Head to head · Figures checked September 2026
DataPelago vs RAPIDS Accelerator
ETL Compare staff · Figures checked September 2026 · Sourced from vendor docs, project pages and public benchmarks · Published 29 September 2026
In brief
RAPIDS Accelerator has the higher ETL fit score on our lead weights (3.65 against 3.14 out of 5). DataPelago scores higher on adoption effort; RAPIDS Accelerator scores higher on stage coverage, cost model transparency, published evidence and maturity and community. They tie on tuning and operating burden and platform and instance portability. 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 RAPIDS Accelerator ranks 4th of 9 and DataPelago ranks 8th of 9. Which one fits depends on the criteria below and on the platform you run.
DataPelago Accelerator for Spark is a commercial Spark accelerator (DPA-S) built on the DataPelago Nucleus engine, using CPU, GPU or FPGA hardware. RAPIDS Accelerator for Apache Spark (now NVIDIA cuDF for Apache Spark) is an open-source NVIDIA plugin that runs supported Spark SQL and DataFrame operations on GPUs. Neither requires changes to Spark SQL or DataFrame code, according to its own documentation.
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
3.65 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 4 of 9
Best for teams that already run GPU capacity
How do DataPelago and RAPIDS Accelerator score on each criterion?
| Criterion | DataPelago | RAPIDS Accelerator | Higher score |
|---|---|---|---|
| Adoption effort 20% | 3.8 | 3.4 | DataPelago |
| Stage coverage 20% | 3.0 | 4.2 | RAPIDS Accelerator |
| Tuning and operating burden 20% | 3.0 | 3.0 | Tie |
| Platform and instance portability 10% | 3.2 | 3.2 | Tie |
| Cost model transparency 5% | 3.0 | 4.2 | RAPIDS Accelerator |
| Published evidence 15% | 3.0 | 3.6 | RAPIDS Accelerator |
| Maturity and community 10% | 2.6 | 4.6 | RAPIDS Accelerator |
| ETL fit score | 3.14 | 3.65 | RAPIDS Accelerator |
Why each score
- Adoption effort
- 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.
- RAPIDS Accelerator (3.4): No code changes, but the job has to move to NVIDIA GPU instances and the cluster needs GPU-specific configuration.
- Stage coverage
- 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.
- RAPIDS Accelerator (4.2): Documents GPU execution for group by, joins, sorts and windows, Parquet and ORC writing, CSV reading and a RAPIDS Shuffle Manager; unsupported operations fall back to CPU.
- Tuning and operating burden
- 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.
- RAPIDS Accelerator (3.0): Qualification and Profiling tools help, but GPU sizing and plugin configuration are your team's job.
- Platform and instance portability
- 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.
- RAPIDS Accelerator (3.2): The widest platform list in this set (EMR, Databricks, Dataproc, GKE, Azure Synapse, Kubernetes, on-premises, OCI), held back because every one of them needs GPU instances.
- Cost model transparency
- 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.
- RAPIDS Accelerator (4.2): The plugin is Apache 2.0 with no fee; the cost question becomes GPU instance price against runtime saved, which you can model from public cloud prices.
- Published evidence
- 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.
- RAPIDS Accelerator (3.6): The Qualification Tool estimates fit from your own event logs; headline benchmark figures were not reviewed for this edition.
- Maturity and community
- 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.
- RAPIDS Accelerator (4.6): A long-running NVIDIA project with more than 9,000 commits on main, now published as NVIDIA cuDF for Apache Spark.
What do DataPelago and RAPIDS Accelerator cost, as published?
| DataPelago | RAPIDS Accelerator | |
|---|---|---|
| License | Commercial | Apache License 2.0 |
| Published price | 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 | No plugin fee; GPU instance pricing applies |
| Instances | Any available hardware, CPU to GPU (vendor statement) | NVIDIA GPU instances (Volta or later) |
| Runs on | Multiple Spark versions on Google Cloud and AWS; Amazon EMR, EKS and Databricks not named on the DPA-S page | Amazon EMR, Databricks, Dataproc, GKE, Azure Synapse, Kubernetes, on-premises, OCI |
| Code changes | None stated; standard Spark settings | None; plugin replaces internal physical plan parts |
Prices and terms as published on the pages we reviewed, 27 September 2026. Neither vendor's figure is a quote.
Sources: datapelago.ai, DPA-S page, DataPelago press page, DataPelago company page, AWS Marketplace listing, spark-rapids overview, RAPIDS Accelerator user guide, RAPIDS Accelerator FAQ, NVIDIA/spark-rapids on GitHub · Fetched 27 Sep 2026
Where are they documented to run?
| EMR | Databricks | Google Cloud (Dataproc) | AWS Glue | Self-managed Spark / Kubernetes | |
|---|---|---|---|---|---|
| DataPelago | Not documentedAWS stated; EMR not named | Not documented | Not documentedGoogle Cloud stated; service not named | Not documented | Not documented |
| RAPIDS Accelerator | Documented | Documented | Documented | Not documented | 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 |
|---|---|---|---|---|---|
| RAPIDS Accelerator | Documented | Documented | Documented | Not stated | Documented |
| 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 DataPelago if
- Low adoption effort on your current platform matters most
- You need to run on several platforms or standard instances
Choose RAPIDS Accelerator if
- You want a long-running project with more than 9,000 commits on main
- You want GPU execution documented for joins, sorts, aggregations, window functions, Parquet and ORC writing and shuffle
- You run on Amazon EMR, Databricks, Google Cloud (Dataproc) and self-managed Spark or Kubernetes
Frequently asked questions
Which has the higher ETL fit score, DataPelago or RAPIDS Accelerator?
RAPIDS Accelerator, with 3.65 against 3.14 out of 5 on our lead weights. DataPelago scores higher on adoption effort and RAPIDS Accelerator on stage coverage, cost model transparency, published evidence and maturity and community. They tie on tuning and operating burden and platform and instance portability. The score measures fit for speeding up existing Spark ETL, not raw speed.
Do DataPelago and RAPIDS Accelerator run on the same platforms?
They share no documented platform on the pages we reviewed. RAPIDS Accelerator is also documented for Amazon EMR, Databricks, Google Cloud (Dataproc) and self-managed Spark or Kubernetes.
What do DataPelago and RAPIDS Accelerator cost?
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). RAPIDS Accelerator: No plugin fee; GPU instance pricing applies (Apache License 2.0). For a like-for-like comparison, work out cost per run on one of your own jobs: see Spark cost per job, explained.