Apache Hop vs Google Cloud Data Fusion

Apache Hop8.9Visit Google Cloud Data Fusion8.7Visit

Higher score Apache Hop #5 in ETL Software 8.9/10 Free plan Free plan✓ 3 of 3 features Visit Apache Hop

vs

Google Cloud Data Fusion #7 in ETL Software 8.7/10 Paid ✓ 3 of 3 features Visit Google Cloud

• Apache Hop fits best

  Open-source visual ETL teams

• Google Cloud Data Fusion fits best

  Google Cloud integration teams

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Side by side

Feature	Apache Hop 8.9/10 Visit ↗	Google Cloud Data Fusion 8.7/10 Visit ↗
At a glance
Editor score	8.9	8.7
Ranking	#5 in ETL Software	#7 in ETL Software
Best for	Open-source visual ETL teams	Google Cloud integration teams
Pricing model	Free	Paid
Starting price	Free plan	$0.35 usage-based
Free plan	✓ (best)	—
Free trial	—	—
Deployment	Cloud, Self-hosted, Desktop	Cloud
Platforms	Windows, macOS, Linux	Web
Support	Community, Docs	Docs
Integrations	20 integrations	20 integrations
Built for	Small business, Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features Apache Hop 3/3 · Google Cloud Data Fusion 3/3
Incremental loading	✓	✓
Change data capture	✓	✓
Custom code transforms	✓	✓
Specs
Deployment	Hybrid	Cloud
Source connectors	Not checked yet	Not checked yet
Destination connectors	Not checked yet	Not checked yet
Transformation mode	Mixed	Mixed
Our review
Pros	250+ pipeline transforms and 80+ workflow actions Runs locally, on servers, containers, Spark, Flink, and Dataflow Supports CDC, scripting, Kubernetes Helm, and 40+ database dialects	Supports batch, real-time, CDC, and continuous replication pipelines Visual designer, Wrangler, lineage, triggers, and prebuilt plugins Connects Google Cloud, databases, storage, Kafka, and SaaS sources
Cons	Its breadth may exceed the needs of simple ETL jobs Multiple execution runtimes add architecture choices to manage Teams wanting a packaged paid tier need a different procurement model	No free plan is listed Google Cloud resource execution costs are billed separately Enterprise features such as RBAC and regional HA cost more
Our verdict	Apache Hop is an open-source data orchestration, engineering, integration, and ETL platform for data engineers and teams building repeatable integration workflows. Its visual environment supports pipeline and workflow design, while… Read the review →	Google Cloud Data Fusion is a fully managed, cloud-native data integration service for business users, developers, data scientists, and enterprise data teams. Its web-based visual pipeline designer supports batch and real-time workflows… Read the review →

Strengths and trade-offs

• Apache Hop — where it wins

  • 250+ pipeline transforms and 80+ workflow actions
  • Runs locally, on servers, containers, Spark, Flink, and Dataflow
  • Supports CDC, scripting, Kubernetes Helm, and 40+ database dialects

  Where it doesn't

  • Its breadth may exceed the needs of simple ETL jobs
  • Multiple execution runtimes add architecture choices to manage
  • Teams wanting a packaged paid tier need a different procurement model

• Google Cloud Data Fusion — where it wins

  • Supports batch, real-time, CDC, and continuous replication pipelines
  • Visual designer, Wrangler, lineage, triggers, and prebuilt plugins
  • Connects Google Cloud, databases, storage, Kafka, and SaaS sources

  Where it doesn't

  • No free plan is listed
  • Google Cloud resource execution costs are billed separately
  • Enterprise features such as RBAC and regional HA cost more

• Apache Hop8.9/10 · Free plan

  A broad, open-source ETL platform for teams that want visual design and runtime flexibility.

  Visit Apache HopFull verdict →
• Google Cloud Data Fusion8.7/10 · Paid

  A Google Cloud service for visual, extensible ETL with CDC, lineage, and hybrid connectivity.

  Visit Google CloudFull verdict →