JavaScript Data Pipeline Tools

View 114 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.

  • 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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  • Grafana: The open and composable observability platform Icon
    Grafana: The open and composable observability platform

    Faster answers, predictable costs, and no lock-in built by the team helping to make observability accessible to anyone.

    Grafana is the open source analytics & monitoring solution for every database.
    Learn More
  • 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/
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    Downloads: 2,380 This Week
    Last Update:
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  • 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:
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