Apache flink : Stream and batch processing in a single engine
By Paris Carbone, Asterios Katsifodimos, Stephan Ewen, Volker Markl, Seif Haridi, Kostas Tzoumas. 2015
1,485 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/9278b8e5813948c51b288b9929784672682988da
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Yoshua Bengio
- Sergey Levine
- Martín Abadi
- Ruslan Salakhutdinov
- Dacheng Tao
- Quoc V. Le
- Yi Yang
- Percy Liang
Citations in the corpus
- Online Machine Learning in Big Data Streams (2018, background): “A recent comparison of distributed recommenders, including an online one based on a Flink parameter server, is given in [185].”
- A comprehensive survey of anomaly detection techniques for high dimensional big data (2020, methodology): “For both stream processing and batch processing, Apache Flink [137] another opensource framework was proposed combining the scalability and programming flexibility of other distributed paradigms such as MapReduce [163, 164].”
- Will Distributed Computing Revolutionize Peace? The Emergence of Battlefield IoT (2018, methodology): “Data-parallel implementations of machine learning algorithms perform the same computation over all compute nodes by definition [28], [29].”
- Naiad: a timely dataflow system (2013, background): “• Later systems decouple processing and coordina/on for faster cluster adap/on – Drizzle [7] • Updates to Naiad – last public commit in 2014 [3] • Industry projects – Apache FlinkTM [8]”
- Characterizing Microservice Dependency and Performance: Alibaba Trace Analysis (2021, background): “These are fundamentally different from DAG dependencies [12, 15, 38].”
- A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues (2017, methodology): “Apache Flink [94] [93] [95] Open source tool, descriptive analytics, pipeline architecture, real-time analytics, big data processing engine Faster iteration processing, better memory management than Spark, low latency, efficient garbage collection Not tested with the large scale of IoT devices”