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Head to head · Figures checked September 2026

DualBird vs Apache DataFusion Comet

ETL Compare staff · Figures checked September 2026 · Sourced from vendor docs, project pages and public benchmarks

In brief

Comet scores higher overall (3.8 against 3.6): it is free, runs on commodity instances and publishes a TPC-DS benchmark. DualBird scores higher on adoption, stage coverage and operating burden, and targets disk spill directly where Comet's own tuning guide warns that memory accounting is not exact.

Comet for teams on Spark 3.5 or 4.x who want to try a free plugin this week; DualBird for EMR and EKS teams who want spill-heavy jobs fixed without owning the tuning.

DualBird

3.6 / 5

ETL fit score (editorial assessment, 0-5)

ETL fit score (editorial assessment, 0-5)

Rank 4 of 7

Best for spill- and shuffle-heavy ETL on EMR and EKS

Comet

3.8 / 5

ETL fit score (editorial assessment, 0-5)

ETL fit score (editorial assessment, 0-5)

Rank 2 of 7

Best for teams on recent Spark 4.x releases

Side-by-side

DualBird and Comet compared. A filled square marks the higher score.
 DualBirdComet
Adoption effort 20%4.6Higher3.8
Stage coverage 20%4.8Higher3.6
Tuning and operating burden 15%4.4Higher2.8
Platform and instance portability 10%2.44.2Higher
Cost model transparency 10%2.05.0Higher
Published evidence 15%2.83.8Higher
Maturity and community 10%2.03.9Higher
ETL fit score3.63.8Higher
ApproachCommercial Spark plugin paired with Amazon EC2 F2 instances, aimed at spill, skew and shuffle bottlenecksOpen-source Spark plugin that runs supported operators on the Apache DataFusion engine (Rust, Arrow)
InstancesAmazon EC2 F2Commodity CPU, Linux amd64 or arm64
PlatformsSpark, EMR, EKS on AWSSelf-managed Spark 3.5, 4.0, 4.1
LicenseCommercialApache 2.0
PriceNot publishedNo license fee
SetupInstance type plus pluginJar, extension, off-heap memory, CometShuffleManager
MemoryDualBird states many gigabytes per active taskShares Spark's off-heap pool; accounting “isn't 100% accurate”
SupportVendorCommunity

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
CometDocumentedDocumentedDocumentedDocumentedDocumentedDocumentedPartialPartialNot statedNot stated
DualBirdPartialPartialDocumentedDocumentedDocumentedDocumentedDocumentedDocumentedPartialPartial
  • 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.

How much tuning does each need?

Comet's tuning guide lists the settings you will touch: off-heap pool size, memory pool fraction and type, batch size, a per-task spill limit and shuffle mode. DualBird states there is no more performance tuning. Treat the DualBird claim as something to verify in a trial and the Comet list as the work you will do.

Which one is easier to evaluate?

Comet: it is free, the jars are on Maven Central and the benchmark is published. DualBird needs F2 capacity and a conversation with the vendor. That difference is why Comet leads on cost model transparency (5.0 against 2.0) and published evidence (3.8 against 2.8).

Choose DualBird if

  • You are on EMR or EKS
  • Spill dominates
  • You want vendor support
  • You can run a proof of concept on F2

Choose Comet if

  • You run Spark 3.5 or 4.x yourself
  • You want a free, quick trial on current instances
  • You can own memory settings

Frequently asked questions

Does Comet run on EMR?

The project does not publish an EMR guide. It is a Spark plugin with Linux jars, so it can generally be added where you control Spark configuration.

Which is faster?

Their published figures come from different workloads and baselines, so they cannot be compared. Test both on the same pipeline.

Sources

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