Name
Abstract | ||
|---|---|---|
The seamless confluence of data in motion and data at rest has the potential to redefine the Big Data analytics landscape in a diverse range of domains. To make this happen, existing data intensive computing frameworks need to be repurposed and integrated at control, data, and management levels. Towards this end, we present the system level integration of IBM InfoSphere Streams with Apache YARN. Our design leverages the key differentiating features of the two frameworks to blend high throughput batch-processing with near line-rate, low latency stream-processing. In addition, both frameworks are able to share resources and offer the same interfaces that their users are accustomed to. Using two real-world examples, we illustrate how such a system can be used in production. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/BigData.2014.7004443 | BigData Conference |
Keywords | Field | DocType |
Big Data,data analysis,user interfaces,Apache YARN,Big Data analytics,IBM InfoSphere Streams,batch-processing,data intensive computing frameworks,stream-processing,user interfaces,batch-processing,cluster management,stream-processing | Data mining,IBM,Yarn,Data-intensive computing,Computer science,Infosphere,Latency (engineering),Throughput,Stream processing,Big data | Conference |
ISSN | Citations | PageRank |
2639-1589 | 3 | 0.40 |
References | Authors | |
18 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zubair Nabi | 1 | 3 | 0.40 |
| Rohit Wagle | 2 | 3 | 0.40 |
| Eric Bouillet | 3 | 47 | 5.06 |