Head to head · Figures checked September 2026
DataFusion Comet vs Speedata
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 2.61 out of 5). DataFusion Comet scores higher on adoption effort, stage coverage, platform and instance portability, cost model transparency, published evidence and maturity and community; Speedata scores higher on tuning and operating burden. 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 Speedata ranks 9th 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). Speedata is a Custom analytics silicon: the C200 Analytics Processing Unit (APU) card plus Dash, a Spark plugin that hooks the Catalyst optimizer. 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
2.61 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 9 of 9
Teams that can add dedicated analytics hardware on premises
How do DataFusion Comet and Speedata score on each criterion?
| Criterion | Comet | Speedata | Higher score |
|---|---|---|---|
| Adoption effort 20% | 3.8 | 2.6 | Comet |
| Stage coverage 20% | 3.6 | 2.6 | Comet |
| Tuning and operating burden 20% | 2.8 | 3.0 | Speedata |
| Platform and instance portability 10% | 4.2 | 1.6 | Comet |
| Cost model transparency 5% | 5.0 | 2.0 | Comet |
| Published evidence 15% | 3.8 | 3.0 | Comet |
| Maturity and community 10% | 3.9 | 2.6 | Comet |
| ETL fit score | 3.67 | 2.61 | 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.
- Speedata (2.6): Speedata states Dash drops into existing Spark environments with zero code changes, but the job has to move onto servers fitted with Speedata APU cards, bought pre-configured or through OEM partners such as HPE.
- 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.
- Speedata (2.6): The product page says Dash offloads compute-intensive work and that non-accelerable elements such as UDFs fall back to CPU; no operator list and no shuffle, spill or write detail are published.
- 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.
- Speedata (3.0): A vendor product with a Workload Analyzer that reads your Spark logs; you run dedicated hardware in your own data center, and support terms are not published.
- 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.
- Speedata (1.6): Documented for on-premises servers with C200 cards, plus a February 2026 sovereign-cloud partnership with Nebul; Amazon EMR, EKS, Databricks and Kubernetes are not mentioned on the product page.
- Cost model transparency
- Comet (5.0): Apache License 2.0, no license fee.
- Speedata (2.0): No pricing page and no public price for the software or the APU servers; buyers need a demo and a quote.
- Published evidence
- Comet (3.8): Publishes a TPC-DS at 1 TB benchmark with a per-query breakdown in its Benchmarking Guide.
- Speedata (3.0): Vendor figures include 62.7x on production Spark workloads and 37 servers consolidated to 3; the site also cites a SIGMOD 2026 Best Paper whose Microsoft CoddSpeed benchmark features the APU. Setup detail for the vendor figures is not 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.
- Speedata (2.6): A commercial hardware vendor with Israeli founders; TechCrunch reported a $44M Series B in June 2025 (secondary source). Founding year is not stated and there is no open-source community.
What do DataFusion Comet and Speedata cost, as published?
| Comet | Speedata | |
|---|---|---|
| License | Apache License 2.0 | Commercial |
| Published price | No license fee | Not published; book a demo |
| Instances | Commodity CPU instances (amd64 or arm64) | Servers fitted with Speedata C200 APU cards |
| Runs on | Self-managed Spark 3.5, 4.0 and 4.1 (3.4 deprecated) on Linux | Speedata APU servers in your data center (pre-configured 2U server or OEM servers such as HPE); Amazon EMR, EKS and Databricks not mentioned on the product page |
| Code changes | None; jar plus Spark configuration | None stated; Dash Spark plugin (spark.plugins) |
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, speedata.io, Speedata product page, Speedata news, Speedata company page · 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 |
| Speedata | Partial | 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 Speedata if
- You want the least tuning and operating work for your team
- You want published, reproducible evidence
- You run on self-managed Spark or Kubernetes
Frequently asked questions
Which has the higher ETL fit score, DataFusion Comet or Speedata?
DataFusion Comet, with 3.67 against 2.61 out of 5 on our lead weights. DataFusion Comet scores higher on adoption effort, stage coverage, platform and instance portability, cost model transparency, published evidence and maturity and community and Speedata on tuning and operating burden. The score measures fit for speeding up existing Spark ETL, not raw speed.
Do DataFusion Comet and Speedata 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 DataFusion Comet and Speedata cost?
DataFusion Comet: No license fee (Apache License 2.0). Speedata: Not published; book a demo (Commercial). For a like-for-like comparison, work out cost per run on one of your own jobs: see Spark cost per job, explained.