Elixir Data Pipeline Tools

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Browse free open source Elixir Data Pipeline Tools and projects below. Use the toggles on the left to filter open source Elixir Data Pipeline Tools by OS, license, language, programming language, and project status.

  • MongoDB Atlas runs apps anywhere Icon
    MongoDB Atlas runs apps anywhere

    Deploy in 115+ regions with the modern database for every enterprise.

    MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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  • Lightspeed golf course management software Icon
    Lightspeed golf course management software

    Lightspeed Golf is all-in-one golf course management software to help courses simplify operations, drive revenue and deliver amazing golf experiences.

    From tee sheet management, point of sale and payment processing to marketing, automation, reporting and more—Lightspeed is built for the pro shop, restaurant, back office, beverage cart and beyond.
    Learn More
  • 1
    GenStage

    GenStage

    Producer and consumer actors with back-pressure for Elixir

    GenStage is a specification and set of behaviours for building demand-driven data pipelines on the BEAM. It formalizes the roles of producers, consumers, and producer-consumers, using back-pressure so that fast producers don’t overwhelm downstream stages. Developers implement callbacks like handle_demand and handle_events to control how items are emitted, transformed, and consumed across asynchronous boundaries. Because stages are OTP processes, you gain fault tolerance, supervised restarts, and concurrency tuned via configurable demand and partitioning. GenStage underpins higher-level libraries like Flow and Broadway, but it can also be used directly for custom pipelines where timing and throughput matter. Its clear separation of concerns encourages testable, composable stages that can be rearranged as requirements evolve. In production, this leads to predictable, resilient dataflows for event ingestion, batching, and parallel processing.
    Downloads: 0 This Week
    Last Update:
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