Automated development of a parallel distributed system for streaming data processing

An automated development of a parallel distributed dynamically scalable fault-tolerant system for processing large amount of streaming data is performed. The system is based on the framework for distributed computing Hazelcast and the usage of the toolkit for generation of programs from high-level specifications of algorithms. The inspection and study of this system is performed on an example of data processing in Twitter social network in which sentiment analysis functionality is implemented. The mechanism of the deployment of the created system on a cloud platform is examined.