Machine Learning & Data Science · head to head
Dask vs Alteryx
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; Alteryx starter is $250 per user per month billed annually, and the Professional and Enterprise editions are quote-only
- They diverge on capability: Dask covers Parallel computing, Alteryx covers Data preparation.
Where they differ
Only the attributes on which Dask and Alteryx actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
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 Alteryx
- Data preparation
- Data blending
- Predictive analytics
- Spatial analytics
- Reporting
- Python
- R
- Snowflake
Both cover
- 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 Alteryx
- Parallelising custom Python task graphsnot Alteryx
- Processing larger than memory arrays and dataframes on a clusternot Alteryx
Alteryx
- Data preparation and building AI-ready datasetsnot Dask
- Predictive analytics without writing codenot Dask
- Automating and orchestrating repeatable analytics workflowsnot Dask
- Enterprise reporting with governed, reusable logicnot Dask
- Connecting to Snowflake, Databricks and cloud warehouses alongside on-premises systemsnot 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
Alteryx
- Starter is $250 per user per month billed annually, and the Professional and Enterprise editions are quote-only
- Automation runs are metered, with 50 included on Starter and 15,000 on Professional, and more must be bought
- Cost depends on three separate dimensions at once: edition, user role and automation capacity
- Advanced analytics, governance and orchestration are withheld from the entry edition
Pricing, plan by plan
Dask
Free- Open SourceFree
- Parallel computing
- Distributed DataFrames
- ML integration
Alteryx
Free- TrialFree
- 14-day trial
- Full features
- Designer Desktop$5195/year
- Data prep
- Blending
- Analytics
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 Alteryx if
- You need data preparation.
- You want to start without paying.
- You work on Windows, Web.
- You also want data blending.
Questions people ask
- Is Dask or Alteryx better?
- Neither clearly leads. Dask starts at Free and Alteryx at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Dask or Alteryx?
- Dask starts at Free and Alteryx at Free.
- Does Dask or Alteryx run on more platforms?
- Dask runs on Linux, Mac, Windows. Alteryx runs on 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 Alteryx is typically brought in for.
- What can Dask do that Alteryx cannot?
- Dask covers Parallel computing, Distributed DataFrames, Lazy evaluation, Dynamic task scheduling. Alteryx covers Data preparation, Data blending, Predictive analytics, Spatial analytics. Both handle Windows support.