JavaScript Data Pipeline Tools

View 119 business solutions

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

  • Full-stack observability with actually useful AI | Grafana Cloud Icon
    Full-stack observability with actually useful AI | Grafana Cloud

    Our generous forever free tier includes the full platform, including the AI Assistant, for 3 users with 10k metrics, 50GB logs, and 50GB traces.

    Built on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
    Create free account
  • $300 in Free Credit Towards Top Cloud Services Icon
    $300 in Free Credit Towards Top Cloud Services

    Build VMs, containers, AI, databases, storage—all in one place.

    Start your project in minutes. After credits run out, 20+ products include free monthly usage. Only pay when you're ready to scale.
    Get Started
  • 1
    Pentaho

    Pentaho

    Pentaho offers comprehensive data integration and analytics platform.

    Pentaho couples data integration with business analytics in a modern platform to easily access, visualize and explore data that impacts business results. Use it as a full suite or as individual components that are accessible on-premise, in the cloud, or on-the-go (mobile). Pentaho enables IT and developers to access and integrate data from any source and deliver it to your applications all from within an intuitive and easy to use graphical tool. The Pentaho Enterprise Edition Free Trial can be obtained from https://pentaho.com/download/
    Leader badge
    Downloads: 1,267 This Week
    Last Update:
    See Project
  • 2
    CueLake

    CueLake

    Use SQL to build ELT pipelines on a data lakehouse

    With CueLake, you can use SQL to build ELT (Extract, Load, Transform) pipelines on a data lakehouse. You write Spark SQL statements in Zeppelin notebooks. You then schedule these notebooks using workflows (DAGs). To extract and load incremental data, you write simple select statements. CueLake executes these statements against your databases and then merges incremental data into your data lakehouse (powered by Apache Iceberg). To transform data, you write SQL statements to create views and tables in your data lakehouse. CueLake uses Celery as the executor and celery-beat as the scheduler. Celery jobs trigger Zeppelin notebooks. Zeppelin auto-starts and stops the Spark cluster for every scheduled run of notebooks.
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next
MongoDB Logo MongoDB