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Flink-ML: machine learning in Apache Flink [PDF]

Brazilian Journal of Technology
The emergence of Big Data has spurred the development of various frameworks designed for efficient data storage and processing. Key frameworks include Hadoop, Spark, Flink, Storm, Pig, and Zookeeper. Among these, Apache Flink stands out as a prominent open-source platform known for its powerful stream and batch processing capabilities.
Messaoud Mezati, Ines Aouria
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Apache Flink in current research

IT - Information Technology, 2016
Abstract Recent trends in data collection and the decreasing prices of storage result in constantly growing amounts of analyzable data. These masses of data cannot easily be processed by traditional database systems as these do not allow for a sufficient degree of scalability.
Asterios Katsifodimos, Volker Markl, Jonas Traub   +2 more
exaly   +3 more sources

Big data multi-query optimisation with Apache Flink

International Journal of Web Engineering and Technology, 2018
Big data analytic frameworks, such as MapReduce, Spark and Flink, have recently gained more popularity to process large data. Flink is an open-source Apache-hosted big data analytic framework for processing batch and streaming data. For historical data processing (batch), Flink's query optimiser is built based on techniques which have been used in the ...
Radhya Sahal, Fatma Omara, Mohamed Khafagy   +2 more
exaly   +2 more sources

HYAS: Hybrid Autoscaler Agent for Apache Flink

Lecture Notes in Computer Science, 2023
Euripides G M Petrakis
exaly   +2 more sources

State management in Apache Flink®

Proceedings of the VLDB Endowment, 2017
Stream processors are emerging in industry as an apparatus that drives analytical but also mission critical services handling the core of persistent application logic. Thus, apart from scalability and low-latency, a rising system need is first-class support for application state together with strong consistency guarantees, and adaptivity to cluster ...
Seif Haridi, Paris Carbone, Kostas Tzoumas   +2 more
exaly   +3 more sources

TALOS: Task Level Autoscaler for Apache Flink

Lecture Notes in Computer Science

exaly   +2 more sources

Adaptive Distributed Partitioning in Apache Flink

2020 IEEE 36th International Conference on Data Engineering Workshops (ICDEW), 2020
Dynamically adapting the workload of each worker in Flink is a challenging issue. In this work, we deal with a special case, where the data are conceptually split in contiguous overlapping regions. This scenario is encountered in several streaming applications, such as those employing nearest neighbor queries.
Theodoros Toliopoulos, Anastasios Gounaris   +1 more
openaire   +2 more sources

When to Use a Distributed Dataflow Engine: Evaluating the Performance of Apache Flink

, 2016
With the increasing amount of available data, distributed data processing systems like Apache Flink, Apache Spark have emerged that allow to analyze large-scale datasets.
Odej Kao, Lauritz Thamsen
exaly   +2 more sources

Keyed Watermarks: A Fine-grained Tracking of Event-time in Apache Flink

, 2023
Big Data Stream processing engines such as Apache Flink use windowing techniques to handle unbounded streams of events. Gathering all perti nent input within a window is crucial for event time windowing since it affects how accurate results are.
Tamer Arafa
exaly   +2 more sources

Apache Flink: Stream Analytics at Scale

2016 IEEE International Conference on Cloud Engineering Workshop (IC2EW), 2016
Apache Flink is an open source system for expressive, declarative, fast, and efficient data analysis on both historical (batch) and real-time (streaming) data. Flink combines the scalability and programming flexibility of distributed MapReduce-like platforms with the efficiency, out-of-core execution, and query optimization capabilities found in ...
Asterios Katsifodimos, Sebastian Schelter   +1 more
openaire   +2 more sources