Head-to-head · ETL Testing Tools
Datagaps ETL Validator vs Apache Griffin
Datagaps ETL Validator leads on 3 checks, Apache Griffin on 1, and 1 is even. Who comes out ahead on the 5 yes/no, price and count checks where we have data for both products. The editor score weighs everything else too.
Our verdict
- Highest scoreDatagaps ETL Validator · 9.0/10
- Free planonly Apache Griffin
- Most featuresDatagaps ETL Validator · 4 of 4
Datagaps ETL Validator scores higher on our rubric for etl testing tools: 9.0 against 7.3 out of 10; our editors rank them #2 and #8.
Apache Griffin offers free plan; Datagaps ETL Validator doesn't. Datagaps ETL Validator offers no-code test authoring; Apache Griffin doesn't. Datagaps ETL Validator offers ci/cd integration; Apache Griffin doesn't. On deployment model, Apache Griffin gives you Self-hosted where Datagaps ETL Validator offers Cloud. Datagaps ETL Validator offers api access; Apache Griffin doesn't.
Datagaps ETL Validator is the better fit for teams that want ETL validation up and running fast without a scripting-heavy setup. Apache Griffin is the better fit for big-data teams on Spark/Hadoop who want validation with zero license cost.
- Datagaps ETL Validator fits best
Teams that want ETL validation up and running fast without a scripting-heavy setup
- Apache Griffin fits best
Big-data teams on Spark/Hadoop who want validation with zero license cost
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Side by side
| Feature | Datagaps ETL Validator 9.0/10 Visit ↗ | Apache Griffin 7.3/10 Visit ↗ |
|---|---|---|
| At a glance | ||
| Editor score | 9.0 | 7.3 |
| Ranking | #2 in ETL Testing Tools | #8 in ETL Testing Tools |
| Best for | Teams that want ETL validation up and running fast without a scripting-heavy setup | Big-data teams on Spark/Hadoop who want validation with zero license cost |
| Pricing model | Free plan + paid | Free |
| Starting price | Free 14-day trial · paid tiers | Free (open source) |
| Free plan | — | ✓ (best) |
| Free trial | — | — |
| Features Datagaps ETL Validator 4/4 · Apache Griffin 1/4 | ||
| Source-to-target reconciliation | ✓ | ✓ |
| No-code test authoring | ✓ (best) | — |
| CI/CD integration | ✓ (best) | — |
| API access | ✓ (best) | — |
| Specs | ||
| Deployment model | Cloud | Self-hosted |
| Our review | ||
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| Cons |
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| Our verdict | Datagaps ETL Validator is built around getting a validation project running quickly: point it at a source and target, and its low-code interface plus AI-assisted rule suggestions do a lot of the first-draft work that would otherwise mean… Read the review → |
Apache Griffin is the choice for a team that's already standardized on Spark for big-data processing and wants data quality and ETL validation to live in that same stack instead of adding a new vendor and a new connector to maintain. It… Read the review → |
Strengths and trade-offs
Datagaps ETL Validator — where it wins
- Claims 200+ supported source and target types, covering most mainstream databases and cloud warehouses
- AI features suggest validation rules and flag likely-broken tests after a schema change
- 14-day free trial needs no credit card, so a real pipeline can be tested before any purchase conversation
Where it doesn't
- Exact paid-tier pricing isn't published, so a firm number still needs a sales call
- AI-suggested rules still need a human review pass before they're trusted on a regulated dataset
- Less BI/report-layer coverage than tools built specifically around reporting validation
Apache Griffin — where it wins
- No licensing cost at all, under the Apache Software Foundation
- Built on Spark, so it scales the same way the big-data pipelines it checks already do
- Supports both batch and streaming data quality measurement
Where it doesn't
- No vendor support line; help comes from community docs and mailing lists, not a support contract
- Setup and ongoing maintenance assume real familiarity with Spark and the Hadoop ecosystem
- Smaller, slower-moving community than more actively marketed commercial tools on this list
- Datagaps ETL Validator9.0/10 · Free 14-day trial · paid tiers
A low-code validator that leans on AI to suggest and self-heal comparison rules across a huge connector list.
Visit DatagapsFull verdict → - Apache Griffin7.3/10 · Free (open source)
A no-cost, self-hosted option for teams already running Spark who want data quality checks in the same stack.
Visit Apache GriffinFull verdict →
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- Datagaps ETL Validator vs Great Expectations
- RightData vs Apache Griffin
All ETL testing tools comparisons → · Full ranking →
Reviewed by iTechGuides Editors · Editorial team · Updated Nov 2026
Last updated · How we research and update