Apache Beam vs Apache Spark

Apache Beam8.7Visit Apache Spark8.6Visit

Higher score Apache Beam #7 in Stream Processing Software 8.7/10 Open source ✓ 0 of 3 features Visit Apache Beam

vs

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

• Apache Beam fits best

  Portable batch and streaming pipelines

• Apache Spark fits best

  Mature teams combining batch and streaming

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Side by side

Feature	Apache Beam 8.7/10 Visit ↗	Apache Spark 8.6/10 Visit ↗
At a glance
Editor score	8.7	8.6
Ranking	#7 in Stream Processing Software	#8 in Stream Processing Software
Best for	Portable batch and streaming pipelines	Mature teams combining batch and streaming
Pricing model	Free	Free
Starting price	Open source	Free plan
Free plan	Not checked yet	✓ (best)
Free trial	—	—
Deployment	Cloud, Self-hosted	Self-hosted
Platforms	Web, Windows, macOS, Linux	Windows, macOS, Linux
Support	Community, Docs	Community, Docs
Integrations	15 integrations	20 integrations
Built for	Small business, Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features Apache Beam 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	Unifies batch and streaming pipelines with event-time windowing Supports Java, Python, Go, SQL, and cross-language pipelines Runs through Flink, Spark, Dataflow, or the local Direct Runner	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	Execution depends on selecting and operating a compatible runner CDC capabilities vary by connector and runner Requires developer-led pipeline construction rather than hosted ETL workflows	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	Apache Beam is an open-source programming model for building batch and streaming data-processing pipelines. It is aimed at developers and data teams working across local, on-premises, hybrid, and managed cloud environments. Teams can… 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

• Apache Beam — where it wins

  • Unifies batch and streaming pipelines with event-time windowing
  • Supports Java, Python, Go, SQL, and cross-language pipelines
  • Runs through Flink, Spark, Dataflow, or the local Direct Runner

  Where it doesn't

  • Execution depends on selecting and operating a compatible runner
  • CDC capabilities vary by connector and runner
  • Requires developer-led pipeline construction rather than hosted ETL workflows

• 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

• Apache Beam8.7/10 · Open source

  A portable framework for teams building batch and streaming pipelines across multiple runners.

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

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

  Visit Apache SparkFull verdict →