Compare · Figures checked September 2026
Compare Spark accelerators side by side
ETL Compare staff · Published 29 September 2026
In brief
Choose two to five accelerators to see every ETL fit criterion, the reason behind each score, where each runs and what each publishes about price. The default view shows the three highest-ranked products in this edition: Apache Gluten, DataFusion Comet and RAPIDS Accelerator. Head-to-head pages for all 21 pairs and an alternatives page for each product are listed below the table.
| Gluten + Velox | Comet | RAPIDS Accelerator | |
|---|---|---|---|
| Overview | |||
| Rank | Rank 1 of 7 | Rank 2 of 7 | Rank 3 of 7 |
| ETL fit score | 3.9 / 5 ETL fit score (editorial assessment, 0-5) | 3.8 / 5 ETL fit score (editorial assessment, 0-5) | 3.7 / 5 ETL fit score (editorial assessment, 0-5) |
| Best for | Highest ETL fit score: open-source native engine for self-managed Spark | Best for teams on recent Spark 4.x releases | Best for teams that already run GPU capacity |
| Approach | Open-source plugin that offloads Spark SQL execution to a native C++ engine (Velox or ClickHouse) | Open-source Spark plugin that runs supported operators on the Apache DataFusion engine (Rust, Arrow) | Open-source NVIDIA plugin that runs supported Spark SQL and DataFrame operations on GPUs |
| Criterion scores | |||
| Adoption effort 20% | 3.8Tied highest in selectionNo application code changes; you add the Gluten JAR, set spark.plugins, enable off-heap memory and switch the shuffle manager, per the Velox getting-started page. | 3.8Tied highest in selectionNo code changes; add the Comet jar from Maven Central, enable the extension and configure off-heap memory and the Comet shuffle manager. | 3.4No code changes, but the job has to move to NVIDIA GPU instances and the cluster needs GPU-specific configuration. |
| Stage coverage 20% | 3.8Offloads execution to the Velox native engine with a columnar shuffle manager; spilling is documented as experimental. | 3.6Native 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. | 4.2Highest in selectionDocuments 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 15% | 2.6You size off-heap memory yourself (the docs example uses 20g) and rely on community channels; no vendor support contract is described on the project pages. | 2.8The tuning guide says memory accounting "isn't 100% accurate" and describes pool fractions, batch size and spill limits to set; community support. | 3.0Highest in selectionQualification and Profiling tools help, but GPU sizing and plugin configuration are your team's job. |
| Platform and instance portability 10% | 4.3Highest in selectionSupports Spark 3.4, 3.5, 4.0 and 4.1 on x86_64 and aarch64 Linux on standard CPU instances; managed-platform guides are not listed, so you install it yourself. | 4.2Supports 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. | 3.2The 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 10% | 5.0Tied highest in selectionApache License 2.0, no license fee; cost is your existing compute plus engineering time. | 5.0Tied highest in selectionApache License 2.0, no license fee. | 4.2The 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 15% | 3.8Tied highest in selectionPublishes TPC-H and TPC-DS results (Velox backend 2.71x overall, tested June 2023) with the benchmark named; results come from the project. | 3.8Tied highest in selectionPublishes a TPC-DS at 1 TB benchmark with a per-query breakdown in its Benchmarking Guide. | 3.6The Qualification Tool estimates fit from your own event logs; headline benchmark figures were not reviewed for this edition. |
| Maturity and community 10% | 4.4Apache top-level project since March 2026, started by Intel and Kyligence in 2022, with contributors including Alibaba Cloud, Meituan, Microsoft, IBM and Google. | 3.9A subproject of Apache DataFusion that has reached a 1.x release line; younger than Gluten and RAPIDS. | 4.6Highest in selectionA long-running NVIDIA project with more than 9,000 commits on main, now published as NVIDIA cuDF for Apache Spark. |
| Where it runs | |||
| Runs on | Self-managed Spark 3.4 to 4.1 on Linux, on any platform where you control Spark config | 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 |
| Instances | Standard CPU instances (x86_64 or aarch64) | Commodity CPU instances (amd64 or arm64) | NVIDIA GPU instances (Volta or later) |
| EMR | Not documentedself-install | Not documentedself-install | Documented |
| Databricks | Not documented | Not documented | Documented |
| Google Cloud (Dataproc) | Not documentedself-install | Not documentedself-install | Documented |
| AWS Glue | Not documented | Not documented | Not documented |
| Self-managed Spark / Kubernetes | Documented | Documented | Documented |
| Price and license | |||
| License | Apache License 2.0 | Apache License 2.0 | Apache License 2.0 |
| Published price | No license fee | No license fee | No plugin fee; GPU instance pricing applies |
| Code changes | None; JAR plus Spark configuration | None; jar plus Spark configuration | None; plugin replaces internal physical plan parts |
| Stage coverage | |||
| Read | Partial | Documented | Documented |
| Transform | Documented | Documented | Documented |
| Shuffle | Documented | Documented | Documented |
| Spill | Partial | Partial | Not stated |
| Write | Not stated | Not stated | Documented |
Sources: gluten.apache.org, Velox backend getting started, apache/incubator-gluten on GitHub, 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
Which head-to-head comparisons are there?
