Method · Figures checked September 2026
How ETL Compare scores Spark ETL accelerators
ETL Compare staff · Figures checked September 2026
In brief
We score seven Spark accelerators from 0 to 5 on seven weighted criteria, using only public vendor and project pages fetched in September 2026. Totals are computed from the published weights. We did not run the products.
What does the ETL fit score measure?
The score answers one question: how well does this product fit a team that wants its existing Spark ETL jobs to run faster and cost less, without rewriting pipelines? It rewards low adoption effort, coverage of the job stages that usually dominate ETL runtime (shuffle and spill), and a low operating burden, and it also rewards the things that reduce buying risk: portability, a cost model you can work out, published evidence and maturity.
It does not measure peak speed. We have no basis to rank raw speed across products, because every vendor measures on a different workload against a different baseline.
Which criteria and weights do we use?
| Criterion | Weight | What we look for |
|---|---|---|
| Adoption effort | 20% | How much work to run an existing Spark ETL job on it, starting from the platform you already use? No code changes, few setup steps and no platform move score high. |
| Stage coverage | 20% | Which stages of an ETL job (read, transform, shuffle, spill, write) does the vendor's own documentation say it addresses, and what happens to unsupported operations? |
| Tuning and operating burden | 15% | After install, how much memory sizing, configuration and troubleshooting falls on your team, and who supports you when a job fails? |
| Platform and instance portability | 10% | How many Spark platforms is it documented for, and does it run on the instance types you already use? |
| Cost model transparency | 10% | Can you work out what it costs before a sales call: license or fee, plus any change in instance price? |
| Published evidence | 15% | Are performance figures published with enough setup detail to reproduce, and has anyone other than the vendor published results? |
| Maturity and community | 10% | How long has it been in use, who contributes, and how visible is its production track record? |
Each accelerator gets a score from 0 to 5, to one decimal, on each criterion, with a one-line reason shown on its review. The ETL fit score is the weighted average: the sum of score times weight, divided by 100. Ranks use the unrounded total. Where two totals are equal to three decimals, the rank shows as tied.
Scoring anchors
- 5: Best available on public evidence, with nothing material missing.
- 4: Strong, with one documented limitation.
- 3: Adequate, with gaps a buyer must fill in during a trial.
- 2: Weak or mostly undocumented on public pages.
- 1: Absent or contradicted by the vendor's own documentation.
- 0: Not applicable to the product.
Worked example: DualBird
| Criterion | Score x weight | Points |
|---|---|---|
| Adoption effort | 4.6 x 20 | 92.0 |
| Stage coverage | 4.8 x 20 | 96.0 |
| Tuning and operating burden | 4.4 x 15 | 66.0 |
| Platform and instance portability | 2.4 x 10 | 24.0 |
| Cost model transparency | 2.0 x 10 | 20.0 |
| Published evidence | 2.8 x 15 | 42.0 |
| Maturity and community | 2.0 x 10 | 20.0 |
| Total | 360.0 / 100 | 3.60, shown as 3.6 |
Why these weights?
Adoption effort and stage coverage carry the most weight (20% each) because they decide whether a product can speed up the job you have today. Tuning and operating burden (15%) matters because a plugin that needs weeks of memory tuning eats the saving. Published evidence (15%) matters because every vendor figure in this category is self-reported. Portability, cost model transparency and maturity (10% each) reduce buying risk but do not by themselves make a job faster.
Change the weights and the order changes. Photon rises if you already run Databricks, because its adoption effort drops to nearly zero for you. Open-source engines fall if you have no one to tune them. We publish every per-criterion score so you can reweight for your own situation.
What counts as evidence?
- Every fact on this site comes from a page we fetched in September 2026: vendor product pages, documentation, project home pages, GitHub repositories and cloud provider documentation. Each review lists its sources, and every fact block ends with a source line.
- Vendor performance and cost figures are quoted as “the vendor states” with a link. We never present them as our findings.
- Where a vendor does not publish something (a price, a platform guide, a supported version), we write “Not published” or “Not documented” and score conservatively.
- Stage coverage marks what each vendor's documentation says it addresses. “Documented” is a statement about the documentation, not a measurement.
What are the limitations?
- Public sources only. We used no private briefings and no vendor interviews.
- No hands-on testing. We did not install, run or benchmark any product.
- Documentation changes. Versions, platform lists and prices can change after September 2026. The review date is on every page.
- Some vendors publish far more than others. Open-source projects document configuration in detail; commercial vendors often keep detail behind a sales process. Our evidence and cost-model criteria reflect that gap on purpose.
How do we handle corrections?
Vendors and readers can send corrections to editors@etlcompare.com with a link to a public source. We check the source and update the data, and the scores recompute. Changes are dated on the affected page.
About ETL Compare
ETL Compare covers one question in depth: how to make Spark ETL jobs faster and cheaper without rewriting them. It compares accelerators, explains how to profile a slow job before buying anything, and explains how to work out cost per job. It is written by ETL Compare staff. Contact: editors@etlcompare.com.
Frequently asked questions
Did you benchmark the accelerators?
No. Scores come from public documentation and vendor pages. Any speed or cost figure on the site is quoted from the vendor or project, with a link.
Why is Amazon EMR not in the ranking?
EMR is a platform that runs Spark, not an accelerator. Several accelerators run on EMR. We cover platforms on the Where Spark accelerators run page (/platforms) without scoring them.
Can I change the weights?
Every per-criterion score is published, so you can apply your own weights. The method section above explains which way the order moves for common situations.
More questions: Spark accelerator FAQ