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

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

Key Differences

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

Feature

Apache Kafka

Messaging

Apache Spark

Data Processing

Side-by-side comparison of developer tools
Distributed streaming platform
Unified analytics engine for large-scale data processing
GitHub Stars
⭐ 32,505
⭐ 43,233
Contributors
👥 1,675
👥 3,432
Pricing
✓ Free
Enterprise: Contact sales
✓ Free
Enterprise: Contact sales
Languages
Java
Scala
Features
  • • Java
  • • Kafka
  • • Scala
  • • Streaming
  • • Big Data
  • • Java
  • • Jdbc
  • • Python
  • • R
Integrations
  • • kafka
No integrations listed
Momentum Score
89/100 (stable)
79/100 (stable)
Community Health
85/100 (excellent)
91/100 (excellent)
Maturity Index
82/100 (established)
90/100 (mature)
Innovation Score
70/100 (innovative)
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
🌐 Visit Website View on GitHub

Apache Kafka Strengths

Apache Spark Strengths

  • ✓ More popular (43,233 stars)
  • ✓ Larger community (3,432 contributors)
  • ✓ More features (5 listed)

When to Use Apache Kafka vs Apache Spark

Use Apache Kafka 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 Kafka vs NATS → Apache Spark vs Apache Flink → Apache Kafka vs Apache Pulsar → Apache Spark vs Apache Beam → Apache Kafka vs RabbitMQ → Apache Spark vs Elasticsearch → Apache Kafka vs ZeroMQ → Apache Spark vs Redis → Apache Kafka vs ActiveMQ → Apache Spark vs Grafana → Apache Kafka vs Elasticsearch → Apache Spark vs Prometheus →

More Comparisons

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Data source: GitHub API

Last updated: 5/4/2026