp as part of an update to
the Kafka 1.0 API and structured streaming
in Apache Spark 2.2.1 which leverages
this timestamp for new stream processing
capabilities like windowing and aggregation.
For IoT applications, this helps ensure that
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data across a globally-distributed network of
devices and sensors can be flexibly separated
into logical topics and properly aggregated
for real-time analytics and applications. For
companies adopting a ‘streaming system
of record’ that can be reliably persisted
for extended periods for compliance or
developer productivity, MapR-ES now also
maintains a time index so applications can
easily seek to a specific point in time from
which to consume.
“We are very excited about the new
features,” said Eric Keister, Advanced
Analytics and Emerging Technologies
Manager at Anadarko. “Spark structured
streaming allows us to use advanced
analytics on real-time oil well data, while
FEATURE: BUSINESS ANALYTICS
NO OTHER
STREAMING
APPROACH
PROVIDES
THIS SCALE,
FLEXIBILITY AND
PERFORMANCE.
INTELLIGENTCIO
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