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Head-to-head · DataOps Platforms

Apache Hop vs Dagster

  • Updated Oct 2026
  • Both researched from official sources
  • 1 check side by side
Higher score Apache Hop #3 in DataOps Platforms 9.0/10 Free plan Free plan✓ 0 of 4 features Visit Apache Hop
Dagster #8 in DataOps Platforms 8.5/10 Free plan · 30-day trial Free planFree trial✓ 0 of 4 features Visit Dagster

Apache Hop leads on 0 checks, Dagster 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 Hop · 9.0/10
  • Free planboth
  • Free trialonly Dagster

Apache Hop scores higher on our rubric for dataops platforms: 9.0 against 8.5 out of 10; our editors rank them #3 and #8.

Apache Hop is the better fit for teams building visual ETL and data pipelines. Dagster is the better fit for data teams focused on assets, lineage, and quality.

  • Apache Hop fits best

    Teams building visual ETL and data pipelines

  • Dagster fits best

    Data teams focused on assets, lineage, and quality

Advertiser disclosure: iTechGuides is reader-supported. Vendors can pay for top positions in our rankings and for a place on other products' pages, and we may earn a commission when you click some links. Bought places stand at the number bought; this page and our disclosure say so. Paying vendors may get a fuller, more detailed review from our editors, and that review can change their score. How we rank.

Side by side

Feature Apache Hop 9.0/10 Visit ↗ Dagster 8.5/10 Visit ↗
At a glance
Editor score 9.0 8.5
Ranking #3 in DataOps Platforms #8 in DataOps Platforms
Best for Teams building visual ETL and data pipelines Data teams focused on assets, lineage, and quality
Pricing model Free Free plan + paid
Starting price Free plan $10/mo
Free plan ✓ ✓
Free trial — 30 days
Deployment Cloud, Self-hosted, Desktop Cloud, Self-hosted
Platforms Windows, macOS, Linux Web
Support Community, Docs Community, Docs, Live chat
Integrations 20 integrations 13 integrations
Built for Small business, Mid-market, Enterprise Solo, Small business, Mid-market, Enterprise
Features Apache Hop 0/4 · Dagster 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
  • 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
  • Tracks asset dependencies and lineage with freshness and quality checks
  • Supports schedules, event-driven sensors, retries, partitions, and backfills
  • Integrates with dbt, Snowflake, Fivetran, Spark, and Databricks
Cons
  • 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
  • Solo is limited to 1 user, 1 code location, and 1 deployment
  • Starter costs $100/month and supports up to 3 users
  • Pro requires a sales conversation, and usage-based charges can apply
Our verdict

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 →

Dagster is a data orchestration framework and platform for data engineering teams building analytics, machine learning, AI, ingestion, and transformation workflows. Its asset-based model connects dependencies, lineage, metadata, execution,…

Read the review →
  1. Apache HopDataOps Platforms 9.0Free plan
  2. DagsterDataOps Platforms 8.5Free plan · 30-day trial

Strengths and trade-offs

  • 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

  • Dagster — where it wins

    • Tracks asset dependencies and lineage with freshness and quality checks
    • Supports schedules, event-driven sensors, retries, partitions, and backfills
    • Integrates with dbt, Snowflake, Fivetran, Spark, and Databricks

    Where it doesn't

    • Solo is limited to 1 user, 1 code location, and 1 deployment
    • Starter costs $100/month and supports up to 3 users
    • Pro requires a sales conversation, and usage-based charges can apply

More comparisons

All DataOps platforms comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update