Google Cloud Dataflow vs Apache Spark

Google Cloud Dataflow9.0Visit Apache Spark8.6Visit

Higher score Google Cloud Dataflow #4 in Stream Processing Software 9.0/10 Paid ✓ 0 of 3 features Visit Google Cloud

vs

Apache Spark #8 in Stream Processing Software 8.6/10 Free plan Free plan✓ 0 of 3 features Visit Apache Spark

• Google Cloud Dataflow fits best

  Google Cloud teams running managed pipelines

• Apache Spark fits best

  Mature teams combining batch and streaming

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Side by side

Feature	Google Cloud Dataflow 9.0/10 Visit ↗	Apache Spark 8.6/10 Visit ↗
At a glance
Editor score	9.0	8.6
Ranking	#4 in Stream Processing Software	#8 in Stream Processing Software
Best for	Google Cloud teams running managed pipelines	Mature teams combining batch and streaming
Pricing model	Paid	Free
Starting price	Not checked yet	Free plan
Free plan	—	✓ (best)
Free trial	—	—
Deployment	Cloud	Self-hosted
Platforms	Web	Windows, macOS, Linux
Support	Docs, Community, Live chat, Phone, Tickets	Community, Docs
Integrations	14 integrations	20 integrations
Built for	Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features Google Cloud Dataflow 0/3 · Apache Spark 0/3
SQL processing	Not checked yet	Not checked yet
Stateful processing	Not checked yet	Not checked yet
Event-time windows	Not checked yet	Not checked yet
Specs
Deployment model	Not checked yet	Not checked yet
Supported languages	Not checked yet	Not checked yet
Source connectors	Not checked yet	Not checked yet
Our review
Pros	Runs both batch and streaming pipelines with autoscaling Supports managed Beam connectors, CDC templates, SQL, and Python Connects with Google Cloud, databases, Kafka, and analytics tools	Structured Streaming supports incremental stream processing Broad APIs across Python, SQL, Scala, Java, and R Connects to major data sources, clusters, and lakehouse systems
Cons	Usage-based pricing does not provide fixed subscription tiers No free plan is listed Cloud deployment may not suit teams seeking self-hosted processing	Self-hosted deployment requires cluster infrastructure Streaming is one part of a broader batch-focused platform Not a hosted ETL SaaS product with subscription tiers
Our verdict	Google Cloud Dataflow is a managed service for running Apache Beam pipelines across batch and streaming workloads. It is aimed at engineering and data teams building scalable processing and integration workflows, with support for Java,… Read the review →	Apache Spark is an open-source distributed computing engine for large-scale data processing and analytics. It is aimed at developers, data engineers, analysts, and organizations processing data across clusters. Structured Streaming… Read the review →

Strengths and trade-offs

• Google Cloud Dataflow — where it wins

  • Runs both batch and streaming pipelines with autoscaling
  • Supports managed Beam connectors, CDC templates, SQL, and Python
  • Connects with Google Cloud, databases, Kafka, and analytics tools

  Where it doesn't

  • Usage-based pricing does not provide fixed subscription tiers
  • No free plan is listed
  • Cloud deployment may not suit teams seeking self-hosted processing

• Apache Spark — where it wins

  • Structured Streaming supports incremental stream processing
  • Broad APIs across Python, SQL, Scala, Java, and R
  • Connects to major data sources, clusters, and lakehouse systems

  Where it doesn't

  • Self-hosted deployment requires cluster infrastructure
  • Streaming is one part of a broader batch-focused platform
  • Not a hosted ETL SaaS product with subscription tiers

• Google Cloud Dataflow9.0/10 · Paid

  A broad managed service for Apache Beam pipelines, CDC, and cloud data integration.

  Visit Google CloudFull verdict →
• Apache Spark8.6/10 · Free plan

  A mature, broad engine for teams combining batch pipelines with incremental streaming.

  Visit Apache SparkFull verdict →