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Software · head to head

Dask vs KNIME

Dask logo

Dask

Software

Scalable analytics in Python

From
Free
Rated
-
Full reviewWebsite
KNIME logo

KNIME

Software

Open source data analytics and integration platform

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; KNIME the free Analytics Platform runs locally only, so anything shared or scheduled requires a paid Hub
  • They diverge on capability: Dask covers Parallel computing, KNIME covers Visual workflows.

Where they differ

Only the attributes on which Dask and KNIME actually diverge.

Attributes where Dask and KNIME differ
AttributeDaskKNIME
Pricing modelopen-sourcefreemium
Founded20152004

Identical on both: starting price (Free), 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 KNIME

  • Visual workflows
  • Data preprocessing
  • Machine learning
  • Visualization
  • Reporting
  • 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 KNIME
  • Parallelising custom Python task graphsnot KNIME
  • Processing larger than memory arrays and dataframes on a clusternot KNIME

KNIME

  • Building data pipelines and analytics workflows visually rather than in codenot Dask
  • Connecting and blending data across many sources for analysisnot 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

KNIME

  • The free Analytics Platform runs locally only, so anything shared or scheduled requires a paid Hub
  • The free AI assistant is limited to 20 interactions a month
  • Paid workflow runtime is metered in credits, with 120 included on Pro and overage at $0.025 per vCore minute
  • The Team plan at $99 a month includes 3 members, with additional seats at $49 a month each
  • Business Hub pricing is on request, and its tiers are capped at 4, 8 and 16 vCores with 5, 5 and 20 users

Pricing, plan by plan

Dask

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

KNIME

Free
  • Analytics PlatformFree
    • Visual workflows
    • All nodes
    • Community extensions
  • ServerFree
    • Team collaboration
    • Workflow automation
    • REST API

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 KNIME if

  • You need visual workflows.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want data preprocessing.
KNIME review

Questions people ask

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