Google Cloud Data Fusion vs Apache Spark

Google Cloud Data Fusion8.7Visit Apache Spark8.2Visit

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

vs

Apache Spark #12 in ETL Software 8.2/10 Free plan Free plan✓ 3 of 3 features Visit Apache Spark

• Google Cloud Data Fusion fits best

  Google Cloud integration teams

• Apache Spark fits best

  Large-scale open-source data processing

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Side by side

Feature	Google Cloud Data Fusion 8.7/10 Visit ↗	Apache Spark 8.2/10 Visit ↗
At a glance
Editor score	8.7	8.2
Ranking	#7 in ETL Software	#12 in ETL Software
Best for	Google Cloud integration teams	Large-scale open-source data processing
Pricing model	Paid	Free
Starting price	$0.35 usage-based	Free plan
Free plan	—	✓ (best)
Free trial	—	—
Deployment	Cloud	Self-hosted
Platforms	Web	Windows, macOS, Linux
Support	Docs	Community, Docs
Integrations	20 integrations	20 integrations
Built for	Small business, Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features Google Cloud Data Fusion 3/3 · Apache Spark 3/3
Incremental loading	✓	✓
Change data capture	✓	✓
Custom code transforms	✓	✓
Specs
Deployment	Cloud	Self_hosted
Source connectors	Not checked yet	Not checked yet
Destination connectors	Not checked yet	Not checked yet
Transformation mode	Mixed	Mixed
Our review
Pros	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	Distributed batch and streaming processing with Structured Streaming Python, SQL, Scala, Java, and R interfaces Broad connectivity across files, databases, warehouses, and data platforms
Cons	No free plan is listed Google Cloud resource execution costs are billed separately Enterprise features such as RBAC and regional HA cost more	Self-hosted deployment requires cluster and infrastructure management More technical than hosted ETL tools for non-developer teams No subscription tiers or packaged hosted SaaS experience
Our verdict	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 →	Apache Spark is an open-source distributed computing engine for large-scale data processing and analytics. It is designed for developers, data engineers, analysts, and organizations running workloads across clusters. Spark handles… Read the review →

Strengths and trade-offs

• 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 Spark — where it wins

  • Distributed batch and streaming processing with Structured Streaming
  • Python, SQL, Scala, Java, and R interfaces
  • Broad connectivity across files, databases, warehouses, and data platforms

  Where it doesn't

  • Self-hosted deployment requires cluster and infrastructure management
  • More technical than hosted ETL tools for non-developer teams
  • No subscription tiers or packaged hosted SaaS experience

• 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 →
• Apache Spark8.2/10 · Free plan

  A free, self-hosted engine for distributed batch, streaming, SQL, and machine learning workloads.

  Visit Apache SparkFull verdict →