JavaScript Data Pipeline Tools

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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.

  • Train ML Models With SQL You Already Know Icon
    Train ML Models With SQL You Already Know

    BigQuery automates data prep, analysis, and predictions with built-in AI assistance.

    Build and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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  • Build Agents and Models on One Platform Icon
    Build Agents and Models on One Platform

    Everything you need to build production-ready agents and models. Access 200+ Google and third-party AI models and tools.

    Gemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
    Start Free
  • 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: 1,071 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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