DataFlint
Reviews
Star rating
Rank in AI Coding Assistants
Pros and cons
Pros
Cons
Traffic & LLM citations
Source: G2 review data
Key stats
Adoption by company size
| Segment | Share of reviews |
|---|---|
| Small Business | 80.0% |
| Mid-Market | 20.0% |
| Enterprise | 0.0% |
Quarterly history
| Quarter | Reviews | Star rating |
|---|---|---|
| 2026 Q1 | 10 | 5.0 |
| 2026 Q2 | 5 | 5.0 |
Satisfaction ratings
What reviewers say
Job Debugger Makes Spark UI Troubleshooting Faster and Easier
“The job debugger component is especially useful. It enhances the Spark UI with clearer, more readable stage views and automatically highlights performance issues, which makes troubleshooting much less tedious for our team. The open-source component was also straightforward to dep”
Stage-Level Analysis That Clearly Surfaces Spark Bottlenecks
“The stage-level analysis makes performance issues much easier to understand. Our ad analytics pipelines run many complex Spark jobs, and this tool surfaces bottlenecks very clearly. I also appreciate how the IDE extension connects directly to production runs, since that added con”
Clear Cost Insights for Spark Jobs with Actionable Optimization Tips
“I like how the dashboard highlights the cost impact of our Spark workloads. Rather than having to review infrastructure metrics in isolation, everything is tied directly to the job and stage level. That added context makes it much easier to see why certain jobs end up being expen”