Head to head · Figures checked September 2026
DataFusion Comet vs DataPelago
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 lead weights (3.67 against 3.14 out of 5). DataFusion Comet scores higher on stage coverage, platform and instance portability, cost model transparency, published evidence and maturity and community; DataPelago scores higher on tuning and operating burden. They tie on adoption effort. 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 lead weights DataFusion Comet ranks 3rd of 9 and DataPelago ranks 8th of 9. 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). DataPelago Accelerator for Spark is a commercial Spark accelerator (DPA-S) built on the DataPelago Nucleus engine, using CPU, GPU or FPGA hardware. Neither requires changes to Spark SQL or DataFrame code, according to its own documentation.
3.67 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 3 of 9
Best for teams on recent Spark 4.x releases
3.14 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 8 of 9
Compute-heavy Spark SQL and DataFrame jobs on Parquet or ORC in Google Cloud or AWS
How do DataFusion Comet and DataPelago score on each criterion?
| Criterion | Comet | DataPelago | Higher score |
|---|---|---|---|
| Adoption effort 20% | 3.8 | 3.8 | Tie |
| Stage coverage 20% | 3.6 | 3.0 | Comet |
| Tuning and operating burden 20% | 2.8 | 3.0 | DataPelago |
| Platform and instance portability 10% | 4.2 | 3.2 | Comet |
| Cost model transparency 5% | 5.0 | 3.0 | Comet |
| Published evidence 15% | 3.8 | 3.0 | Comet |
| Maturity and community 10% | 3.9 | 2.6 | Comet |
| ETL fit score | 3.67 | 3.14 | 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.
- DataPelago (3.8): DataPelago states existing Spark code runs unchanged with no data migration, configured through standard Spark settings and bought through the Google Cloud or AWS marketplace.
- 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.
- DataPelago (3.0): Its page names good fits (compute-intensive jobs, Parquet, ORC, SQL and DataFrame) and says to avoid heavy RDD and UDF use; shuffle, spill and write behavior are not described.
- 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.
- DataPelago (3.0): A vendor-run accelerator configured with standard Spark settings; tuning guidance and support terms are not published on the pages we reviewed.
- 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.
- DataPelago (3.2): Supports multiple Spark versions on Google Cloud and AWS and any hardware from CPU to GPU, by the vendor's statement; specific managed services such as Amazon EMR, Dataproc or Databricks are not named.
- Cost model transparency
- Comet (5.0): Apache License 2.0, no license fee.
- DataPelago (3.0): Pay as you go through cloud marketplaces; the AWS Marketplace listing shows a vCPU-hour dimension and a $100,000 per month contract entry, but no per-job rate you can model without a quote.
- Published evidence
- Comet (3.8): Publishes a TPC-DS at 1 TB benchmark with a per-query breakdown in its Benchmarking Guide.
- DataPelago (3.0): Vendor claims of 10x faster at 80% cost reduction, customer quotes with figures (Akad Seguros, ShareChat) and a benchmark page against NVIDIA cuDF, all vendor-published.
- Maturity and community
- Comet (3.9): A subproject of Apache DataFusion that has reached a 1.x release line; younger than Gluten and RAPIDS.
- DataPelago (2.6): The Accelerator for Spark launched on 5 August 2025, on the Nucleus engine unveiled in October 2024; offices in Mountain View and Hyderabad; no open-source community of its own.
What do DataFusion Comet and DataPelago cost, as published?
| Comet | DataPelago | |
|---|---|---|
| License | Apache License 2.0 | Commercial |
| Published price | No license fee | Pay as you go through the Google Cloud and AWS marketplaces; the AWS Marketplace listing shows a vCPU-hour dimension and a $100,000 per month one-month contract entry |
| Instances | Commodity CPU instances (amd64 or arm64) | Any available hardware, CPU to GPU (vendor statement) |
| Runs on | Self-managed Spark 3.5, 4.0 and 4.1 (3.4 deprecated) on Linux | Multiple Spark versions on Google Cloud and AWS; Amazon EMR, EKS and Databricks not named on the DPA-S page |
| Code changes | None; jar plus Spark configuration | None stated; standard Spark settings |
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, datapelago.ai, DPA-S page, DataPelago press page, DataPelago company page, AWS Marketplace listing · 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 |
| DataPelago | Not documentedAWS stated; EMR not named | Not documented | Not documentedGoogle Cloud stated; service not named | Not documented | Not 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 |
| DataPelago | Documented | Partial | Not stated | Not stated | 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 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 DataPelago if
- Low adoption effort on your current platform matters most
- You need to run on several platforms or standard instances
Frequently asked questions
Which has the higher ETL fit score, DataFusion Comet or DataPelago?
DataFusion Comet, with 3.67 against 3.14 out of 5 on our lead weights. DataFusion Comet scores higher on stage coverage, platform and instance portability, cost model transparency, published evidence and maturity and community and DataPelago on tuning and operating burden. They tie on adoption effort. The score measures fit for speeding up existing Spark ETL, not raw speed.
Do DataFusion Comet and DataPelago 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. Open-source plugins without a guide for a platform can often be self-installed where you control Spark configuration.
What do DataFusion Comet and DataPelago cost?
DataFusion Comet: No license fee (Apache License 2.0). DataPelago: Pay as you go through the Google Cloud and AWS marketplaces; the AWS Marketplace listing shows a vCPU-hour dimension and a $100,000 per month one-month contract entry (Commercial). For a like-for-like comparison, work out cost per run on one of your own jobs: see Spark cost per job, explained.