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

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

Key Differences

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

Feature

Redis

Database

Apache Spark

Data Processing

Side-by-side comparison of developer tools
In-memory data structure store
Unified analytics engine for large-scale data processing
GitHub Stars
⭐ 74,123
⭐ 43,233
Contributors
👥 909
👥 3,432
Pricing
✓ Free
Enterprise: Contact sales
✓ Free
Enterprise: Contact sales
Languages
C
Scala
Features
  • • Cache
  • • Caching
  • • Database
  • • Distributed Systems
  • • In Memory
  • • Big Data
  • • Java
  • • Jdbc
  • • Python
  • • R
Integrations
  • • redis
No integrations listed
Momentum Score
89/100 (stable)
79/100 (stable)
Community Health
81/100 (good)
91/100 (excellent)
Maturity Index
82/100 (established)
90/100 (mature)
Innovation Score
91/100 (pioneering)
91/100 (pioneering)
Risk Score (higher is safer)
68/100 (low)
94/100 (minimal)
Developer Experience
68/100 (fair)
80/100 (good)
Links
🌐 Visit Website View on GitHub
🌐 Visit Website View on GitHub

Redis Strengths

  • ✓ More popular (74,123 stars)

Apache Spark Strengths

  • ✓ Larger community (3,432 contributors)

When to Use Redis vs Apache Spark

Use Redis 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

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

More Comparisons

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Data source: GitHub API

Last updated: 5/4/2026