Head to head · Figures checked September 2026
Apache Auron vs Speedata
ETL Compare staff · Figures checked September 2026 · Sourced from vendor docs, project pages and public benchmarks · Published 29 September 2026
In brief
Apache Auron has the higher ETL fit score on our lead weights (3.42 against 2.61 out of 5). Apache Auron 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 Apache Auron ranks 6th of 9 and Speedata ranks 9th of 9. Which one fits depends on the criteria below and on the platform you run.
Apache Auron (formerly Blaze) is an incubating open-source engine that maps Spark physical plans onto DataFusion native execution, with its own shuffle format. 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.
3.42 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 6 of 9
Open-source option with its own shuffle and memory layer
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 Apache Auron and Speedata score on each criterion?
| Criterion | Auron | Speedata | Higher score |
|---|---|---|---|
| Adoption effort 20% | 3.4 | 2.6 | Auron |
| Stage coverage 20% | 3.8 | 2.6 | Auron |
| Tuning and operating burden 20% | 2.6 | 3.0 | Speedata |
| Platform and instance portability 10% | 3.8 | 1.6 | Auron |
| Cost model transparency 5% | 5.0 | 2.0 | Auron |
| Published evidence 15% | 3.4 | 3.0 | Auron |
| Maturity and community 10% | 3.2 | 2.6 | Auron |
| ETL fit score | 3.42 | 2.61 | Auron |
Why each score
- Adoption effort
- Auron (3.4): No code changes, but you enable it through Spark settings and a custom shuffle manager and install it yourself.
- 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
- Auron (3.8): Native vectorized execution on DataFusion, compacted shuffle formats and multi-level memory management are all documented.
- 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
- Auron (2.6): Self-managed with community support through the Apache mailing list.
- 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
- Auron (3.8): Runs on standard CPU instances and supports JDK 8, 11, 17 and 21; the project says it is adapted to Spark mainline versions without listing them on the page we reviewed.
- 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
- Auron (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
- Auron (3.4): The project states about 2x faster than Spark 3.5 on TPC-DS with about 50% cluster resources saved; project-published.
- 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
- Auron (3.2): Still incubating at the Apache Software Foundation (latest listed release v8.0.0-incubating), with a history under the Blaze name.
- 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 Apache Auron and Speedata cost, as published?
| Auron | Speedata | |
|---|---|---|
| License | Apache License 2.0 | Commercial |
| Published price | No license fee | Not published; book a demo |
| Instances | Standard CPU instances | Servers fitted with Speedata C200 APU cards |
| Runs on | Self-managed Spark on JDK 8, 11, 17 or 21 | 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; Spark settings and custom shuffle manager | 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: auron.apache.org, apache/auron on GitHub, kwai/blaze on GitHub, 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 |
|---|---|---|---|---|---|
| Auron | Partial | 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 Apache Auron if
- You want no license fee: Auron is released under the Apache License 2.0
- You want an open-source engine with its own compacted shuffle format and multi-level memory management
- 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, Apache Auron or Speedata?
Apache Auron, with 3.42 against 2.61 out of 5 on our lead weights. Apache Auron 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 Apache Auron 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 Apache Auron and Speedata cost?
Apache Auron: 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.