Elixir Data Pipeline Tools

View 123 business solutions

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.

  • Paessler: Easy to Use With Enterprise Power. Free Trial Icon
    Paessler: Easy to Use With Enterprise Power. Free Trial

    A low-code dashboard makes monitoring intuitive for any admin, while scripting and custom sensors give experts full control.

    You shouldn't have to choose between a monitoring tool that's easy to use and one that's powerful enough for a complex environment. PRTG's low-code interface lets any admin build dashboards, set alerts and monitor devices without scripting, while custom sensors and full API access are there when your team needs deeper control. One platform, no compromise. Download a free 30-day trial now.
    Get Free Download
  • Save Up to 91% on Cloud Compute With Spot VMs Icon
    Save Up to 91% on Cloud Compute With Spot VMs

    Automatic sustained-use discounts. One free VM per month. No negotiation needed.

    Run batch jobs at 60-91% off with Spot VMs. Long-running workloads get automatic discounts with sustained use.
    Start Free
  • 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:
    See Project
  • Previous
  • You're on page 1
  • Next