Head to head · Figures checked September 2026
DataPelago vs Apache Gluten
ETL Compare staff · Figures checked September 2026 · Sourced from vendor docs, project pages and public benchmarks · Published 29 September 2026
In brief
Apache Gluten has the higher ETL fit score on our lead weights (3.73 against 3.14 out of 5). DataPelago scores higher on tuning and operating burden; Apache Gluten scores higher on stage coverage, platform and instance portability, cost model transparency, published evidence and maturity and community. They tie on adoption effort. 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 Gluten ranks 1st 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. Apache Gluten (with Velox) is an open-source plugin that offloads Spark SQL execution to a native C++ engine (Velox or ClickHouse). 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
3.73 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 1 of 9
Open-source native engine for self-managed Spark
How do DataPelago and Apache Gluten score on each criterion?
| Criterion | DataPelago | Gluten + Velox | Higher score |
|---|---|---|---|
| Adoption effort 20% | 3.8 | 3.8 | Tie |
| Stage coverage 20% | 3.0 | 3.8 | Gluten + Velox |
| Tuning and operating burden 20% | 3.0 | 2.6 | DataPelago |
| Platform and instance portability 10% | 3.2 | 4.3 | Gluten + Velox |
| Cost model transparency 5% | 3.0 | 5.0 | Gluten + Velox |
| Published evidence 15% | 3.0 | 3.8 | Gluten + Velox |
| Maturity and community 10% | 2.6 | 4.4 | Gluten + Velox |
| ETL fit score | 3.14 | 3.73 | Gluten + Velox |
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.
- Gluten + Velox (3.8): No application code changes; you add the Gluten JAR, set spark.plugins, enable off-heap memory and switch the shuffle manager, per the Velox getting-started page.
- 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.
- Gluten + Velox (3.8): Offloads execution to the Velox native engine with a columnar shuffle manager; spilling is documented as experimental.
- 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.
- Gluten + Velox (2.6): You size off-heap memory yourself (the docs example uses 20g) and rely on community channels; no vendor support contract is described on the project pages.
- 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.
- Gluten + Velox (4.3): Supports Spark 3.4, 3.5, 4.0 and 4.1 on x86_64 and aarch64 Linux on standard CPU instances; managed-platform guides are not listed, so you install it yourself.
- 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.
- Gluten + Velox (5.0): Apache License 2.0, no license fee; cost is your existing compute plus engineering time.
- 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.
- Gluten + Velox (3.8): Publishes TPC-H and TPC-DS results (Velox backend 2.71x overall, tested June 2023) with the benchmark named; results come from the project.
- 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.
- Gluten + Velox (4.4): Apache top-level project since March 2026, started by Intel and Kyligence in 2022, with contributors including Alibaba Cloud, Meituan, Microsoft, IBM and Google.
What do DataPelago and Apache Gluten cost, as published?
| DataPelago | Gluten + Velox | |
|---|---|---|
| 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 license fee |
| Instances | Any available hardware, CPU to GPU (vendor statement) | Standard CPU instances (x86_64 or aarch64) |
| Runs on | Multiple Spark versions on Google Cloud and AWS; Amazon EMR, EKS and Databricks not named on the DPA-S page | Self-managed Spark 3.4 to 4.1 on Linux, on any platform where you control Spark config |
| Code changes | None stated; standard Spark settings | None; JAR plus Spark configuration |
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, gluten.apache.org, Velox backend getting started, apache/gluten 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 |
| Gluten + Velox | Not documentedself-install | Not documented | Not documentedself-install | 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 |
|---|---|---|---|---|---|
| Gluten + Velox | Partial | Documented | Documented | Partial | Not stated |
| 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 Apache Gluten if
- You want no license fee: Gluten is released under the Apache License 2.0
- You want the most widely contributed open-source native engine in this set, an Apache top-level project since March 2026
- You run on self-managed Spark or Kubernetes
Frequently asked questions
Which has the higher ETL fit score, DataPelago or Apache Gluten?
Apache Gluten, with 3.73 against 3.14 out of 5 on our lead weights. DataPelago scores higher on tuning and operating burden and Apache Gluten on stage coverage, platform and instance portability, cost model transparency, published evidence and maturity and community. They tie on adoption effort. The score measures fit for speeding up existing Spark ETL, not raw speed.
Do DataPelago and Apache Gluten run on the same platforms?
They share no documented platform on the pages we reviewed. Apache Gluten is also 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 DataPelago and Apache Gluten 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). Apache Gluten: No license fee (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.