Case Study: Migrating from dbt to KelpMesh¶
Company: FinFlow Analytics (FinTech, 20-person data team)¶
The Problem¶
FinFlow had been using dbt for 18 months. Their data team of 20 analysts was productive in SQL, but struggled with dbt's Jinja templating:
- Onboarding took 3+ weeks — new hires needed to learn Jinja + dbt conventions before writing their first model
- AI tools didn't work — GitHub Copilot generated invalid Jinja-SQL hybrids
- Code review was slow — reviewers mentally parsed Jinja to understand the actual SQL
- Enterprise compliance required — they needed audit logging, RLS, and column masking for SOC 2
The Solution¶
They migrated to KelpMesh in 2 days:
Results¶
| Metric | Before (dbt) | After (KelpMesh) |
|---|---|---|
| Onboarding time | 3 weeks | 3 days |
| Model development time | 4 hours | 1.5 hours |
| Code review time | 45 min | 15 min |
| AI assistant success rate | 20% | 95% |
| Security compliance | Manual | Automated |
Key takeaways¶
"The biggest win isn't any single feature — it's that our analysts can write SQL without thinking about tooling. KelpMesh just works."
— VP Data, FinFlow Analytics
"We got audit logging, RLS, and column masking out of the box. That would have taken us months to build ourselves."
— CISO, FinFlow Analytics