Apache Hop vs Azure Data Factory

Apache Hop8.9Visit Azure Data Factory8.8Visit

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

vs

Azure Data Factory #6 in ETL Software 8.8/10 Paid ✓ 3 of 3 features Visit site

• Apache Hop fits best

  Open-source visual ETL teams

• Azure Data Factory fits best

  Cloud ETL teams in mixed environments

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Side by side

Feature	Apache Hop 8.9/10 Visit ↗	Azure Data Factory 8.8/10 Visit ↗
At a glance
Editor score	8.9	8.8
Ranking	#5 in ETL Software	#6 in ETL Software
Best for	Open-source visual ETL teams	Cloud ETL teams in mixed environments
Pricing model	Free	Paid
Starting price	Free plan	Not checked yet
Free plan	✓ (best)	—
Free trial	—	—
Deployment	Cloud, Self-hosted, Desktop	Cloud, Self-hosted
Platforms	Windows, macOS, Linux	Web
Support	Community, Docs	Docs
Integrations	20 integrations	90+ integrations
Built for	Small business, Mid-market, Enterprise	Mid-market, Enterprise
Features Apache Hop 3/3 · Azure Data Factory 3/3
Incremental loading	✓	✓
Change data capture	✓	✓
Custom code transforms	✓	✓
Specs
Deployment	Hybrid	Hybrid
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	More than 90 built-in connectors across cloud, on-premises, and multicloud sources Visual pipelines, mapping data flows, scheduling, monitoring, and alerting Supports incremental loading, CDC, self-hosted runtimes, and SSIS migration
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	Usage-based pricing varies by region and currency CDC factory resources are documented as public preview Its broad feature set may exceed the needs of simple ETL projects
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 →	Azure Data Factory is a fully managed, serverless service for building ETL and ELT pipelines. It is designed for teams moving and transforming data across cloud, on-premises, hybrid, and multicloud environments. Visual pipeline authoring,… 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

• Azure Data Factory — where it wins

  • More than 90 built-in connectors across cloud, on-premises, and multicloud sources
  • Visual pipelines, mapping data flows, scheduling, monitoring, and alerting
  • Supports incremental loading, CDC, self-hosted runtimes, and SSIS migration

  Where it doesn't

  • Usage-based pricing varies by region and currency
  • CDC factory resources are documented as public preview
  • Its broad feature set may exceed the needs of simple ETL projects

• 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 →
• Azure Data Factory8.8/10 · Paid

  A visual, hybrid ETL service with broad connectivity and enterprise orchestration.

  Visit siteFull verdict →