• Sindhu Dinesh
• Dr. Hemavathy R

: In the current world, stream processing is considered as one of the big data technologies. Distributed stream processing platforms are real-time monitoring systems that help evaluate, analyze and infer insights from huge unbounded data streams in an efficient way. Since these are characterized by high throughput and low latency, they suffice the requirements of Big Data or Internet of Things applications. However, these platforms offer services that are raw and hence make it difficult to integrate a real-world application with them. Moreover, given the options, choosing the right platform requires some effort. This paper analyses and describes the architectures, features such as fault tolerance, recovery, efficiency and throughput of some stream processing platforms. The study shows that native stream processing systems perform better than micro-batching. Owing to the high-speed evolving nature of this data, several challenges are faced in mining it. This paper also throws a light on such challenges and presents some techniques in mining such data. The performance is compared and analyzed based on the certain evaluation metrics.

stream processing, fault tolerance, stream processing platforms and techniques

"A Review of Stream Processing Platforms and Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.201-207, June 2019, Available :http://www.jetir.org/papers/JETIR1906I55.pdf

"A Review of Stream Processing Platforms and Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp201-207, June 2019, Available at : http://www.jetir.org/papers/JETIR1906I55.pdf