Machine Learning & Data Science · head to head
Dask vs Minitab
Minitab
Machine Learning & Data Science
Statistical software for quality improvement
- From
- Free
- Rated
- -
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; Minitab pricing is by quote only: the pricing page is an inquiry form and publishes no rate, no seat price and no minimum
- They diverge on capability: Dask covers Parallel computing, Minitab covers Statistical analysis.
Where they differ
Only the attributes on which Dask and Minitab actually diverge.
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 Minitab
- Statistical analysis
- Quality tools
- Regression analysis
- Control charts
- Design of experiments
- Excel
- Python
- R
Both cover
- 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 Minitab
- Parallelising custom Python task graphsnot Minitab
- Processing larger than memory arrays and dataframes on a clusternot Minitab
Minitab
- Statistical analysis and hypothesis testing for quality engineeringnot Dask
- Six Sigma and process improvement studies with control chartsnot Dask
- Design of experiments and capability 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
Minitab
- Pricing is by quote only: the pricing page is an inquiry form and publishes no rate, no seat price and no minimum
- Obtaining a price requires submitting contact details and waiting for a sales representative
Pricing, plan by plan
Dask
Free- Open SourceFree
- Parallel computing
- Distributed DataFrames
- ML integration
Minitab
Free- TrialFree
- 7-day trial
- Full features
- Single User$29/month
- Full Minitab
- All features
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.
Choose Minitab if
- You need statistical analysis.
- You want to start without paying.
- You work on Mac, Windows, Web.
- You also want quality tools.
Questions people ask
- Is Dask or Minitab better?
- Neither clearly leads. Dask starts at Free and Minitab at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Dask or Minitab?
- Dask starts at Free and Minitab at Free.
- Does Dask or Minitab run on more platforms?
- Dask runs on Linux, Mac, Windows. Minitab runs on 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 Minitab is typically brought in for.
- What can Dask do that Minitab cannot?
- Dask covers Parallel computing, Distributed DataFrames, Lazy evaluation, Dynamic task scheduling. Minitab covers Statistical analysis, Quality tools, Regression analysis, Control charts. Both handle Mac support, Windows support.
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