Softwr

Machine Learning & Data Science · head to head

Dask vs Amazon Redshift ML

Dask logo

Dask

Machine Learning & Data Science

Scalable analytics in Python

From
Free
Rated
-
Full reviewWebsite
Amazon Redshift ML logo

Amazon Redshift ML

Machine Learning & Data Science

Create machine learning models using SQL

From
Free
Rated
-
Full reviewWebsite

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; 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
  • They diverge on capability: Dask covers Parallel computing, Amazon Redshift ML covers SQL-based ML.

Where they differ

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

Attributes where Dask and Amazon Redshift ML differ
AttributeDaskAmazon Redshift ML
Pricing modelopen-sourceusage-based
PlatformsLinux, Mac, WindowsWeb
Founded20152006

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 Amazon Redshift ML

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

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 Amazon Redshift ML
  • Parallelising custom Python task graphsnot Amazon Redshift ML
  • Processing larger than memory arrays and dataframes on a clusternot Amazon Redshift ML

Amazon Redshift ML

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

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

Pricing, plan by plan

Dask

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

Amazon Redshift ML

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

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.
Dask review

Choose Amazon Redshift ML if

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

Questions people ask

Is Dask or Amazon Redshift ML better?
Neither clearly leads. Dask starts at Free and Amazon Redshift ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dask or Amazon Redshift ML?
Dask starts at Free and Amazon Redshift ML at Free.
Does Dask or Amazon Redshift ML run on more platforms?
Dask runs on Linux, Mac, Windows. Amazon Redshift ML runs on 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 Amazon Redshift ML is typically brought in for.
What can Dask do that Amazon Redshift ML cannot?
Dask covers Parallel computing, Distributed DataFrames, Lazy evaluation, Dynamic task scheduling. Amazon Redshift ML covers SQL-based ML, AutoML, SageMaker integration, BYOM support.
ShareXLinkedInRedditWhatsAppEmail

Related pages

More on Dask

More on Amazon Redshift ML

Keep looking

Other head to heads