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

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

Key Differences

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

Feature

Grafana

Monitoring

Apache Spark

Data Processing

Side-by-side comparison of developer tools
The open and composable observability and data visualization platform
Unified analytics engine for large-scale data processing
GitHub Stars
⭐ 75,799
⭐ 43,717
Contributors
👥 3,071
👥 3,535
Pricing
✓ Free
Enterprise: Contact sales
✓ Free
Enterprise: Contact sales
Languages
TypeScript
Scala
Features
  • • Alerting
  • • Analytics
  • • Business Intelligence
  • • Dashboard
  • • Data Visualization
  • • Big Data
  • • Java
  • • Jdbc
  • • Python
  • • R
Integrations
  • • prometheus
  • • grafana
  • • elasticsearch
No integrations listed
Momentum Score
94/100 (slowing)
81/100 (stable)
Community Health
95/100 (excellent)
91/100 (excellent)
Maturity Index
89/100 (mature)
89/100 (mature)
Innovation Score
95/100 (pioneering)
91/100 (pioneering)
Risk Score (higher is safer)
94/100 (minimal)
94/100 (minimal)
Developer Experience
80/100 (good)
80/100 (good)
Links
🌐 Visit Website View on GitHub
🌐 Visit Website View on GitHub

Grafana Strengths

  • ✓ More popular (75,799 stars)

Apache Spark Strengths

  • ✓ Larger community (3,535 contributors)

When to Use Grafana vs Apache Spark

Use Grafana 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

Grafana vs Prometheus → Apache Spark vs Apache Flink → Grafana vs Loki → Apache Spark vs Apache Beam → Grafana vs Jaeger → Apache Spark vs Elasticsearch → Grafana vs Zipkin → Apache Spark vs Redis → Grafana vs VictoriaMetrics → Apache Spark vs Prometheus → Grafana vs Thanos → Apache Spark vs ClickHouse →

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Data source: GitHub API

Last updated: 7/28/2026