Apache Beam vs Apache Spark: Key Differences & When to Use Each

Comprehensive side-by-side comparison of features, pricing, and metrics

Key Differences

Compare Apache Beam and Apache Spark across features, pricing, integrations, and community metrics. Apache Beam / Apache Spark.

Feature

Apache Beam

Data Processing

Apache Spark

Data Processing

Side-by-side comparison of developer tools

Unified programming model for batch and streaming

Unified analytics engine for large-scale data processing

GitHub Stars

⭐ 8,575

⭐ 43,233

Contributors

👥 1,891

👥 3,432

Pricing

✓ Free

Enterprise: Contact sales

✓ Free

Enterprise: Contact sales

Languages

Java

Scala

Features

• Batch
• Beam
• Big Data
• Golang
• Java

• Big Data
• Java
• Jdbc
• Python
• R

Integrations

No integrations listed

Momentum Score

79/100 (stable)

Community Health

73/100 (good)

91/100 (excellent)

Maturity Index

63/100 (growing)

90/100 (mature)

Innovation Score

59/100 (progressive)

91/100 (pioneering)

Risk Score (higher is safer)

82/100 (minimal)

94/100 (minimal)

Developer Experience

54/100 (needs-improvement)

80/100 (good)

Links

🌐 Visit Website View on GitHub

Apache Beam Strengths

Apache Spark Strengths

✓ More popular (43,233 stars)
✓ Larger community (3,432 contributors)

When to Use Apache Beam vs Apache Spark

Use Apache Beam when its strengths align better with your stack and team needs, and choose Apache Spark when its ecosystem, integrations, or cost profile is a better fit.

Related comparisons

Apache Beam vs Apache Flink → Apache Spark vs Apache Flink → Apache Beam vs Elasticsearch → Apache Spark vs Elasticsearch → Apache Beam vs Redis → Apache Spark vs Redis → Apache Beam vs Grafana → Apache Spark vs Grafana → Apache Beam vs Prometheus → Apache Spark vs Prometheus → Apache Beam vs ClickHouse → Apache Spark vs ClickHouse →

More Comparisons

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Data source: GitHub API

Last updated: 5/4/2026