Head to head · Figures checked September 2026
DualBird vs Speedata
ETL Compare staff · Figures checked September 2026 · Sourced from vendor docs, project pages and public benchmarks · Published 29 September 2026
In brief
DualBird has the higher ETL fit score on our lead weights (3.72 against 2.61 out of 5). DualBird scores higher on adoption effort, stage coverage, tuning and operating burden and platform and instance portability; Speedata scores higher on published evidence and maturity and community. They tie on cost model transparency. 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 DualBird ranks 2nd of 9 and Speedata ranks 9th of 9. Which one fits depends on the criteria below and on the platform you run.
DualBird is a commercial Spark plugin paired with Amazon EC2 F2 instances, aimed at spill, skew and shuffle bottlenecks. 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.
3.72 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 2 of 9
Best for spill- and shuffle-heavy ETL on EMR and EKS
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 DualBird and Speedata score on each criterion?
| Criterion | DualBird | Speedata | Higher score |
|---|---|---|---|
| Adoption effort 20% | 4.6 | 2.6 | DualBird |
| Stage coverage 20% | 4.8 | 2.6 | DualBird |
| Tuning and operating burden 20% | 4.4 | 3.0 | DualBird |
| Platform and instance portability 10% | 2.4 | 1.6 | DualBird |
| Cost model transparency 5% | 2.0 | 2.0 | Tie |
| Published evidence 15% | 2.8 | 3.0 | Speedata |
| Maturity and community 10% | 2.0 | 2.6 | Speedata |
| ETL fit score | 3.72 | 2.61 | DualBird |
Why each score
- Adoption effort
- DualBird (4.6): DualBird states two steps: change the EC2 instance type and add the DualBird Spark plugin, with no code changes and no platform move for EMR and EKS users.
- 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
- DualBird (4.8): The only vendor in this set whose product page names all three ETL pain points directly: disk spills, data skew and shuffle data, plus an end-to-end claim.
- 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
- DualBird (4.4): DualBird states "no more performance tuning and troubleshooting" and the product is vendor-supported; this is a vendor claim we could not check.
- 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
- DualBird (2.4): Documented for Apache Spark, Amazon EMR and Amazon EKS on AWS only, and it runs on Amazon EC2 F2 instances rather than the instance types most jobs use today.
- 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
- DualBird (2.0): No pricing on the pages we reviewed; buyers need a quote to compare the fee plus F2 instance cost against current spend.
- 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
- DualBird (2.8): Publishes an Iceberg compaction benchmark with dataset shape and cost per TB, written by DualBird staff; cluster sizes and Spark versions are not stated and no third-party results were found.
- 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
- DualBird (2.0): A young commercial product (it raised $25M in February 2026) with a short public track record and no open-source community.
- 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 DualBird and Speedata cost, as published?
| DualBird | Speedata | |
|---|---|---|
| License | Commercial | Commercial |
| Published price | Not published; contact DualBird | Not published; book a demo |
| Instances | Amazon EC2 F2 instances | Servers fitted with Speedata C200 APU cards |
| Runs on | Apache Spark, Amazon EMR and Amazon EKS, on AWS | 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 stated; change instance type and add plugin | 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: dualbird.io, DualBird product page, Iceberg compaction benchmark, A Simpler Spark, 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 |
|---|---|---|---|---|---|
| DualBird | Partial | Documented | Documented | Documented | Partial |
| 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 DualBird if
- Your slowest stages are disk spill and shuffle, which DualBird's product page targets directly
- You run on Amazon EMR or EKS and want to stay there: DualBird states setup is an instance-type change plus a Spark plugin
- You run on Amazon EMR and 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, DualBird or Speedata?
DualBird, with 3.72 against 2.61 out of 5 on our lead weights. DualBird scores higher on adoption effort, stage coverage, tuning and operating burden and platform and instance portability and Speedata on published evidence and maturity and community. They tie on cost model transparency. The score measures fit for speeding up existing Spark ETL, not raw speed.
Do DualBird and Speedata run on the same platforms?
Both are documented for self-managed Spark or Kubernetes. DualBird is also documented for Amazon EMR.
What do DualBird and Speedata cost?
DualBird: Not published; contact DualBird (Commercial). 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.