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DVC vs Apache Spark MLlib

DVC logo

DVC

Software

Data version control for machine learning projects

From
Free
Rated
-
Full reviewWebsite
Apache Spark MLlib logo

Apache Spark MLlib

Software

Scalable machine learning on Apache Spark

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: DVC dVC is Apache 2.0 licensed open source with no enterprise tier or paid support offering documented in the project itself; teams needing SLA-backed support get nothing from the DVC project directly.; Apache Spark MLlib apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.
  • They diverge on capability: DVC covers Data versioning, Apache Spark MLlib covers Classification.

Where they differ

Only the attributes on which DVC and Apache Spark MLlib actually diverge.

Attributes where DVC and Apache Spark MLlib differ
AttributeDVCApache Spark MLlib
PlatformsLinux, Mac, WindowsLinux, macOS, Windows
Founded20181999

Identical on both: starting price (Free), pricing model (open-source), free tier (Yes), user rating (Not yet rated), category (Unknown).

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 DVC

  • Data versioning
  • Pipeline management
  • Experiment tracking
  • Remote storage
  • Git integration
  • Git
  • S3
  • Azure Blob

Only in Apache Spark MLlib

  • Classification
  • Regression
  • Clustering
  • Collaborative filtering
  • Feature engineering
  • Apache Spark
  • Hadoop
  • Kafka

Both cover

  • Linux support
  • Mac support
  • Windows support

What people use each for

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

DVC

  • Machine learningnot Apache Spark MLlib
  • Data analysisnot Apache Spark MLlib
  • Model trainingnot Apache Spark MLlib
  • Predictive analyticsnot Apache Spark MLlib

Apache Spark MLlib

  • Large-scale distributed machine learning on Spark clustersnot DVC
  • Classification and regression with decision trees, random forests, gradient-boosted treesnot DVC
  • Clustering with K-means and Gaussian Mixture Modelsnot DVC

Where each one falls short

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

DVC

  • DVC is Apache 2.0 licensed open source with no enterprise tier or paid support offering documented in the project itself; teams needing SLA-backed support get nothing from the DVC project directly.

Apache Spark MLlib

  • Apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.

Pricing, plan by plan

DVC

Free
  • Open SourceFree
    • Data versioning
    • Pipeline management
    • Experiment tracking
  • DVC StudioFree
    • Web UI
    • Team collaboration
    • Visualizations

Apache Spark MLlib

Free

No published plan breakdown. See the Apache Spark MLlib review.

Which should you pick?

Choose DVC if

  • You need data versioning.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want pipeline management.
DVC review

Choose Apache Spark MLlib if

  • You need classification.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want regression.
Apache Spark MLlib review

Questions people ask

Is DVC or Apache Spark MLlib better?
Neither clearly leads. DVC starts at Free and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, DVC or Apache Spark MLlib?
DVC starts at Free and Apache Spark MLlib at Free.
Does DVC or Apache Spark MLlib run on more platforms?
DVC runs on Linux, Mac, Windows. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use DVC for free?
Both have a free tier, so you can try either at no cost before committing.
What is DVC best used for?
DVC is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Apache Spark MLlib is typically brought in for.
What can DVC do that Apache Spark MLlib cannot?
DVC covers Data versioning, Pipeline management, Experiment tracking, Remote storage. Apache Spark MLlib covers Classification, Regression, Clustering, Collaborative filtering. Both handle Linux support, Mac support, Windows support.
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