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Machine Learning & Data Science · head to head

Dask vs Orange

Dask logo

Dask

Machine Learning & Data Science

Scalable analytics in Python

From
Free
Rated
-
Full reviewWebsite
Orange logo

Orange

Machine Learning & Data Science

Data mining and visualization toolkit

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; Orange orange is licensed under the GNU General Public License version 3, so distributing modified or derived software requires releasing the source under the GPL
  • They diverge on capability: Dask covers Parallel computing, Orange covers Visual programming.

Where they differ

Only the attributes on which Dask and Orange actually diverge.

Attributes where Dask and Orange differ
AttributeDaskOrange
Founded20151996

Identical on both: starting price (Free), pricing model (open-source), free tier (Yes), platforms (Linux, Mac, Windows), 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
  • XGBoost

Only in Orange

  • Visual programming
  • Data visualization
  • Machine learning
  • Text mining
  • Bioinformatics
  • Python
  • PyQt

Both cover

  • scikit-learn
  • 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 Orange
  • Parallelising custom Python task graphsnot Orange
  • Processing larger than memory arrays and dataframes on a clusternot Orange

Orange

  • Visual programming for data mining and machine learning workflowsnot Dask
  • Teaching data science without writing codenot Dask
  • Exploratory data visualisation and clustering on tabular datanot 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

Orange

  • Orange is licensed under the GNU General Public License version 3, so distributing modified or derived software requires releasing the source under the GPL
  • The widgets and canvas are built on Qt, which is itself distributed under GPL 3.0
  • Orange add-ons may carry additional licensing requirements set in their own licence files
  • Documentation and website content are under Creative Commons Attribution-ShareAlike, which imposes an attribution and share-alike obligation on reuse
  • The software is distributed without any warranty of merchantability or fitness for a particular purpose

Pricing, plan by plan

Dask

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

Orange

Free
  • Open SourceFree
    • Visual programming
    • Machine learning
    • Data visualization

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 Orange if

  • You need visual programming.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want data visualization.
Orange review

Questions people ask

Is Dask or Orange better?
Neither clearly leads. Dask starts at Free and Orange at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dask or Orange?
Dask starts at Free and Orange at Free.
Does Dask or Orange 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 Orange is typically brought in for.
What can Dask do that Orange cannot?
Dask covers Parallel computing, Distributed DataFrames, Lazy evaluation, Dynamic task scheduling. Orange covers Visual programming, Data visualization, Machine learning, Text mining. Both handle scikit-learn, Linux support, Mac support, Windows support.
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