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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.
Stream processing hardware can use scoreboarding, for example, to initiate a direct memory access (DMA) when dependencies become known. The elimination of manual DMA management reduces software complexity, and an associated elimination for hardware cached I/O, reduces the data area expanse that has to be involved with service by specialized ...
Samza allows users to build stateful applications that process data in real-time from multiple sources including Apache Kafka.. Samza provides fault tolerance, isolation and stateful processing.
Stream editing processes a file or files, in-place, without having to load the file(s) into a user interface. One example of such use is to do a search and replace on all the files in a directory, from the command line. On Unix and related systems based on the C language, a stream is a source or sink of data, usually individual bytes or characters.
In type theory and functional programming, a stream is a potentially infinite analog of a list, given by the coinductive definition: [1] [2] data Stream α = Nil | Cons α ( Stream α ) Generating and computing with streams requires lazy evaluation , either implicitly in a lazily evaluated language or by creating and forcing thunks in an eager ...
On April 30, 2015 version 1.0.0 of Reactive Streams for the JVM was released, [5] [6] [11] including Java API, [12] a textual specification, [13] a TCK and implementation examples. It comes with a multitude of compliant implementations verified by the TCK for 1.0.0, listed in alphabetical order: [11] Akka Streams [14] [15] MongoDB [16]
Apache Beam “provides an advanced unified programming model, allowing (a developer) to implement batch and streaming data processing jobs that can run on any execution engine.” [23] The Apache Flink-on-Beam runner is the most feature-rich according to a capability matrix maintained by the Beam community.
Though streaming algorithms had already been studied by Munro and Paterson [1] as early as 1978, as well as Philippe Flajolet and G. Nigel Martin in 1982/83, [2] the field of streaming algorithms was first formalized and popularized in a 1996 paper by Noga Alon, Yossi Matias, and Mario Szegedy. [3]