Amazon SageMaker Data Wrangler vs Apache Hop

Amazon SageMaker Data Wrangler9.0Visit Apache Hop8.7Visit

Higher score Amazon SageMaker Data Wrangler #3 in Data Preparation for Analytics 9.0/10 Paid ✓ 1 of 3 features Visit AWS

vs

Apache Hop #6 in Data Preparation for Analytics 8.7/10 Free plan Free plan✓ 0 of 3 features Visit Apache Hop

• Amazon SageMaker Data Wrangler fits best

  AWS analytics teams building SageMaker workflows

• Apache Hop fits best

  Teams wanting free, flexible visual pipelines

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Side by side

Feature	Amazon SageMaker Data Wrangler 9.0/10 Visit ↗	Apache Hop 8.7/10 Visit ↗
At a glance
Editor score	9.0	8.7
Ranking	#3 in Data Preparation for Analytics	#6 in Data Preparation for Analytics
Best for	AWS analytics teams building SageMaker workflows	Teams wanting free, flexible visual pipelines
Pricing model	Paid	Free
Starting price	—	Free plan
Free plan	—	✓ (best)
Free trial	—	—
Deployment	Cloud	Cloud, Self-hosted, Desktop
Platforms	Web	Windows, macOS, Linux
Support	Docs	Community, Docs
Integrations	10 integrations	20 integrations
Built for	Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features Amazon SageMaker Data Wrangler 1/3 · Apache Hop 0/3
Data quality checks	Not checked yet	Not checked yet
Scheduled runs	✓ (best)	Not checked yet
Version control	Not checked yet	Not checked yet
Specs
Deployment	Cloud	Hybrid
Workflow interface	Not checked yet	Not checked yet
Source connectors	Not checked yet	Not checked yet
Our review
Pros	Visual flows pair 300+ transformations with data-quality and insights reports. Custom transformations support Python, PySpark, SQL, and Pandas. Exports connect flows to SageMaker Pipelines, Feature Store, notebooks, and scripts.	Visual pipeline and workflow design with metadata-driven configuration 250+ transforms, 80+ workflow actions, and 40+ database dialects Runs locally or on server, Spark, Flink, and Dataflow
Cons	Usage-based compute, storage, and processing costs can be harder to predict. Its SageMaker focus is a narrower fit for teams outside the AWS ecosystem. The published support channel is documentation.	ETL and orchestration focus is less tailored to interactive data preparation Broad runtime choices can add complexity to selecting a deployment approach Support is through community channels and documentation
Our verdict	Amazon SageMaker Data Wrangler helps machine learning teams prepare tabular, time-series, text, and image data in the cloud. Its visual flows support combining datasets, profiling and cleaning data, applying transformations, and analyzing… Read the review →	Apache Hop is an open-source platform for designing data pipelines and workflows, aimed at data engineers and teams building repeatable integration and orchestration processes. Its visual development environment and metadata-driven… Read the review →

Strengths and trade-offs

• Amazon SageMaker Data Wrangler — where it wins

  • Visual flows pair 300+ transformations with data-quality and insights reports.
  • Custom transformations support Python, PySpark, SQL, and Pandas.
  • Exports connect flows to SageMaker Pipelines, Feature Store, notebooks, and scripts.

  Where it doesn't

  • Usage-based compute, storage, and processing costs can be harder to predict.
  • Its SageMaker focus is a narrower fit for teams outside the AWS ecosystem.
  • The published support channel is documentation.

• Apache Hop — where it wins

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

  Where it doesn't

  • ETL and orchestration focus is less tailored to interactive data preparation
  • Broad runtime choices can add complexity to selecting a deployment approach
  • Support is through community channels and documentation

• Amazon SageMaker Data Wrangler9.0/10 · Paid

  A visual preparation workflow with extensive transformations and direct SageMaker exports.

  Visit AWSFull verdict →
• Apache Hop8.7/10 · Free plan

  A flexible, free pipeline platform for teams building data integration workflows.

  Visit Apache HopFull verdict →