Google Cloud Dataflow vs AWS Glue DataBrew

Google Cloud Dataflow8.5Visit AWS Glue DataBrew8.4Visit

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

vs

AWS Glue DataBrew #8 in Data Integration 8.4/10 Paid ✓ 0 of 1 features Visit AWS DataBrew

• Google Cloud Dataflow fits best

  Google Cloud teams running batch and streaming pipelines

• AWS Glue DataBrew fits best

  AWS teams preparing and scheduling datasets

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Side by side

Feature	Google Cloud Dataflow 8.5/10 Visit ↗	AWS Glue DataBrew 8.4/10 Visit ↗
At a glance
Editor score	8.5	8.4
Ranking	#7 in Data Integration	#8 in Data Integration
Best for	Google Cloud teams running batch and streaming pipelines	AWS teams preparing and scheduling datasets
Pricing model	Paid	Paid
Starting price	Not checked yet	$0.48/unit usage-based
Free plan	—	—
Free trial	—	—
Deployment	Cloud	Cloud
Platforms	Web	Web
Support	Docs, Community, Live chat, Phone, Tickets	Docs
Integrations	14 integrations	7 integrations
Built for	Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features Google Cloud Dataflow 1/1 · AWS Glue DataBrew 0/1
Change data capture	✓ (best)	Not checked yet
Specs
Deployment	Cloud	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	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	Point-and-click profiling, anomaly detection, and data preparation More than 250 built-in transformations and reusable, versioned recipes Visual lineage and scheduled jobs with outputs to Amazon S3 and supported stores
Cons	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	Focused on data preparation rather than broad integration Usage-based charges apply to interactive sessions and jobs AWS-oriented ecosystem may not fit teams working across other platforms
Our verdict	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 →	AWS Glue DataBrew is a cloud-based, visual data-preparation service for analysts and data scientists working with datasets for analytics or machine learning. Its point-and-click interface supports profiling, cleaning, normalizing, and… Read the review →

Strengths and trade-offs

• 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

• AWS Glue DataBrew — where it wins

  • Point-and-click profiling, anomaly detection, and data preparation
  • More than 250 built-in transformations and reusable, versioned recipes
  • Visual lineage and scheduled jobs with outputs to Amazon S3 and supported stores

  Where it doesn't

  • Focused on data preparation rather than broad integration
  • Usage-based charges apply to interactive sessions and jobs
  • AWS-oriented ecosystem may not fit teams working across other platforms

• Google Cloud Dataflow8.5/10 · Paid

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

  Visit Google CloudFull verdict →
• AWS Glue DataBrew8.4/10 · Paid

  A visual AWS service for preparing and scheduling datasets, not broad data integration.

  Visit AWS DataBrewFull verdict →