Best Data Pipeline Software for Stonebranch

Find and compare the best Data Pipeline software for Stonebranch in 2024

Use the comparison tool below to compare the top Data Pipeline software for Stonebranch on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    Apache Kafka Reviews

    Apache Kafka

    The Apache Software Foundation

    1 Rating
    Apache Kafka®, is an open-source distributed streaming platform.
  • 2
    dbt Reviews

    dbt

    dbt Labs

    $50 per user per month
    Data teams can collaborate as software engineering teams by using version control, quality assurance, documentation, and modularity. Analytics errors should be treated as serious as production product bugs. Analytic workflows are often manual. We believe that workflows should be designed to be executed with one command. Data teams use dbt for codifying business logic and making it available to the entire organization. This is useful for reporting, ML modeling and operational workflows. Built-in CI/CD ensures data model changes are made in the correct order through development, staging, production, and production environments. dbt Cloud offers guaranteed uptime and custom SLAs.
  • 3
    Fivetran Reviews
    Fivetran is the smartest method to replicate data into your warehouse. Our zero-maintenance pipeline is the only one that allows for a quick setup. It takes months of development to create this system. Our connectors connect data from multiple databases and applications to one central location, allowing analysts to gain profound insights into their business.
  • 4
    Apache Airflow Reviews

    Apache Airflow

    The Apache Software Foundation

    Airflow is a community-created platform that allows programmatically to schedule, author, and monitor workflows. Airflow is modular in architecture and uses a message queue for managing a large number of workers. Airflow can scale to infinity. Airflow pipelines can be defined in Python to allow for dynamic pipeline generation. This allows you to write code that dynamically creates pipelines. You can easily define your own operators, and extend libraries to suit your environment. Airflow pipelines can be both explicit and lean. The Jinja templating engine is used to create parametrization in the core of Airflow pipelines. No more XML or command-line black-magic! You can use standard Python features to create your workflows. This includes date time formats for scheduling, loops to dynamically generate task tasks, and loops for scheduling. This allows you to be flexible when creating your workflows.
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