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Apache Flink. Apache Flink is an open-source, unified stream-processing and batch-processing framework developed by the Apache Software Foundation. The core of Apache Flink is a distributed streaming data-flow engine written in Java and Scala. [3][4] Flink executes arbitrary dataflow programs in a data-parallel and pipelined (hence task ...
In computer science, stream processing (also known as event stream processing, data stream processing, or distributed stream processing) is a programming paradigm which views streams, or sequences of events in time, as the central input and output objects of computation. Stream processing encompasses dataflow programming, reactive programming ...
Apache Kafka is a distributed event store and stream-processing platform. It is an open-source system developed by the Apache Software Foundation written in Java and Scala. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds. Kafka can connect to external systems (for data import/export ...
Apache Samza is an open-source, near-realtime, asynchronous computational framework for stream processing developed by the Apache Software Foundation in Scala and Java. It has been developed in conjunction with Apache Kafka. Both were originally developed by LinkedIn. [2]
Reactive Streams were proposed to become part of Java 9 by Doug Lea, leader of JSR 166 [8] as a new Flow class [9] that would include the interfaces currently provided by Reactive Streams. [5] [10] After a successful 1.0 release of Reactive Streams and growing adoption, the proposal was accepted and Reactive Streams was included in JDK9 via the ...
Apache Storm is a distributed stream processing computation framework written predominantly in the Clojure programming language. Originally created by Nathan Marz [2] and team at BackType, [3] the project was open sourced after being acquired by Twitter. [4] It uses custom created "spouts" and "bolts" to define information sources and ...
MOA is an open-source framework software that allows to build and run experiments of machine learning or data mining on evolving data streams. It includes a set of learners and stream generators that can be used from the Graphical User Interface (GUI), the command-line, and the Java API.
Event processing is a method of tracking and analyzing (processing) streams of information (data) about things that happen (events), [1] and deriving a conclusion from them. Complex event processing (CEP) consists of a set of concepts and techniques developed in the early 1990s for processing real-time events and extracting information from ...