  Distributed networks and real-time systems are becoming the most important
components for the new computer age, the Internet of Things (IoT), with huge
data streams or data sets generated from sensors and data generated from
existing legacy systems. The data generated offers the ability to measure,
infer and understand environmental indicators, from delicate ecologies and
natural resources to urban environments. This can be achieved through the
analysis of the heterogeneous data sources (structured and unstructured). In
this paper, we propose a distributed framework Event STream Processing Engine
for Environmental Monitoring Domain (ESTemd) for the application of stream
processing on heterogeneous environmental data. Our work in this area
demonstrates the useful role big data techniques can play in an environmental
decision support system, early warning and forecasting systems. The proposed
framework addresses the challenges of data heterogeneity from heterogeneous
systems and real time processing of huge environmental datasets through a
publish/subscribe method via a unified data pipeline with the application of
Apache Kafka for real time analytics.
