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3 dépôts

Awesome GitHub RepositoriesServerless Processing

Execution of lightweight compute functions directly on data streams without managing underlying server infrastructure.

Distinct from Stream Processing: Distinct from Stream Processing: focuses on the serverless, function-based execution model rather than general stream frameworks.

Explore 3 awesome GitHub repositories matching data & databases · Serverless Processing. Refine with filters or upvote what's useful.

Awesome Serverless Processing GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • apache/pulsarAvatar de apache

    apache/pulsar

    15,276Voir sur GitHub↗

    Apache Pulsar is a cloud-native distributed pub-sub messaging system designed for high-performance data ingestion. It functions as a geo-replicated data streamer and a multi-tenant event streaming platform, providing a serverless stream processing engine and a tiered storage messaging broker. The system distinguishes itself by separating serving layers from storage layers to allow independent scaling of compute and data retention. It features native geo-replication to synchronize messages across different geographical regions and employs a multi-layered tenant isolation model using authentica

    Provides a serverless engine to execute compute functions directly on streams for data transformation and routing.

    Java
    Voir sur GitHub↗15,276
  • nuclio/nuclioAvatar de nuclio

    nuclio/nuclio

    5,730Voir sur GitHub↗

    Nuclio is a high-performance serverless framework designed for Kubernetes that automatically executes user functions when events arrive from HTTP endpoints, message queues, or streaming data platforms. It processes hundreds of thousands of events per second per function instance through efficient parallel workers, and can allocate functions to run on either CPU or GPU hardware to match workload requirements for data processing or machine learning tasks. The platform scales function instances down to zero when idle and wakes them on demand based on incoming event load, while providing an event

    Processes large volumes of streaming data and events in real time with sub-second latency using serverless functions.

    Go
    Voir sur GitHub↗5,730
  • gosom/google-maps-scraperAvatar de gosom

    gosom/google-maps-scraper

    3,192Voir sur GitHub↗

    This project is a distributed scraping engine designed to extract business details, customer reviews, and lead information from Google Maps. It functions as a business scraper and data extractor that can be deployed as a permanent system or as on-demand serverless functions. The system utilizes a proxy-routed web crawler to manage request origins via SOCKS5, HTTP, and HTTPS proxies. To locate contact information, it includes an email extraction tool that recursively crawls business websites linked within map listings. The software supports coordinate-based radius searches for efficient data

    Processes business data using on-demand serverless compute functions without managing underlying infrastructure.

    Godistributed-scraperdistributed-scrapinggolang
    Voir sur GitHub↗3,192
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Explorer les sous-tags

  • High-Throughput Event ProcessingProcesses hundreds of thousands of events per second from HTTP, message queues, and streaming sources with automatic scaling. **Distinct from Serverless Processing:** Distinct from Serverless Processing: adds explicit high-throughput performance characteristics to the serverless processing model.
  • High-Throughput ProcessingProcesses large volumes of streaming data and events in real time with sub-second latency using serverless functions. **Distinct from Serverless Processing:** Distinct from Serverless Processing: adds explicit high-throughput performance characteristics to the serverless processing model.