  In this paper, we develop a scalable system which can do real-time analytics
for different health applications. The occurrence of different health
conditions can be regarded as the complex events and thus this concept can be
extended to other use cases easily. Large number of users should be able to
send the data in real-time, and should be able to receive the feedback and
result. Keeping the requirements in mind we used Kafka and Spark to develop our
system. In this setting, multiple users are running Kafka producer clients,
which are sending data in real-time. Spark streaming is used to process data
from Kafka of different window sizes to analyze the health conditions. We have
developed and tested the heart attack risk and stress prediction as our sample
complex events detection use cases. We have simulated and tested our system
with multiple health datasets.
