Apache Flink vs Apache Spark: Key Differences & When to Use Each
Comprehensive side-by-side comparison of features, pricing, and metrics
Key Differences
Compare Apache Flink and Apache Spark across features, pricing, integrations, and community metrics. Apache Flink / Apache Spark.
Feature
Apache Flink
Data Processing
Apache Spark
Data Processing
Side-by-side comparison of developer tools
Stream processing framework
Unified analytics engine for large-scale data processing
GitHub Stars
⭐ 25,980
⭐ 43,233
Contributors
👥 2,096
👥 3,432
Pricing
✓ Free
Enterprise: Contact sales
✓ Free
Enterprise: Contact sales
Languages
Java
Scala
Features
- • Big Data
- • Flink
- • Java
- • Python
- • Scala
- • Big Data
- • Java
- • Jdbc
- • Python
- • R
Integrations
No integrations listed
No integrations listed
Momentum Score
79/100Momentum797979
(stable)
79/100Momentum797979
(stable)
Community Health
68/100Health686868
(moderate)
91/100Health919191
(excellent)
Maturity Index
82/100Maturity828282
(established)
90/100Maturity909090
(mature)
Innovation Score
76/100Innovation767676
(innovative)
91/100Innovation919191
(pioneering)
Risk Score (higher is safer)
87/100Risk878787
(minimal)
94/100Risk949494
(minimal)
Developer Experience
68/100DX686868
(fair)
80/100DX808080
(good)
Links
Apache Flink Strengths
Apache Spark Strengths
- ✓ More popular (43,233 stars)
- ✓ Larger community (3,432 contributors)
When to Use Apache Flink vs Apache Spark
Use Apache Flink 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.
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Data source: GitHub API
Last updated: 5/5/2026