Head to head · Figures checked September 2026
DataFusion Comet vs RAPIDS Accelerator
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 3.7 out of 5). DataFusion Comet scores higher on adoption effort, platform and instance portability, cost model transparency and published evidence; RAPIDS Accelerator scores higher on stage coverage, tuning and operating burden and maturity and community. 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 RAPIDS Accelerator ranks 3rd of 7. Which one fits depends on the criteria below and on the platform you run.
Apache DataFusion Comet is an open-source Spark plugin that runs supported operators on the Apache DataFusion engine (Rust, Arrow). RAPIDS Accelerator for Apache Spark (now NVIDIA cuDF for Apache Spark) is an open-source NVIDIA plugin that runs supported Spark SQL and DataFrame operations on GPUs. Neither requires changes to Spark SQL or DataFrame code, according to its own documentation.
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
3.7 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 3 of 7
Best for teams that already run GPU capacity
How do DataFusion Comet and RAPIDS Accelerator score on each criterion?
| Criterion | Comet | RAPIDS Accelerator | Higher score |
|---|---|---|---|
| Adoption effort 20% | 3.8 | 3.4 | Comet |
| Stage coverage 20% | 3.6 | 4.2 | RAPIDS Accelerator |
| Tuning and operating burden 15% | 2.8 | 3.0 | RAPIDS Accelerator |
| Platform and instance portability 10% | 4.2 | 3.2 | Comet |
| Cost model transparency 10% | 5.0 | 4.2 | Comet |
| Published evidence 15% | 3.8 | 3.6 | Comet |
| Maturity and community 10% | 3.9 | 4.6 | RAPIDS Accelerator |
| ETL fit score | 3.8 | 3.7 | Comet |
Why each score
- Adoption effort
- 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.
- RAPIDS Accelerator (3.4): No code changes, but the job has to move to NVIDIA GPU instances and the cluster needs GPU-specific configuration.
- Stage coverage
- 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.
- RAPIDS Accelerator (4.2): Documents GPU execution for group by, joins, sorts and windows, Parquet and ORC writing, CSV reading and a RAPIDS Shuffle Manager; unsupported operations fall back to CPU.
- Tuning and operating burden
- 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.
- RAPIDS Accelerator (3.0): Qualification and Profiling tools help, but GPU sizing and plugin configuration are your team's job.
- Platform and instance portability
- 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.
- RAPIDS Accelerator (3.2): The widest platform list in this set (EMR, Databricks, Dataproc, GKE, Azure Synapse, Kubernetes, on-premises, OCI), held back because every one of them needs GPU instances.
- Cost model transparency
- Comet (5.0): Apache License 2.0, no license fee.
- RAPIDS Accelerator (4.2): The plugin is Apache 2.0 with no fee; the cost question becomes GPU instance price against runtime saved, which you can model from public cloud prices.
- Published evidence
- Comet (3.8): Publishes a TPC-DS at 1 TB benchmark with a per-query breakdown in its Benchmarking Guide.
- RAPIDS Accelerator (3.6): The Qualification Tool estimates fit from your own event logs; headline benchmark figures were not reviewed for this edition.
- Maturity and community
- Comet (3.9): A subproject of Apache DataFusion that has reached a 1.x release line; younger than Gluten and RAPIDS.
- RAPIDS Accelerator (4.6): A long-running NVIDIA project with more than 9,000 commits on main, now published as NVIDIA cuDF for Apache Spark.
What do DataFusion Comet and RAPIDS Accelerator cost, as published?
| Comet | RAPIDS Accelerator | |
|---|---|---|
| License | Apache License 2.0 | Apache License 2.0 |
| Published price | No license fee | No plugin fee; GPU instance pricing applies |
| Instances | Commodity CPU instances (amd64 or arm64) | NVIDIA GPU instances (Volta or later) |
| Runs on | Self-managed Spark 3.5, 4.0 and 4.1 (3.4 deprecated) on Linux | Amazon EMR, Databricks, Dataproc, GKE, Azure Synapse, Kubernetes, on-premises, OCI |
| Code changes | None; jar plus Spark configuration | None; plugin replaces internal physical plan parts |
Prices and terms as published on the pages we reviewed, 27 September 2026. Neither vendor's figure is a quote.
Sources: datafusion.apache.org/comet, Comet installation guide, Comet tuning guide, spark-rapids overview, RAPIDS Accelerator user guide, RAPIDS Accelerator FAQ, NVIDIA/spark-rapids on GitHub · Fetched 27 Sep 2026
Where are they documented to run?
| EMR | Databricks | Google Cloud (Dataproc) | AWS Glue | Self-managed Spark / Kubernetes | |
|---|---|---|---|---|---|
| Comet | Not documentedself-install | Not documented | Not documentedself-install | Not documented | Documented |
| RAPIDS Accelerator | Documented | Documented | Documented | Not documented | Documented |
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 |
| RAPIDS Accelerator | Documented | Documented | Documented | Not stated | Documented |
- 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 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
Choose RAPIDS Accelerator if
- You want a long-running project with more than 9,000 commits on main
- You want GPU execution documented for joins, sorts, aggregations, window functions, Parquet and ORC writing and shuffle
- You run on Amazon EMR, Databricks, Google Cloud (Dataproc) and self-managed Spark or Kubernetes
Frequently asked questions
Which has the higher ETL fit score, DataFusion Comet or RAPIDS Accelerator?
DataFusion Comet, with 3.8 against 3.7 out of 5 on our published weights. DataFusion Comet scores higher on adoption effort, platform and instance portability, cost model transparency and published evidence and RAPIDS Accelerator on stage coverage, tuning and operating burden and maturity and community. The score measures fit for speeding up existing Spark ETL, not raw speed.
Do DataFusion Comet and RAPIDS Accelerator run on the same platforms?
Both are documented for self-managed Spark or Kubernetes. RAPIDS Accelerator is also documented for Amazon EMR, Databricks and Google Cloud (Dataproc). Open-source plugins without a guide for a platform can often be self-installed where you control Spark configuration.
What do DataFusion Comet and RAPIDS Accelerator cost?
DataFusion Comet: No license fee (Apache License 2.0). RAPIDS Accelerator: No plugin fee; GPU instance pricing applies (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.