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

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

Key Differences

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

Feature

Apache Spark

Data Processing

Side-by-side comparison of developer tools
Distributed RESTful search and analytics engine
Unified analytics engine for large-scale data processing
GitHub Stars
⭐ 76,630
⭐ 43,233
Contributors
👥 2,471
👥 3,432
Pricing
✓ Free
Enterprise: Contact sales
✓ Free
Enterprise: Contact sales
Languages
Java
Scala
Features
  • Elasticsearch
  • Java
  • Search Engine
  • Big Data
  • Java
  • Jdbc
  • Python
  • R
Integrations
  • • elasticsearch
No integrations listed
Momentum Score
66/100 (stable)
79/100 (stable)
Community Health
95/100 (excellent)
91/100 (excellent)
Maturity Index
93/100 (mature)
90/100 (mature)
Innovation Score
65/100 (progressive)
91/100 (pioneering)
Risk Score (higher is safer)
87/100 (minimal)
94/100 (minimal)
Developer Experience
54/100 (needs-improvement)
80/100 (good)
Links

Elasticsearch Strengths

  • ✓ More popular (76,630 stars)

Apache Spark Strengths

  • ✓ Larger community (3,432 contributors)
  • ✓ More features (5 listed)

When to Use Elasticsearch vs Apache Spark

Use Elasticsearch 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.

Data source: GitHub API

Last updated: 5/4/2026