Software · head to head
Apache Spark MLlib vs Weka
The short version
- Each has a real cost: 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.; Weka the package management system needs an internet connection to download and install packages, so an air-gapped install gets only the core distribution
- They diverge on capability: Apache Spark MLlib covers Collaborative filtering, Weka covers Association rules.
Where they differ
Only the attributes on which Apache Spark MLlib and Weka actually diverge.
| Attribute | Apache Spark MLlib | Weka |
|---|---|---|
| Platforms | Linux, macOS, Windows | Linux, Mac, Windows |
| Founded | 1999 | 1993 |
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 Apache Spark MLlib
- Collaborative filtering
- Feature engineering
- Apache Spark
- Hadoop
- Kafka
- Databricks
- AWS EMR
Only in Weka
- Association rules
- Feature selection
- Java
- R
- Python
Both cover
- Classification
- Regression
- Clustering
- Linux support
- Mac support
- Windows support
What people use each for
The jobs each tool is most often brought in to do.
Apache Spark MLlib
- Large-scale distributed machine learning on Spark clustersnot Weka
- Classification and regression with decision trees, random forests, gradient-boosted treesnot Weka
- Clustering with K-means and Gaussian Mixture Modelsnot Weka
Weka
- Teaching and exploring classic machine learning algorithms through a GUInot Apache Spark MLlib
- Running data mining experiments and preprocessing without writing codenot Apache Spark MLlib
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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.
Weka
- The package management system needs an internet connection to download and install packages, so an air-gapped install gets only the core distribution
- Weka is split into a stable 3.8 branch that receives only bug fixes and compatibility-safe upgrades and a 3.9 development branch that may receive features that break compatibility
- Weka requires a 64-bit Java VM; the bundled installers ship Bellsoft OpenJDK 25 per platform and architecture
Pricing, plan by plan
Apache Spark MLlib
FreeNo published plan breakdown. See the Apache Spark MLlib review.
Weka
Free- Open SourceFree
- All ML algorithms
- GUI and CLI
- Java API
Which should you pick?
Choose Apache Spark MLlib if
- You need collaborative filtering.
- You want to start without paying.
- You work on Linux, macOS, Windows.
- You also want feature engineering.
Choose Weka if
- You need association rules.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want feature selection.
Questions people ask
- Is Apache Spark MLlib or Weka better?
- Neither clearly leads. Apache Spark MLlib starts at Free and Weka at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Apache Spark MLlib or Weka?
- Apache Spark MLlib starts at Free and Weka at Free.
- Does Apache Spark MLlib or Weka run on more platforms?
- Apache Spark MLlib runs on Linux, macOS, Windows. Weka runs on Linux, Mac, Windows.
- Can I use Apache Spark MLlib for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Apache Spark MLlib best used for?
- Apache Spark MLlib is most often used for large-scale distributed machine learning on spark clusters, classification and regression with decision trees, random forests, gradient-boosted trees, clustering with k-means and gaussian mixture models. Of those, large-scale distributed machine learning on spark clusters and classification and regression with decision trees, random forests, gradient-boosted trees are not what Weka is typically brought in for.
- What can Apache Spark MLlib do that Weka cannot?
- Apache Spark MLlib covers Collaborative filtering, Feature engineering, Apache Spark, Hadoop. Weka covers Association rules, Feature selection, Java, R. Both handle Classification, Regression, Clustering, Linux support.
Related pages
More on Apache Spark MLlib
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