Machine Learning & Data Science · head to head
Dataiku vs Dask
Dataiku
Machine Learning & Data Science
Everyday AI, Extraordinary People
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Dataiku no pricing is published at any tier, and the plans page carries no figures at all; 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: Dataiku covers Visual data prep, Dask covers Parallel computing.
Where they differ
Only the attributes on which Dataiku and Dask 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 Dataiku
- Visual data prep
- AutoML
- MLOps
- Collaboration
- Governence
- 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.
Dataiku
- Building and deploying data science and machine learning pipelinesnot Dask
- Giving analysts and data scientists a shared visual and code environmentnot Dask
Dask
- Scaling pandas and NumPy workloads beyond a single machine's memorynot Dataiku
- Parallelising custom Python task graphsnot Dataiku
- Processing larger than memory arrays and dataframes on a clusternot Dataiku
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Dataiku
- No pricing is published at any tier, and the plans page carries no figures at all
- User, row and compute limits are not stated, so nothing about scale can be assessed before contacting sales
- Access begins with a demo request or a trial rather than a self serve signup
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
Dataiku
Free- Free EditionFree
- Single user
- Core features
- EnterpriseFree
- Full platform
- Collaboration
- MLOps
Dask
Free- Open SourceFree
- Parallel computing
- Distributed DataFrames
- ML integration
Which should you pick?
Choose Dataiku if
- You need visual data prep.
- You want to start without paying.
- You work on Linux, Mac, Windows, Web.
- You also want automl.
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 Dataiku or Dask better?
- Neither clearly leads. Dataiku 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, Dataiku or Dask?
- Dataiku starts at Free and Dask at Free.
- Does Dataiku or Dask run on more platforms?
- Dataiku runs on Linux, Mac, Windows, Web. Dask runs on Linux, Mac, Windows.
- Can I use Dataiku for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Dataiku best used for?
- Dataiku is most often used for building and deploying data science and machine learning pipelines, giving analysts and data scientists a shared visual and code environment. Of those, building and deploying data science and machine learning pipelines and giving analysts and data scientists a shared visual and code environment are not what Dask is typically brought in for.
- What can Dataiku do that Dask cannot?
- Dataiku covers Visual data prep, AutoML, MLOps, Collaboration. Dask covers Parallel computing, Distributed DataFrames, Lazy evaluation, Dynamic task scheduling. Both handle Linux support, Mac support, Windows support.