Data pipeline buyers have to compare tools that solve different parts of the problem: ingestion, streaming, orchestration, transformation, reverse ETL, CDC, and ML/data workloads. The May 2026 ToolVitals data gives a compact view across 37 tools using ToolVitals score, health score, shipping score, GitHub stars, and status.
This ranking is ordered by ToolVitals score first, with GitHub stars used only as a secondary popularity signal when scores are tied. That means highly starred projects can rank below smaller projects if their ToolVitals score is lower, while ties at the top are separated by GitHub star count.
Rankings
| Rank | Tool | Health | Shipping | GitHub Stars | Score | Status |
|---|---|---|---|---|---|---|
| 1 | CocoIndex | 92 | 100 | 9201 | 94 | 🟢 Excellent |
| 2 | Benthos | 97 | 100 | 8662 | 94 | 🟢 Excellent |
| 3 | Jitsu | 97 | 100 | 4759 | 94 | 🟢 Excellent |
| 4 | RudderStack | 97 | 100 | 4405 | 94 | 🟢 Excellent |
| 5 | Sail | 92 | 100 | 2447 | 94 | 🟢 Excellent |
| 6 | Pixeltable | 92 | 100 | 1550 | 94 | 🟢 Excellent |
| 7 | dbmazz | 92 | 100 | 10 | 94 | 🟢 Excellent |
| 8 | Dagster | 97 | 88 | 15467 | 93 | 🟢 Excellent |
| 9 | dbt | 97 | 88 | 12731 | 93 | 🟢 Excellent |
| 10 | Altimate Code | 88 | 100 | 571 | 93 | 🟢 Excellent |
| 11 | CloudQuery | 91 | 95 | 0 | 93 | 🟢 Excellent |
| 12 | MLRun | 86 | 100 | 1666 | 92 | 🟢 Excellent |
| 13 | Bruin | 86 | 100 | 1583 | 92 | 🟢 Excellent |
| 14 | Apache Airflow | 92 | 88 | 45339 | 90 | 🟢 Excellent |
| 15 | Open Wearables | 89 | 88 | 1601 | 89 | 🟢 Excellent |
| 16 | Multiwoven | 92 | 100 | 1652 | 86 | 🟢 Excellent |
| 17 | parsimony | 80 | 88 | 0 | 84 | 🟢 Excellent |
| 18 | Apache DevLake | 91 | 72 | 3005 | 82 | 🟢 Excellent |
| 19 | Pathway | 88 | 72 | 63285 | 81 | 🟢 Excellent |
| 20 | Meltano | 81 | 56 | 2477 | 70 | 🟢 Good |
| 21 | HRFlow | 70 | 56 | 39 | 64 | 🟢 Good |
| 22 | Airbyte | 83 | 0 | 21231 | 46 | 🟡 Fair |
| 23 | Arroyo | 71 | 0 | 4902 | 39 | 🔴 Needs Attention |
| 24 | qData | 70 | 0 | 425 | 38 | 🔴 Needs Attention |
| 25 | AtroCore | 70 | 0 | 217 | 38 | 🔴 Needs Attention |
| 26 | Mage | 67 | 0 | 8720 | 37 | 🔴 Needs Attention |
| 27 | Zinc Labs | 52 | 0 | 17822 | 29 | 🔴 Needs Attention |
| 28 | Seldon | 52 | 0 | 4747 | 29 | 🔴 Needs Attention |
| 29 | DocETL | 52 | 0 | 3746 | 29 | 🔴 Needs Attention |
| 30 | SyncMaven | 52 | 0 | 22 | 29 | 🔴 Needs Attention |
| 31 | Automate DV | 39 | 0 | 587 | 21 | 🔴 Needs Attention |
| 32 | AppBase | 36 | 0 | 4921 | 20 | 🔴 Needs Attention |
| 33 | Ploomber | 36 | 0 | 3626 | 20 | 🔴 Needs Attention |
| 34 | Documind | 36 | 0 | 1473 | 20 | 🔴 Needs Attention |
| 35 | Neosync | 32 | 0 | 4151 | 18 | 🔴 Needs Attention |
| 36 | Grouparoo | 33 | 0 | 773 | 18 | 🔴 Needs Attention |
| 37 | Orchest | 16 | 0 | 4140 | 9 | 🔴 Needs Attention |
Top 3 Highlights
CocoIndex ranks first with a ToolVitals score of 94, a shipping score of 100, and 9,201 GitHub stars. Its description points to an incremental engine for long horizon agents, which makes it a different kind of data pipeline entry than traditional ETL or orchestration tools.
Benthos ranks second with the same ToolVitals score as CocoIndex at 94, but fewer GitHub stars at 8,662. It has a 97 health score and 100 shipping score, and its YAML-driven streaming pipeline model makes it one of the strongest-ranked general-purpose pipeline processors in this set.
Jitsu ranks third with a ToolVitals score of 94, 4,759 GitHub stars, a 97 health score, and a 100 shipping score. Its positioning as an open-source Segment alternative and real-time ingestion engine puts it squarely in the customer-data and event-pipeline category.
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