Apache Auron vs DataFusion Comet
DataFusion Comet has the higher ETL fit score (3.8 against 3.5).
Apache Auron vs DualBird
DualBird has the higher ETL fit score (3.6 against 3.5).
Apache Auron vs Flarion
Apache Auron has the higher ETL fit score (3.5 against 3.1).
Apache Auron vs Apache Gluten
Apache Gluten has the higher ETL fit score (3.9 against 3.5).
Apache Auron vs Databricks Photon
Apache Auron has the higher ETL fit score (3.5 against 3.4).
Apache Auron vs RAPIDS Accelerator
RAPIDS Accelerator has the higher ETL fit score (3.7 against 3.5).
DataFusion Comet vs Flarion
DataFusion Comet has the higher ETL fit score (3.8 against 3.1).
DataFusion Comet vs Databricks Photon
DataFusion Comet has the higher ETL fit score (3.8 against 3.4).
DataFusion Comet vs RAPIDS Accelerator
DataFusion Comet has the higher ETL fit score (3.8 against 3.7).
DualBird vs DataFusion Comet
DataFusion Comet has the higher ETL fit score (3.8 against 3.6).
DualBird vs Flarion
DualBird has the higher ETL fit score (3.6 against 3.1).
DualBird vs Apache Gluten
Apache Gluten has the higher ETL fit score (3.9 against 3.6).
DualBird vs Databricks Photon
DualBird has the higher ETL fit score (3.6 against 3.4).
DualBird vs RAPIDS Accelerator
RAPIDS Accelerator has the higher ETL fit score (3.7 against 3.6).
Flarion vs Apache Gluten
Apache Gluten has the higher ETL fit score (3.9 against 3.1).
Flarion vs Databricks Photon
Databricks Photon has the higher ETL fit score (3.4 against 3.1).
Flarion vs RAPIDS Accelerator
RAPIDS Accelerator has the higher ETL fit score (3.7 against 3.1).
Apache Gluten vs DataFusion Comet
Apache Gluten has the higher ETL fit score (3.9 against 3.8).
Apache Gluten vs Databricks Photon
Apache Gluten has the higher ETL fit score (3.9 against 3.4).
Apache Gluten vs RAPIDS Accelerator
Apache Gluten has the higher ETL fit score (3.9 against 3.7).
Databricks Photon vs RAPIDS Accelerator
RAPIDS Accelerator has the higher ETL fit score (3.7 against 3.4).
Which products have an alternatives page?
How should you read this table?
Scores are editorial assessments on a 0 to 5 scale against the criteria on the method page. The marker in each criterion row shows the highest score among the products you selected, and shared highs are shown as ties. Platform and price rows repeat what each vendor or project publishes; "Not documented" means we did not find the platform on the pages we reviewed, not that the product cannot run there. To apply your own weights, use the calculator.
Frequently asked questions
How many products can I compare at once?
Two to five. Your selection is kept in the page address, so you can bookmark or share it.
Why is Amazon EMR not in the list?
Amazon EMR is a platform that runs Spark, not an accelerator, so it is not scored. The table shows which products are documented for EMR and the other platforms, and several run on EMR. See where Spark accelerators run.
Where do the scores come from?
From desk research on public vendor and project pages, scored against seven weighted criteria. See how we score. We did not run the products.