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Machine Learning & Data Science · head to head

Amazon Redshift ML vs Dask

Amazon Redshift ML logo

Amazon Redshift ML

Machine Learning & Data Science

Create machine learning models using SQL

From
Free
Rated
-
Full reviewWebsite
Dask logo

Dask

Machine Learning & Data Science

Scalable analytics in Python

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Amazon Redshift ML free tier covers only two CREATE MODEL requests per month for two months, capped at 100,000 cells per request; beyond that training is metered at $20 per million cells for the first 10 million, dropping in tiers to $7 per million cells over 100 million; 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: Amazon Redshift ML covers SQL-based ML, Dask covers Parallel computing.

Where they differ

Only the attributes on which Amazon Redshift ML and Dask actually diverge.

Attributes where Amazon Redshift ML and Dask differ
AttributeAmazon Redshift MLDask
Pricing modelusage-basedopen-source
PlatformsWebLinux, Mac, Windows
Founded20062015

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 Amazon Redshift ML

  • SQL-based ML
  • AutoML
  • SageMaker integration
  • BYOM support
  • In-database predictions
  • Amazon Redshift
  • SageMaker
  • S3

Only in Dask

  • Parallel computing
  • Distributed DataFrames
  • Lazy evaluation
  • Dynamic task scheduling
  • Dashboard
  • NumPy
  • Pandas
  • scikit-learn

What people use each for

The jobs each tool is most often brought in to do.

Amazon Redshift ML

  • Training and running machine learning models directly from SQL inside Amazon Redshiftnot Dask

Dask

  • Scaling pandas and NumPy workloads beyond a single machine's memorynot Amazon Redshift ML
  • Parallelising custom Python task graphsnot Amazon Redshift ML
  • Processing larger than memory arrays and dataframes on a clusternot Amazon Redshift ML

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Amazon Redshift ML

  • Free tier covers only two CREATE MODEL requests per month for two months, capped at 100,000 cells per request; beyond that training is metered at $20 per million cells for the first 10 million, dropping in tiers to $7 per million cells over 100 million

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

Amazon Redshift ML

Free
  • Free TrialFree
    • 2-month trial
    • 750 DC2.Large hours
  • On-Demand$0.25/hour
    • Per-node pricing
    • SageMaker training

Dask

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

Which should you pick?

Choose Amazon Redshift ML if

  • You need sql-based ml.
  • You want to start without paying.
  • You also want automl.
Amazon Redshift ML review

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

Questions people ask

Is Amazon Redshift ML or Dask better?
Neither clearly leads. Amazon Redshift ML 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, Amazon Redshift ML or Dask?
Amazon Redshift ML starts at Free and Dask at Free.
Does Amazon Redshift ML or Dask run on more platforms?
Amazon Redshift ML runs on Web. Dask runs on Linux, Mac, Windows.
Can I use Amazon Redshift ML for free?
Both have a free tier, so you can try either at no cost before committing.
What is Amazon Redshift ML best used for?
Amazon Redshift ML is most often used for training and running machine learning models directly from sql inside amazon redshift. Of those, training and running machine learning models directly from sql inside amazon redshift is not what Dask is typically brought in for.
What can Amazon Redshift ML do that Dask cannot?
Amazon Redshift ML covers SQL-based ML, AutoML, SageMaker integration, BYOM support. Dask covers Parallel computing, Distributed DataFrames, Lazy evaluation, Dynamic task scheduling.
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