Head to head · Figures checked September 2026
Apache Auron vs DataPelago
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 3.14 out of 5). Apache Auron scores higher on stage coverage, platform and instance portability, cost model transparency, published evidence and maturity and community; DataPelago scores higher on adoption effort and 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 DataPelago ranks 8th 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. DataPelago Accelerator for Spark is a commercial Spark accelerator (DPA-S) built on the DataPelago Nucleus engine, using CPU, GPU or FPGA hardware. 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
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
How do Apache Auron and DataPelago score on each criterion?
| Criterion | Auron | DataPelago | Higher score |
|---|---|---|---|
| Adoption effort 20% | 3.4 | 3.8 | DataPelago |
| Stage coverage 20% | 3.8 | 3.0 | Auron |
| Tuning and operating burden 20% | 2.6 | 3.0 | DataPelago |
| Platform and instance portability 10% | 3.8 | 3.2 | Auron |
| Cost model transparency 5% | 5.0 | 3.0 | Auron |
| Published evidence 15% | 3.4 | 3.0 | Auron |
| Maturity and community 10% | 3.2 | 2.6 | Auron |
| ETL fit score | 3.42 | 3.14 | 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.
- 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.
- Stage coverage
- Auron (3.8): Native vectorized execution on DataFusion, compacted shuffle formats and multi-level memory management are all documented.
- 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.
- Tuning and operating burden
- Auron (2.6): Self-managed with community support through the Apache mailing list.
- 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.
- 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.
- 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.
- Cost model transparency
- Auron (5.0): Apache License 2.0, no license fee.
- 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.
- 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.
- 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.
- 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.
- 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.
What do Apache Auron and DataPelago cost, as published?
| Auron | DataPelago | |
|---|---|---|
| License | Apache License 2.0 | Commercial |
| Published price | No license fee | 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 |
| Instances | Standard CPU instances | Any available hardware, CPU to GPU (vendor statement) |
| Runs on | Self-managed Spark on JDK 8, 11, 17 or 21 | Multiple Spark versions on Google Cloud and AWS; Amazon EMR, EKS and Databricks not named on the DPA-S page |
| Code changes | None; Spark settings and custom shuffle manager | None stated; standard Spark settings |
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, datapelago.ai, DPA-S page, DataPelago press page, DataPelago company page, AWS Marketplace listing · Fetched 27 Sep 2026
Where are they documented to run?
| EMR | Databricks | Google Cloud (Dataproc) | AWS Glue | Self-managed Spark / Kubernetes | |
|---|---|---|---|---|---|
| Auron | Not documentedself-install | Not documented | Not documentedself-install | Not documented | Documented |
| DataPelago | Not documentedAWS stated; EMR not named | Not documented | Not documentedGoogle Cloud stated; service not named | Not documented | Not 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 |
|---|---|---|---|---|---|
| Auron | 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 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 DataPelago if
- Low adoption effort on your current platform matters most
- You need to run on several platforms or standard instances
Frequently asked questions
Which has the higher ETL fit score, Apache Auron or DataPelago?
Apache Auron, with 3.42 against 3.14 out of 5 on our lead weights. Apache Auron scores higher on stage coverage, platform and instance portability, cost model transparency, published evidence and maturity and community and DataPelago on adoption effort and tuning and operating burden. The score measures fit for speeding up existing Spark ETL, not raw speed.
Do Apache Auron and DataPelago run on the same platforms?
They share no documented platform on the pages we reviewed. Apache Auron 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 Apache Auron and DataPelago cost?
Apache Auron: No license fee (Apache License 2.0). 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). For a like-for-like comparison, work out cost per run on one of your own jobs: see Spark cost per job, explained.