2 repository-uri
Awesome GitHub RepositoriesMulti-Threaded Data Pipelines
Architectural patterns for processing high-throughput data streams across parallel worker threads to maintain stream integrity.
Distinct from Multi-Threaded Batch Processing: Distinct from Multi-Threaded Batch Processing: focuses on continuous streaming data pipelines rather than discrete batch job execution.
Explore 2 awesome GitHub repositories matching software engineering & architecture · Multi-Threaded Data Pipelines. Refine with filters or upvote what's useful.
Awesome Multi-Threaded Data Pipelines GitHub Repositories
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- f4exb/sdrangel
f4exb/sdrangel
3,828Vezi pe GitHubSDRangel is a comprehensive software-defined radio suite and digital signal processing framework. It functions as an RF spectrum analyzer and modular radio demodulator, providing a unified hardware abstraction layer to connect various radio devices to software processing pipelines for data acquisition and transmission. The platform is distinguished by its modular architecture, which uses a data-flow graph of dynamic libraries to construct signal processing chains. This allows for a plugin-based environment where users can extract audio and digital data from raw radio signals using various mod
Implements a multi-threaded sample pipeline with shared memory buffers to minimize signal latency.
C++airspyairspyhfbladerf - abhitronix/vidgear
abhiTronix/vidgear
3,714Vezi pe GitHubVidGear is a high-performance Python video processing framework designed for capturing, transcoding, and manipulating video streams. It functions as a multi-protocol video streamer and a WebRTC streaming server, enabling the transfer of video frames over networks using RTSP, RTMP, RTP, and MJPEG protocols. The project distinguishes itself through hardware-accelerated video transcoding and decoding using GPU backends like CUDA to reduce CPU load. It includes a cross-platform screen capture tool and a specialized system for establishing direct peer-to-peer media connections using WebRTC signali
Reads frames from IP cameras, network streams, and hardware decoders using multi-threaded processing.
Pythondashffmpegframework
