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Machine Learning & Data Science · head to head

Dask vs Dataiku

Dask logo

Dask

Machine Learning & Data Science

Scalable analytics in Python

From
Free
Rated
-
Full reviewWebsite
Dataiku logo

Dataiku

Machine Learning & Data Science

Everyday AI, Extraordinary People

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; Dataiku no pricing is published at any tier, and the plans page carries no figures at all
  • They diverge on capability: Dask covers Parallel computing, Dataiku covers Visual data prep.

Where they differ

Only the attributes on which Dask and Dataiku actually diverge.

Attributes where Dask and Dataiku differ
AttributeDaskDataiku
Pricing modelopen-sourcefreemium
PlatformsLinux, Mac, WindowsLinux, Mac, Windows, Web
Founded20152013

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning & Data Science).

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 Dataiku

  • Visual data prep
  • AutoML
  • MLOps
  • Collaboration
  • Governence
  • Python
  • R
  • Spark

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 Dataiku
  • Parallelising custom Python task graphsnot Dataiku
  • Processing larger than memory arrays and dataframes on a clusternot Dataiku

Dataiku

  • Building and deploying data science and machine learning pipelinesnot Dask
  • Giving analysts and data scientists a shared visual and code environmentnot 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

Dataiku

  • No pricing is published at any tier, and the plans page carries no figures at all
  • User, row and compute limits are not stated, so nothing about scale can be assessed before contacting sales
  • Access begins with a demo request or a trial rather than a self serve signup

Pricing, plan by plan

Dask

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

Dataiku

Free
  • Free EditionFree
    • Single user
    • Core features
  • EnterpriseFree
    • Full platform
    • Collaboration
    • MLOps

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 Dataiku if

  • You need visual data prep.
  • You want to start without paying.
  • You work on Linux, Mac, Windows, Web.
  • You also want automl.
Dataiku review

Questions people ask

Is Dask or Dataiku better?
Neither clearly leads. Dask starts at Free and Dataiku at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dask or Dataiku?
Dask starts at Free and Dataiku at Free.
Does Dask or Dataiku run on more platforms?
Dask runs on Linux, Mac, Windows. Dataiku runs on Linux, Mac, Windows, Web.
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 Dataiku is typically brought in for.
What can Dask do that Dataiku cannot?
Dask covers Parallel computing, Distributed DataFrames, Lazy evaluation, Dynamic task scheduling. Dataiku covers Visual data prep, AutoML, MLOps, Collaboration. Both handle Linux support, Mac support, Windows support.
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