Spark accelerator FAQ

A plugin or engine that runs parts of a Spark job's physical plan faster than standard Spark while keeping the Spark API, so SQL and DataFrame code stays the same. Gluten, Comet, Auron, Flarion and Photon use native CPU engines, the RAPIDS Accelerator uses NVIDIA GPUs, and DualBird pairs a Spark plugin with Amazon EC2 F2 instances. See how accelerators plug into Spark.

None of the seven we track require changes to Spark SQL or DataFrame code, according to their own documentation. Setup differs: open-source plugins need jars, off-heap memory and shuffle-manager settings, the RAPIDS Accelerator needs GPU instances, DualBird states you change the instance type and add its plugin, and Photon is a setting inside Databricks.

No. Amazon EMR is a platform that runs Spark and ships its own optimized Spark runtime. You do not have to leave EMR to use an accelerator: DualBird, the RAPIDS Accelerator and Flarion list EMR, and open-source plugins can usually be installed on it. See where Spark accelerators run.

A platform provisions clusters, schedules jobs and ships a Spark runtime: Amazon EMR, Databricks, Google Cloud's managed Spark service (Dataproc), AWS Glue or self-managed Spark on Kubernetes. An accelerator sits under the Spark API on that platform and replaces parts of how the plan executes. Photon is the one accelerator here that only exists inside a platform, Databricks.

One of four things in most cases: shuffle (data moved between stages), disk spill (tasks running out of execution memory), data skew (a few partitions much larger than the rest) and small files. The profiling guide shows how to tell which one you have from the Spark UI.

They fall back to standard Spark execution, so the job still finishes. The cost is that those parts get no speedup and data may be converted between formats at each boundary. See fallback and the lesson on running an accelerator in production.

Apache Gluten, Apache DataFusion Comet, Apache Auron and the RAPIDS Accelerator are released under the Apache License 2.0 with no license fee. The RAPIDS Accelerator needs NVIDIA GPU instances, and all of them need engineering time for tuning and support. See open-source or commercial accelerators.

None of the commercial products publish a price we could use in a calculation. DualBird does not publish pricing on the pages we reviewed, Flarion has no price on its homepage and is listed on AWS Marketplace, and Photon is billed through Databricks DBUs at rates that differ from non-Photon compute.

Use cost per run on one of your own jobs, not the speedup multiple. Cost per run is the cluster's total hourly cost (instances, platform fee and any accelerator fee) times the runtime in hours. See Spark cost per job, explained.

No. Each vendor measures a different workload against a different baseline and cluster. AWS states that the Amazon EMR runtime for Spark is up to 5.4x faster than open-source Apache Spark, so a figure measured against open-source Spark and one measured against EMR do not start from the same place. See how to read a Spark accelerator benchmark.

Pick one real pipeline, record a baseline from Spark event logs on the platform you run today, run the product on the same data at two or three cluster sizes, and set a pass or fail threshold before the first run. Check output tables and fallback as well as speed. See how to run a Spark accelerator proof of concept.

On the pages we reviewed, DualBird, the RAPIDS Accelerator and Flarion list Amazon EMR. Gluten, Comet and Auron do not publish EMR guides, but as Spark plugins they can generally be added where you control Spark configuration. Photon runs only on Databricks.

An open-source Spark plugin that offloads Spark SQL execution to native engines, mainly Velox (C++) and also ClickHouse. It ranks 1st of 7 in this edition (3.9), scores strongest on cost model transparency and maturity and community and weakest on tuning and operating burden and adoption effort. See the Gluten review and Gluten alternatives.

An open-source Spark plugin that runs supported operators on the Apache DataFusion engine (Rust, on Apache Arrow). It reached 1.0.0 on 7 August 2026 and ranks 2nd of 7 in this edition (3.8), strongest on cost model transparency and platform and instance portability and weakest on tuning and operating burden and stage coverage. See the Comet review.

NVIDIA's open-source plugin, now published as NVIDIA cuDF for Apache Spark, that runs supported Spark SQL and DataFrame operations on NVIDIA GPUs. It ranks 3rd of 7 in this edition (3.7), strongest on maturity and community and stage coverage and weakest on tuning and operating burden and platform and instance portability. See the RAPIDS Accelerator review.

A commercial Spark plugin paired with Amazon EC2 F2 instances, aimed at spill, skew and shuffle bottlenecks, and documented for Apache Spark, Amazon EMR and Amazon EKS on AWS. It ranks 4th of 7 in this edition (3.6), strongest on stage coverage and adoption effort and weakest on cost model transparency and maturity and community. See the DualBird review.

An incubating Apache project, formerly called Blaze, that maps Spark physical plans onto DataFusion native execution with its own shuffle format. It ranks 5th of 7 in this edition (3.5), strongest on cost model transparency and stage coverage and weakest on tuning and operating burden and maturity and community. See the Auron review.

The Databricks-native vectorized C++ engine inside the Databricks Runtime: one setting on classic compute and on by default on serverless. It ranks 6th of 7 on our weights (3.4), strongest on tuning and operating burden and maturity and community and weakest on platform and instance portability and adoption effort, largely because it only runs on Databricks. See the Photon review.

A commercial DataFusion-based, Arrow-native execution engine for Spark, Hadoop and Ray that lists Databricks, Amazon EMR, Google Cloud Dataproc, Azure HDInsight and on-premises. Flarion states 3x performance and a 60% cost reduction, without a published methodology on the pages we reviewed. It ranks 7th of 7 in this edition (3.1). See the Flarion review.

Each product gets a 0 to 5 score on seven criteria: adoption effort, stage coverage, tuning and operating burden, platform and instance portability, cost model transparency, published evidence, and maturity and community. The ETL fit score is the weighted average, and ranks use the unrounded total. See how we score.

Apache Gluten has the highest ETL fit score in this edition (3.9). The best choice still depends on your platform and your slowest stage, and the calculator re-ranks the products on your own weights.

No. The scores come from desk research on public vendor and project pages, checked in September 2026. We did not install, run or benchmark any product, and every speed or cost figure on the site is quoted from the vendor or project with a link.

Yes. The calculator applies your weights to the same criterion scores and shows how the ranking moves. Compare side by side shows every score and its reason for up to five products.

Figures were last checked in September 2026. Send corrections to editors@etlcompare.com with a link to a public source; we check it, update the data and the scores recompute. See corrections.