ClickHouse vs Apache Spark: Key Differences & When to Use Each
Comprehensive side-by-side comparison of features, pricing, and metrics
Key Differences
Compare ClickHouse and Apache Spark across features, pricing, integrations, and community metrics. ClickHouse / Apache Spark.
Feature
ClickHouse
Database
Apache Spark
Data Processing
Side-by-side comparison of developer tools
Column-oriented database for OLAP
Unified analytics engine for large-scale data processing
GitHub Stars
⭐ 46,789
⭐ 43,103
Contributors
👥 2,823
👥 3,411
Pricing
✓ Free
Enterprise: Contact sales
✓ Free
Enterprise: Contact sales
Languages
C++
Scala
Features
- • Ai
- • Analytics
- • Big Data
- • Clickhouse
- • Cloud Native
- • Big Data
- • Java
- • Jdbc
- • Python
- • R
Integrations
No integrations listed
No integrations listed
Momentum Score
95/100Momentum959595
(stable)
87/100Momentum878787
(stable)
Community Health
77/100Health777777
(good)
91/100Health919191
(excellent)
Maturity Index
85/100Maturity858585
(mature)
89/100Maturity898989
(mature)
Innovation Score
82/100Innovation828282
(innovative)
91/100Innovation919191
(pioneering)
Risk Score (higher is safer)
94/100Risk949494
(minimal)
94/100Risk949494
(minimal)
Developer Experience
95/100DX959595
(excellent)
79/100DX797979
(good)
Links
ClickHouse Strengths
- ✓ More popular (46,789 stars)
Apache Spark Strengths
- ✓ Larger community (3,411 contributors)
When to Use ClickHouse vs Apache Spark
Use ClickHouse 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: 4/10/2026