Discretized Streams: An Efficient and Fault-Tolerant Model for Stream Processing on Large Clusters

By Matei A. Zaharia, Tathagata Das, Haoyuan Li, Scott Shenker, Ion Stoica. USENIX Workshop on Hot Topics in Cloud Computing, 2012

546 citations recorded by the source corpus.

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State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing (2016, methodology): “Considering compatibility aspects, Spark Streaming is suitable if Apache Sparks is315 chosen as a batch processing component.”
Exploiting iterative-ness for parallel ML computations (2014, background): “Some mix MapReduce iterations with multi-iteration in-memory state [41, 43].”
A Survey on Ensemble Learning for Data Stream Classification (2017, methodology): “StreamDM is an open source framework for big data stream mining that uses the Spark Streaming [Zaharia et al. 2012] extension of the core Spark API.”
MillWheel: Fault-Tolerant Stream Processing at Internet Scale (2013, background): “Furthermore, our system is capable of significantly finer checkpoints than Spark Streaming [34], which proposes backups every minute, and depends on application idempotency and system slack for recovery.”