ETL CompareETL Compare

Head to head · Figures checked September 2026

Apache Gluten vs Speedata

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 2.61 out of 5). Apache Gluten scores higher on adoption effort, stage coverage, platform and instance portability, cost model transparency, published evidence and maturity and community; Speedata scores higher on 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 Gluten ranks 1st of 9 and Speedata ranks 9th of 9. Which one fits depends on the criteria below and on the platform you run.

Apache Gluten (with Velox) is an open-source plugin that offloads Spark SQL execution to a native C++ engine (Velox or ClickHouse). 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.

Apache Gluten

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

Speedata

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 Apache Gluten and Speedata score on each criterion?

Apache Gluten and Speedata by criterion. Scores are editorial, 0-5.
CriterionGluten + VeloxSpeedataHigher score
Adoption effort 20%3.82.6Gluten + Velox
Stage coverage 20%3.82.6Gluten + Velox
Tuning and operating burden 20%2.63.0Speedata
Platform and instance portability 10%4.31.6Gluten + Velox
Cost model transparency 5%5.02.0Gluten + Velox
Published evidence 15%3.83.0Gluten + Velox
Maturity and community 10%4.42.6Gluten + Velox
ETL fit score3.732.61Gluten + Velox
Why each score
Adoption effort
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.
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
Gluten + Velox (3.8): Offloads execution to the Velox native engine with a columnar shuffle manager; spilling is documented as experimental.
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
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.
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
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.
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
Gluten + Velox (5.0): Apache License 2.0, no license fee; cost is your existing compute plus engineering time.
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
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.
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
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.
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 Apache Gluten and Speedata cost, as published?

 Gluten + VeloxSpeedata
LicenseApache License 2.0Commercial
Published priceNo license feeNot published; book a demo
InstancesStandard CPU instances (x86_64 or aarch64)Servers fitted with Speedata C200 APU cards
Runs onSelf-managed Spark 3.4 to 4.1 on Linux, on any platform where you control Spark configSpeedata 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 changesNone; JAR plus Spark configurationNone 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: gluten.apache.org, Velox backend getting started, apache/gluten on GitHub, speedata.io, Speedata product page, Speedata news, Speedata company page · Fetched 27 Sep 2026

Where are they documented to run?

 EMRDatabricksGoogle Cloud (Dataproc)AWS GlueSelf-managed Spark / Kubernetes
Gluten + VeloxNot documentedself-installNot documentedNot documentedself-installNot documentedDocumented
SpeedataNot documentedNot documentedNot documentedNot documentedDocumentedon Speedata APU servers, on premises

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?

Spark ETL job stages and which stages each accelerator documents addressingA waterfall of five Spark ETL job stages (read, transform, shuffle, spill, write) with illustrative proportions, and below it a grid showing, for each accelerator, whether its own documentation says it addresses that stage.Anatomy of a Spark ETL jobRead: scan and decode filesTransform: filter, join, aggregateShuffle: write and fetch between stagesSpill: memory pressure pushes data to diskWrite: encode and commit outputIllustrative proportions, not measured data. Your own split comes from the Spark UI: see Profile a slow Spark job.
Stage coverage by accelerator, from vendor documentation
AcceleratorReadTransformShuffleSpillWrite
Gluten + VeloxPartialPartialDocumentedDocumentedDocumentedDocumentedPartialPartialNot statedNot stated
SpeedataPartialPartialPartialPartialNot statedNot statedNot statedNot statedNot statedNot stated
  • DocumentedDocumented: the vendor's own documentation says it addresses this stage
  • PartialPartial: indirect or experimental, or covered only by an end-to-end claim
  • Not statedNot 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 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

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, Apache Gluten or Speedata?

Apache Gluten, with 3.73 against 2.61 out of 5 on our lead weights. Apache Gluten scores higher on adoption effort, stage coverage, platform and instance portability, cost model transparency, published evidence and maturity and community and Speedata on tuning and operating burden. The score measures fit for speeding up existing Spark ETL, not raw speed.

Do Apache Gluten and Speedata run on the same platforms?

Both are 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 Gluten and Speedata cost?

Apache Gluten: No license fee (Apache License 2.0). 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.

Related