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  2. Streaming data - Wikipedia

    en.wikipedia.org/wiki/Streaming_data

    Data streaming is also modular, because systems components may be separated and recombined mainly for flexibility and variety. Data streaming works in different application versions and systems such as iOS. It is also possible to change the speed of data streaming. [9] A consequence of modularity is the creation of platforms.

  3. Complex event processing - Wikipedia

    en.wikipedia.org/wiki/Complex_event_processing

    openPDC — A set of applications for processing streaming time-series data in real-time. Oracle Event Processing - for building applications to filter, correlate, and process events in real time. SAP ESP - A low-latency, rapid development and deployment platform that allows processing multiple streams of data in real time [ 19 ]

  4. Apache Kafka - Wikipedia

    en.wikipedia.org/wiki/Apache_Kafka

    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.

  5. Stream processing - Wikipedia

    en.wikipedia.org/wiki/Stream_processing

    The first is an example of processing a data stream using a continuous SQL query (a query that executes forever processing arriving data based on timestamps and window duration). This code fragment illustrates a JOIN of two data streams, one for stock orders, and one for the resulting stock trades.

  6. Comet (programming) - Wikipedia

    en.wikipedia.org/wiki/Comet_(programming)

    It emulates a bidirectional stream between the browser and server by using two synchronous HTTP connections. The JSONRequest object, proposed by Douglas Crockford, would be an alternative to the XHR object. [31] Use of plugins, such as Java applets or the proprietary Adobe Flash (using RTMP protocol for data streaming to Flash applications ...

  7. Real-time database - Wikipedia

    en.wikipedia.org/wiki/Real-time_database

    Real-time databases may be modified to improve accuracy and efficiency and to avoid conflict, by providing deadlines and wait periods to insure temporal consistency. Real-time database systems offer a way of monitoring a physical system and representing it in data streams to a database. A data stream, like memory, fades over time.

  8. Apache Flink - Wikipedia

    en.wikipedia.org/wiki/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 parallel) manner. [5]

  9. Dynamic Adaptive Streaming over HTTP - Wikipedia

    en.wikipedia.org/wiki/Dynamic_Adaptive_Streaming...

    DASH is an adaptive bitrate streaming technology where a multimedia file is partitioned into one or more segments and delivered to a client using HTTP. [15] A media presentation description (MPD) describes segment information (timing, URL, media characteristics like video resolution and bit rates), and can be organized in different ways such as SegmentList, SegmentTemplate, SegmentBase and ...