Photon: Fault-tolerant and Scalable Joining of Continuous Data Streams
SIGMOD '13: Proceedings of the 2013 international conference on Management of data, ACM, New York, NY, USA, pp. 577-588 Web-based enterprises process events generated by millions of users interacting with their websites. Rich statistical data distilled from combining such interactions in near real-time generates enormous business value. In this paper, we describe the architecture of Photon, a geog
Paper: MillWheel: Fault-Tolerant Stream Processing at Internet Scale - High Scalability -
« Stuff The Internet Says On Scalability For September 6, 2013 | Main | Wide Fast SATA: the Recipe for Hot Performance » Ever wonder what powers Google's world spirit sensing Zeitgeist service? No, it's not a homunculus of Georg Wilhelm Friedrich Hegel sitting in each browser. It's actually a stream processing (think streaming MapReduce on steroids) system called MillWheel, described in this very
StormRider: Harnessing “Storm” for Social Networks Vaibhav Khadilkar, Murat Kantarcioglu, Bhavani Thuraisingham The University of Texas at Dallas Contents 1 Introduction 1 2 Overview of Tools and Technologies Used 4 2.1 Overview of RDF . . . . . . .
StormRider: Harnessing “Storm” for Social Networks Vaibhav Khadilkar, Murat Kantarcioglu, Bhavani Thuraisingham The University of Texas at Dallas Contents 1 Introduction 1 2 Overview of Tools and Technologies Used 4 2.1 Overview of RDF . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Overview of Jena . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3 Overview of Jena-HBase . .
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One SQL to rule them all: an efficient and syntactically idiomatic approach to management of streams and tables | the morning paper
http://www.vldb.org/conf/2006/p163-jeffery.pdf
Continuous Pipelines at Google
gopulsar.io
http://www.vldb.org/pvldb/vol7/p1441-boykin.pdf
MillWheel: Fault-Tolerant Stream Processing at Internet Scale
PVLDB Proceedings Template - WORD
