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This article needs to be updated. Please help update this article to reflect recent events or newly available information. (August 2022) The following tables compare general and technical information for a number of current, notable video hosting services. Please see the individual products' articles for further information. General information Basic general information about the hosts ...
Kafka: Confluent Data streaming processing 2.3.0 Apache Kafka: 2011 Kaltura: Kaltura Video and rich media management platform and applications dual-licensed under AGPL, and commercial license, provided as self hosted and SaaS 6.0 (Falcon) Kaltura 2012 Kea DHCP: Internet Systems Consortium: DHCPv4 and DHCPv6 server software 1.8.2 Kea DHCP: 2014 ...
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.
(HTTP Live Streaming, Smooth Streaming, HTTP Dynamic Streaming) Yes: Yes Yes No Yes Yes Yes Yes Yes (RTMP, RTMPE, RTMPTE, RTMPT, RTMPS, RTMP Dynamic Streaming) Yes No No Yes Yes Name HTTP MPEG DASH WebRTC RTSP MMS RTP RTCP UDP TCP RTMP MPEG TS Real Data Transport Web sockets HLS DASH SRTP
Online video platforms allow users to upload, share videos or live stream their own videos to the Internet. These can either be for the general public to watch, or particular users on a shared network. The most popular video hosting website is YouTube, 2 billion active until October 2020 and the most extensive catalog of online videos. [1]
The second generation uses the pull mode in data transportation, and file system in data storage. It paid more attention to stability and reliability, and shows a comparable performance to the first generation in response time and Kafka on log collection. The third generation combines the Pull mode with some Push operations.
Spark Core is the foundation of the overall project. It provides distributed task dispatching, scheduling, and basic I/O functionalities, exposed through an application programming interface (for Java, Python, Scala, .NET [16] and R) centered on the RDD abstraction (the Java API is available for other JVM languages, but is also usable for some other non-JVM languages that can connect to the ...
Stream processing is essentially a compromise, driven by a data-centric model that works very well for traditional DSP or GPU-type applications (such as image, video and digital signal processing) but less so for general purpose processing with more randomized data access (such as databases). By sacrificing some flexibility in the model, the ...