Apache Arrow
Reviews
Star rating
Rank in Python Integrated Development Environments (IDE)
Pros and cons
Pros
- Features
- Language Support
- Data Analysis
- Performance Efficiency
- Data Management
- Compatibility
Cons
- Complexity
- Learning Curve
- Integration Issues
- Complex Setup
- Debugging Issues
- Beginner Unfriendliness
Traffic & LLM citations
Source: G2 review data
Winning from
Key stats
Adoption by company size
| Segment | Share of reviews |
|---|---|
| Small Business | 38.7% |
| Mid-Market | 35.5% |
| Enterprise | 25.8% |
Quarterly history
| Quarter | Reviews | Star rating |
|---|---|---|
| 2025 Q1 | 2 | 3.5 |
| 2025 Q4 | 2 | 4.8 |
Satisfaction ratings
What reviewers say
Lightning-fast inter-language data transfers with zero copy overhead
“The zero-copy in-memory format makes passing massive dataframes between Python and C++ insanely fast, saving tons of CPU time and memory overhead.”
High-Performance Data Framework for Analytics and ML Workflows
“What I like most about Apache Arrow is how efficiently it handles large datasets in memory. It provides a fast, columnar data format that improves performance when moving data between different systems. In our projects, we’ve used it to connect Python, R, and Java applications wi”
High-Performance Data Framework for Modern Analytics
“Apache Arrow provides exceptional speed and efficiency for in-memory data sharing across different systems and languages, reducing serialization overhead.”