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Head-to-head · DataOps Platforms

Apache Airflow vs Apache Hop

  • Updated Oct 2026
  • Both researched from official sources
  • 1 check side by side
Higher score Apache Airflow #1 in DataOps Platforms 9.3/10 Free plan Free plan✓ 0 of 4 features Visit Apache Airflow
Apache Hop #3 in DataOps Platforms 9.0/10 Free plan Free plan✓ 0 of 4 features Visit Apache Hop

Apache Airflow leads on 0 checks, Apache Hop on 0, and 1 is even. Who comes out ahead on the 1 yes/no, price and count check where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreApache Airflow · 9.3/10
  • Free planboth

Apache Airflow scores higher on our rubric for dataops platforms: 9.3 against 9.0 out of 10; our editors rank them #1 and #3.

Apache Airflow is the better fit for teams wanting proven Python workflow orchestration. Apache Hop is the better fit for teams building visual ETL and data pipelines.

  • Apache Airflow fits best

    Teams wanting proven Python workflow orchestration

  • Apache Hop fits best

    Teams building visual ETL and data pipelines

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Side by side

Feature Apache Airflow 9.3/10 Visit ↗ Apache Hop 9.0/10 Visit ↗
At a glance
Editor score 9.3 9.0
Ranking #1 in DataOps Platforms #3 in DataOps Platforms
Best for Teams wanting proven Python workflow orchestration Teams building visual ETL and data pipelines
Pricing model Free Free
Starting price Free plan Free plan
Free plan ✓ ✓
Free trial — —
Deployment Self-hosted Cloud, Self-hosted, Desktop
Platforms Web Windows, macOS, Linux
Support Community, Docs Community, Docs
Built for Small business, Mid-market, Enterprise Small business, Mid-market, Enterprise
Features Apache Airflow 0/4 · Apache Hop 0/4
Workflow scheduling Not checked yet Not checked yet
Pipeline monitoring Not checked yet Not checked yet
Data quality checks Not checked yet Not checked yet
Version control Not checked yet Not checked yet
Specs
Deployment options Not checked yet Not checked yet
Supported integrations Not checked yet Not checked yet
Our review
Pros
  • Author DAGs in Python and define task dependencies.
  • Automatic retries and configurable retry policies help manage failures.
  • Monitor logs, task status, and execution through a web UI.
  • 250+ transforms, 80+ workflow actions, and metadata-driven design
  • Runs locally, on servers, containers, Spark, Flink, and Dataflow
  • Supports CDC plus 40+ database dialects and named warehouse connectors
Cons
  • Deployment is self-hosted, so teams manage the environment.
  • Workflows are authored in Python rather than a visual builder.
  • Support channels are community and documentation.
  • Deployment spans several runtimes, adding architecture decisions
  • Scripting transforms require JavaScript, Java, Groovy, or Python skills
  • Support channels are centered on community resources and documentation
Our verdict

Apache Airflow is an open-source workflow orchestration platform for teams building data pipelines, machine-learning workflows, infrastructure automation, and other batch-oriented processes. Workflows are defined as Python-based directed…

Read the review →

Apache Hop is an open-source data orchestration, engineering, integration, and ETL platform from the Apache Software Foundation. Its visual environment is designed for data engineers and teams that need repeatable pipelines and workflows…

Read the review →
  1. Apache AirflowDataOps Platforms 9.3Free plan
  2. Apache HopDataOps Platforms 9.0Free plan

Strengths and trade-offs

  • Apache Airflow — where it wins

    • Author DAGs in Python and define task dependencies.
    • Automatic retries and configurable retry policies help manage failures.
    • Monitor logs, task status, and execution through a web UI.

    Where it doesn't

    • Deployment is self-hosted, so teams manage the environment.
    • Workflows are authored in Python rather than a visual builder.
    • Support channels are community and documentation.

  • Apache Hop — where it wins

    • 250+ transforms, 80+ workflow actions, and metadata-driven design
    • Runs locally, on servers, containers, Spark, Flink, and Dataflow
    • Supports CDC plus 40+ database dialects and named warehouse connectors

    Where it doesn't

    • Deployment spans several runtimes, adding architecture decisions
    • Scripting transforms require JavaScript, Java, Groovy, or Python skills
    • Support channels are centered on community resources and documentation

More comparisons

All DataOps platforms comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update