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Software · head to head

Dask vs DVC

Dask logo

Dask

Software

Scalable analytics in Python

From
Free
Rated
-
Full reviewWebsite
DVC logo

DVC

Software

Data version control for machine learning projects

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Dask each Dask task carries between 200 microseconds and 1 millisecond of scheduler overhead, so graphs of millions of tasks add 10 minutes to hours of pure overhead; DVC dVC is Apache 2.0 licensed open source with no enterprise tier or paid support offering documented in the project itself; teams needing SLA-backed support get nothing from the DVC project directly.
  • They diverge on capability: Dask covers Parallel computing, DVC covers Data versioning.

Where they differ

Only the attributes on which Dask and DVC actually diverge.

Attributes where Dask and DVC differ
AttributeDaskDVC
Founded20152018

Identical on both: starting price (Free), pricing model (open-source), free tier (Yes), platforms (Linux, Mac, Windows), user rating (Not yet rated), category (Unknown).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Dask

  • Parallel computing
  • Distributed DataFrames
  • Lazy evaluation
  • Dynamic task scheduling
  • Dashboard
  • NumPy
  • Pandas
  • scikit-learn

Only in DVC

  • Data versioning
  • Pipeline management
  • Experiment tracking
  • Remote storage
  • Git integration
  • Git
  • S3
  • Azure Blob

Both cover

  • Linux support
  • Mac support
  • Windows support

What people use each for

The jobs each tool is most often brought in to do.

Dask

  • Scaling pandas and NumPy workloads beyond a single machine's memorynot DVC
  • Parallelising custom Python task graphsnot DVC
  • Processing larger than memory arrays and dataframes on a clusternot DVC

DVC

  • Machine learningnot Dask
  • Data analysisnot Dask
  • Model trainingnot Dask
  • Predictive analyticsnot Dask

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Dask

  • Each Dask task carries between 200 microseconds and 1 millisecond of scheduler overhead, so graphs of millions of tasks add 10 minutes to hours of pure overhead
  • Partition sizing is left to the user: chunks must fit several times over in worker memory, and both oversized and undersized chunks are documented failure modes
  • Embedding large locally created DataFrames or Arrays into a Dask computation is documented as a practice to avoid because of network overhead
  • Calling compute repeatedly in a loop rather than batching prevents parallelisation of queries
  • The documentation itself advises trying better algorithms, file formats or sampling before adopting Dask

DVC

  • DVC is Apache 2.0 licensed open source with no enterprise tier or paid support offering documented in the project itself; teams needing SLA-backed support get nothing from the DVC project directly.

Pricing, plan by plan

Dask

Free
  • Open SourceFree
    • Parallel computing
    • Distributed DataFrames
    • ML integration

DVC

Free
  • Open SourceFree
    • Data versioning
    • Pipeline management
    • Experiment tracking
  • DVC StudioFree
    • Web UI
    • Team collaboration
    • Visualizations

Which should you pick?

Choose Dask if

  • You need parallel computing.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want distributed dataframes.
Dask review

Choose DVC if

  • You need data versioning.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want pipeline management.
DVC review

Questions people ask

Is Dask or DVC better?
Neither clearly leads. Dask starts at Free and DVC at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dask or DVC?
Dask starts at Free and DVC at Free.
Does Dask or DVC run on more platforms?
Both run on Linux, Mac, Windows, so platform support will not decide this one for you.
Can I use Dask for free?
Both have a free tier, so you can try either at no cost before committing.
What is Dask best used for?
Dask is most often used for scaling pandas and numpy workloads beyond a single machine's memory, parallelising custom python task graphs, processing larger than memory arrays and dataframes on a cluster. Of those, scaling pandas and numpy workloads beyond a single machine's memory and parallelising custom python task graphs are not what DVC is typically brought in for.
What can Dask do that DVC cannot?
Dask covers Parallel computing, Distributed DataFrames, Lazy evaluation, Dynamic task scheduling. DVC covers Data versioning, Pipeline management, Experiment tracking, Remote storage. Both handle Linux support, Mac support, Windows support.
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