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2 repositorios

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

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • f4exb/sdrangelAvatar de f4exb

    f4exb/sdrangel

    3,828Ver en GitHub↗

    SDRangel 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
    Ver en GitHub↗3,828
  • abhitronix/vidgearAvatar de abhiTronix

    abhiTronix/vidgear

    3,714Ver en GitHub↗

    VidGear 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
    Ver en GitHub↗3,714
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  • Multi-Threaded Video CaptureUses parallel worker threads to ingest video frames from network and hardware sources to prevent bottlenecks. **Distinct from Multi-Threaded Data Pipelines:** Distinct from Multi-Threaded Data Pipelines: specifically focuses on video frame capture and decoding rather than general data processing.