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

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

Key Differences

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

Feature

MongoDB

Database

Apache Spark

Data Processing

Side-by-side comparison of developer tools
NoSQL document database
Unified analytics engine for large-scale data processing
GitHub Stars
⭐ 28,519
⭐ 43,909
Contributors
👥 1,487
👥 3,575
Pricing
✓ Free
Enterprise: Contact sales
✓ Free
Enterprise: Contact sales
Languages
C++
Scala
Features
  • • C Plus Plus
  • • Database
  • • Mongodb
  • • Nosql
  • • Big Data
  • • Java
  • • Jdbc
  • • Python
  • • R
Integrations
  • • mongodb
No integrations listed
Momentum Score
71/100 (stable)
81/100 (stable)
Community Health
81/100 (good)
91/100 (excellent)
Maturity Index
70/100 (established)
89/100 (mature)
Innovation Score
58/100 (progressive)
91/100 (pioneering)
Risk Score (higher is safer)
75/100 (minimal)
94/100 (minimal)
Developer Experience
53/100 (needs-improvement)
80/100 (good)
Links
🌐 Visit Website View on GitHub
🌐 Visit Website View on GitHub

MongoDB Strengths

Apache Spark Strengths

  • ✓ More popular (43,909 stars)
  • ✓ Larger community (3,575 contributors)
  • ✓ More features (5 listed)

When to Use MongoDB vs Apache Spark

Use MongoDB 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

MongoDB vs Elasticsearch → Apache Spark vs Apache Flink → MongoDB vs Redis → Apache Spark vs Apache Beam → MongoDB vs ClickHouse → Apache Spark vs Elasticsearch → MongoDB vs TiDB → Apache Spark vs Grafana → MongoDB vs CockroachDB → Apache Spark vs Redis → MongoDB vs InfluxDB → Apache Spark vs Prometheus →

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Data source: GitHub API

Last updated: 8/30/2026