Head to head · Figures checked September 2026
DataFusion Comet vs Databricks Photon
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 3.4 out of 5). DataFusion Comet scores higher on adoption effort, platform and instance portability, cost model transparency and published evidence; Databricks Photon scores higher on stage coverage, tuning and operating burden and maturity and community. Photon only runs inside Databricks, so for most teams the platform they use decides this comparison before the scores do.
On our weights DataFusion Comet ranks 2nd of 7 and Databricks Photon ranks 6th 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). Databricks Photon is a Databricks-native vectorized C++ query engine inside the Databricks Runtime. 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
3.4 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 6 of 7
Best for teams already on Databricks
How do DataFusion Comet and Databricks Photon score on each criterion?
| Criterion | Comet | Photon | Higher score |
|---|---|---|---|
| Adoption effort 20% | 3.8 | 2.6 | Comet |
| Stage coverage 20% | 3.6 | 3.8 | Photon |
| Tuning and operating burden 15% | 2.8 | 4.6 | Photon |
| Platform and instance portability 10% | 4.2 | 1.6 | Comet |
| Cost model transparency 10% | 5.0 | 3.2 | Comet |
| Published evidence 15% | 3.8 | 3.2 | Comet |
| Maturity and community 10% | 3.9 | 4.6 | Photon |
| ETL fit score | 3.8 | 3.4 | 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.
- Photon (2.6): One checkbox (or on by default on serverless) with no code changes if you already run on Databricks; for a team on EMR or self-managed Spark it means moving to Databricks first.
- 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.
- Photon (3.8): Documents native scans (Parquet, Delta, CSV, JSON), filters, joins, aggregates, windows and writes, with fallback to the Spark runtime; no UDF, RDD or stateful streaming support.
- 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.
- Photon (4.6): Managed by Databricks inside its runtime, so there is no plugin for your team to install or version.
- 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.
- Photon (1.6): Available only inside Databricks (on AWS, Azure and Google Cloud).
- Cost model transparency
- Comet (5.0): Apache License 2.0, no license fee.
- Photon (3.2): Databricks publishes DBU rates, but Photon compute consumes DBUs at different rates, so the net effect has to be modelled per job.
- Published evidence
- Comet (3.8): Publishes a TPC-DS at 1 TB benchmark with a per-query breakdown in its Benchmarking Guide.
- Photon (3.2): Databricks documents where Photon does not help (queries under two seconds, UDFs); performance comparisons outside Databricks are not possible because it only runs there.
What do DataFusion Comet and Databricks Photon cost, as published?
| Comet | Photon | |
|---|---|---|
| License | Apache License 2.0 | Commercial (part of Databricks) |
| Published price | No license fee | Databricks DBU pricing; Photon changes DBU consumption |
| Instances | Commodity CPU instances (amd64 or arm64) | Supported Databricks instance types, including Graviton |
| Runs on | Self-managed Spark 3.5, 4.0 and 4.1 (3.4 deprecated) on Linux | Databricks only (AWS, Azure, Google Cloud) |
| Code changes | None; jar plus Spark configuration | None on Databricks; platform move otherwise |
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, Photon documentation · 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 |
| Photon | Documented | Documented | Not stated | 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 Databricks Photon if
- Your jobs already run on Databricks and you want no plugin to install or version
- You want the engine that is the default on Databricks serverless and SQL warehouses
- You run on Databricks
Frequently asked questions
Which has the higher ETL fit score, DataFusion Comet or Databricks Photon?
DataFusion Comet, with 3.8 against 3.4 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 Databricks Photon 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 Databricks Photon run on the same platforms?
They share no documented platform on the pages we reviewed. DataFusion Comet is also documented for self-managed Spark or Kubernetes. Databricks Photon is also documented for Databricks. Open-source plugins without a guide for a platform can often be self-installed where you control Spark configuration.
What do DataFusion Comet and Databricks Photon cost?
DataFusion Comet: No license fee (Apache License 2.0). Databricks Photon: Databricks DBU pricing; Photon changes DBU consumption (Commercial (part of Databricks)). For a like-for-like comparison, work out cost per run on one of your own jobs: see Spark cost per job, explained.