Head to head · Figures checked September 2026
Apache Auron vs DataFusion Comet
ETL Compare staff · Figures checked September 2026 · Sourced from vendor docs, project pages and public benchmarks · Published 29 September 2026
In brief
DataFusion Comet has the higher ETL fit score on our published weights (3.8 against 3.5 out of 5). Apache Auron scores higher on stage coverage; DataFusion Comet scores higher on adoption effort, tuning and operating burden, platform and instance portability, published evidence and maturity and community. They tie on cost model transparency. The score measures fit for speeding up existing Spark ETL without rewrites, not raw speed, so test both on one of your own pipelines before you decide.
On our weights DataFusion Comet ranks 2nd of 7 and Apache Auron ranks 5th of 7. Which one fits depends on the criteria below and on the platform you run.
Apache Auron (formerly Blaze) is an incubating open-source engine that maps Spark physical plans onto DataFusion native execution, with its own shuffle format. Apache DataFusion Comet is an open-source Spark plugin that runs supported operators on the Apache DataFusion engine (Rust, Arrow). Neither requires changes to Spark SQL or DataFrame code, according to its own documentation.
3.5 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 5 of 7
Open-source option with its own shuffle and memory layer
3.8 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 2 of 7
Best for teams on recent Spark 4.x releases
How do Apache Auron and DataFusion Comet score on each criterion?
| Criterion | Auron | Comet | Higher score |
|---|---|---|---|
| Adoption effort 20% | 3.4 | 3.8 | Comet |
| Stage coverage 20% | 3.8 | 3.6 | Auron |
| Tuning and operating burden 15% | 2.6 | 2.8 | Comet |
| Platform and instance portability 10% | 3.8 | 4.2 | Comet |
| Cost model transparency 10% | 5.0 | 5.0 | Tie |
| Published evidence 15% | 3.4 | 3.8 | Comet |
| Maturity and community 10% | 3.2 | 3.9 | Comet |
| ETL fit score | 3.5 | 3.8 | Comet |
Why each score
- Adoption effort
- Auron (3.4): No code changes, but you enable it through Spark settings and a custom shuffle manager and install it yourself.
- Comet (3.8): No code changes; add the Comet jar from Maven Central, enable the extension and configure off-heap memory and the Comet shuffle manager.
- Stage coverage
- Auron (3.8): Native vectorized execution on DataFusion, compacted shuffle formats and multi-level memory management are all documented.
- Comet (3.6): Native Parquet scans (including for Iceberg), native operators and native or columnar shuffle; native operators can spill, bounded per task; write path not described on the pages we reviewed.
- Tuning and operating burden
- Auron (2.6): Self-managed with community support through the Apache mailing list.
- Comet (2.8): The tuning guide says memory accounting "isn't 100% accurate" and describes pool fractions, batch size and spill limits to set; community support.
- Platform and instance portability
- Auron (3.8): Runs on standard CPU instances and supports JDK 8, 11, 17 and 21; the project says it is adapted to Spark mainline versions without listing them on the page we reviewed.
- Comet (4.2): Supports Spark 3.5, 4.0 and 4.1 (3.4 deprecated) with prebuilt Linux amd64 and arm64 jars on commodity hardware; you install it yourself on any platform.
- Cost model transparency
- Auron (5.0): Apache License 2.0, no license fee.
- Comet (5.0): Apache License 2.0, no license fee.
What do Apache Auron and DataFusion Comet cost, as published?
| Auron | Comet | |
|---|---|---|
| License | Apache License 2.0 | Apache License 2.0 |
| Published price | No license fee | No license fee |
| Instances | Standard CPU instances | Commodity CPU instances (amd64 or arm64) |
| Runs on | Self-managed Spark on JDK 8, 11, 17 or 21 | Self-managed Spark 3.5, 4.0 and 4.1 (3.4 deprecated) on Linux |
| Code changes | None; Spark settings and custom shuffle manager | None; jar plus Spark configuration |
Prices and terms as published on the pages we reviewed, 27 September 2026. Neither vendor's figure is a quote.
Sources: auron.apache.org, apache/auron on GitHub, kwai/blaze on GitHub, datafusion.apache.org/comet, Comet installation guide, Comet tuning guide · Fetched 27 Sep 2026
Where are they documented to run?
Documented means the vendor or project lists the platform on the pages we reviewed. Self-install means you can usually add an open-source plugin to a platform that lets you set Spark configuration and classpath, but the project does not publish a guide for that platform. None of the vendor pages we reviewed list AWS Glue.
Which job stages does each address?
| Accelerator | Read | Transform | Shuffle | Spill | Write |
|---|---|---|---|---|---|
| Comet | Documented | Documented | Documented | Partial | Not stated |
| Auron | Partial | Documented | Documented | Partial | Not stated |
- Documented: the vendor's own documentation says it addresses this stage
- Partial: indirect or experimental, or covered only by an end-to-end claim
- Not stated: not found on the pages we reviewed
This shows what each vendor says, not what we measured. Sources are listed on each review.
Choose Apache Auron if
- You want no license fee: Auron is released under the Apache License 2.0
- You want an open-source engine with its own compacted shuffle format and multi-level memory management
- You run on self-managed Spark or Kubernetes
Choose DataFusion Comet if
- You want no license fee: Comet is released under the Apache License 2.0
- You run Spark 3.5, 4.0 or 4.1 yourself on commodity amd64 or arm64 instances
- You run on self-managed Spark or Kubernetes
Frequently asked questions
Which has the higher ETL fit score, Apache Auron or DataFusion Comet?
DataFusion Comet, with 3.8 against 3.5 out of 5 on our published weights. Apache Auron scores higher on stage coverage and DataFusion Comet on adoption effort, tuning and operating burden, platform and instance portability, published evidence and maturity and community. They tie on cost model transparency. The score measures fit for speeding up existing Spark ETL, not raw speed.
Do Apache Auron and DataFusion Comet run on the same platforms?
Both are documented for self-managed Spark or Kubernetes. Open-source plugins without a guide for a platform can often be self-installed where you control Spark configuration.
What do Apache Auron and DataFusion Comet cost?
Apache Auron: No license fee (Apache License 2.0). DataFusion Comet: No license fee (Apache License 2.0). For a like-for-like comparison, work out cost per run on one of your own jobs: see Spark cost per job, explained.