A Survey of Stream Processing Frameworks for Big Data
| Author(s) | : | Mansi Shah, Vatika Tayal |
| Institution | : | M. Tech. Scholar, Computer Science and Engineering Department, N.S.I.T, Jetalpur, Gujarat |
| Published In | : | Vol. 2, Issue 5 — May 2015 |
| Page No. | : | 465-471 |
| Domain | : | Engineering |
| Type | : | Research Paper |
| ISSN (Online) | : | 2348-4470 |
| ISSN (Print) | : | 2348-6406 |
— In recent years due to the acceleration in IoT (Internet-of-Things) and M2M (Machine-to-Machine)communications streams are everywhere. Twitter streams, log streams, TCP streams click streams and event streams aresome good examples. Big data streaming applications need to process and analyze information in real-time. TheMap/Reduce model and its open source implementation Hadoop designed as a high fault-tolerant system for batchprocessing and high throughput jobs. However, the Map/Reduce framework is not suitable real-time streamingapplications that require very low latency of response. Owing to the high demand for processing non -batch jobs such asreal-time and streaming jobs several big data frameworks have been developed or under developing. This paper presentsa survey of open source frameworks that support big data stream processing.
Mansi Shah, Vatika Tayal, “A Survey of Stream Processing Frameworks for Big Data”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 2, Issue 5, pp. 465-471, May 2015.
