Machine Learning & Data Science · head to head
KNIME vs Dask
KNIME
Machine Learning & Data Science
Open source data analytics and integration platform
- From
- Free
- Rated
- -
The short version
- Each has a real cost: KNIME the free Analytics Platform runs locally only, so anything shared or scheduled requires a paid Hub; 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
- They diverge on capability: KNIME covers Visual workflows, Dask covers Parallel computing.
Where they differ
Only the attributes on which KNIME and Dask actually diverge.
Identical on both: starting price (Free), 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 KNIME
- Visual workflows
- Data preprocessing
- Machine learning
- Visualization
- Reporting
- Python
- R
- Spark
Only in Dask
- Parallel computing
- Distributed DataFrames
- Lazy evaluation
- Dynamic task scheduling
- Dashboard
- NumPy
- Pandas
- scikit-learn
Both cover
- Linux support
- Mac support
- Windows support
What people use each for
The jobs each tool is most often brought in to do.
KNIME
- Building data pipelines and analytics workflows visually rather than in codenot Dask
- Connecting and blending data across many sources for analysisnot Dask
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
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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
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
Pricing, plan by plan
KNIME
Free- Analytics PlatformFree
- Visual workflows
- All nodes
- Community extensions
- ServerFree
- Team collaboration
- Workflow automation
- REST API
Dask
Free- Open SourceFree
- Parallel computing
- Distributed DataFrames
- ML integration
Which should you pick?
Choose KNIME if
- You need visual workflows.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want data preprocessing.
Choose Dask if
- You need parallel computing.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want distributed dataframes.
Questions people ask
- Is KNIME or Dask better?
- Neither clearly leads. KNIME starts at Free and Dask at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, KNIME or Dask?
- KNIME starts at Free and Dask at Free.
- Does KNIME or Dask run on more platforms?
- Both run on Linux, Mac, Windows, so platform support will not decide this one for you.
- Can I use KNIME for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is KNIME best used for?
- KNIME is most often used for building data pipelines and analytics workflows visually rather than in code, connecting and blending data across many sources for analysis. Of those, building data pipelines and analytics workflows visually rather than in code and connecting and blending data across many sources for analysis are not what Dask is typically brought in for.
- What can KNIME do that Dask cannot?
- KNIME covers Visual workflows, Data preprocessing, Machine learning, Visualization. Dask covers Parallel computing, Distributed DataFrames, Lazy evaluation, Dynamic task scheduling. Both handle Linux support, Mac support, Windows support.