Apache Hop vs Google Cloud Dataflow

Apache Hop9.0Visit Google Cloud Dataflow8.5Visit

Higher score Apache Hop #3 in Data Integration 9.0/10 Free plan Free plan✓ 1 of 1 features Visit Apache Hop

vs

Google Cloud Dataflow #7 in Data Integration 8.5/10 Paid ✓ 1 of 1 features Visit Google Cloud

• Apache Hop fits best

  Teams wanting free, flexible pipeline engineering

• Google Cloud Dataflow fits best

  Google Cloud teams running batch and streaming pipelines

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Side by side

Feature	Apache Hop 9.0/10 Visit ↗	Google Cloud Dataflow 8.5/10 Visit ↗
At a glance
Editor score	9.0	8.5
Ranking	#3 in Data Integration	#7 in Data Integration
Best for	Teams wanting free, flexible pipeline engineering	Google Cloud teams running batch and streaming pipelines
Pricing model	Free	Paid
Starting price	Free plan	Not checked yet
Free plan	✓ (best)	—
Free trial	—	—
Deployment	Cloud, Self-hosted, Desktop	Cloud
Platforms	Windows, macOS, Linux	Web
Support	Community, Docs	Docs, Community, Live chat, Phone, Tickets
Integrations	20 integrations	14 integrations
Built for	Small business, Mid-market, Enterprise	Mid-market, Enterprise
Features Apache Hop 1/1 · Google Cloud Dataflow 1/1
Change data capture	✓	✓
Specs
Deployment	Hybrid	Cloud
Prebuilt connectors	Not checked yet	Not checked yet
Sync frequency	Not checked yet	Not checked yet
Data transformations	Not checked yet	Not checked yet
Destination types	Not checked yet	Not checked yet
Our review
Pros	Visual pipeline and workflow design with metadata-driven configuration 250+ transforms, 80+ workflow actions, and 40+ database dialects Run locally or on server, Spark, Flink, and Dataflow runtimes	Runs batch and streaming Apache Beam pipelines with managed worker infrastructure Offers templates, CDC workflows, and managed I/O connectors Autoscaling and monitoring support ongoing pipeline operations
Cons	Pipeline development is aimed at data engineers and integration teams Support channels are community and documentation Deployment choices include desktop, self-hosted, and cloud environments	Usage-based pricing can make costs less predictable than fixed tiers Requires engineering work to build and manage pipeline logic Its focus is pipeline execution, not end-to-end integration design
Our verdict	Apache Hop is an open-source data integration, engineering, and orchestration platform for teams building repeatable pipelines and workflows. Its visual development environment combines metadata-driven configuration with a broad set of… Read the review →	Google Cloud Dataflow is a managed service for running Apache Beam pipelines that process batch and streaming data. It is aimed at engineering and data teams building data processing and integration workflows on Google Cloud, particularly… Read the review →

Strengths and trade-offs

• Apache Hop — where it wins

  • Visual pipeline and workflow design with metadata-driven configuration
  • 250+ transforms, 80+ workflow actions, and 40+ database dialects
  • Run locally or on server, Spark, Flink, and Dataflow runtimes

  Where it doesn't

  • Pipeline development is aimed at data engineers and integration teams
  • Support channels are community and documentation
  • Deployment choices include desktop, self-hosted, and cloud environments

• Google Cloud Dataflow — where it wins

  • Runs batch and streaming Apache Beam pipelines with managed worker infrastructure
  • Offers templates, CDC workflows, and managed I/O connectors
  • Autoscaling and monitoring support ongoing pipeline operations

  Where it doesn't

  • Usage-based pricing can make costs less predictable than fixed tiers
  • Requires engineering work to build and manage pipeline logic
  • Its focus is pipeline execution, not end-to-end integration design

• Apache Hop9.0/10 · Free plan

  Flexible pipeline engineering across databases, scripting options, and execution runtimes.

  Visit Apache HopFull verdict →
• Google Cloud Dataflow8.5/10 · Paid

  A managed Google Cloud service for teams building batch and streaming pipelines.

  Visit Google CloudFull verdict →