MADlib: Big Data Machine Learning in SQL for Data Scientists

  • Open Source, commercially friendly Apache License
  • Supports Postgres, Pivotal Greenplum Database, and Apache HAWQ (incubating)
  • Powerful analytics for Big Data

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MADlib 1.9 Release (GA)

On April 6, 2016, MADlib completed its second release as an Apache Software Foundation incubator project: general availability of MADlib 1.9.

New features include: path functions, support vector machines including non-linear kernels, matrix operations (phase 2), covariance matrix, proportion of variance for PCA, stemmer function, and support for Apache HAWQ 2.0 (incubating).

You are invited to  download the 1.9 release and review the release notes. This is a source code only release.

 

MADlib 1.9 alpha Release

On March 11, 2016, MADlib completed its first release as an Apache Software Foundation incubator project.

The purpose of the release was to clear all potential IP issues in the code base and make it legally ready to be adopted by the community. In addition, we want to share the new features that have been developed, in order to give the community a good sense of the upcoming 1.9 release.

You are invited to  download the 1.9 alpha release and review the release notes. This is a source code only release.

The MADlib 1.9 release will be coming out shortly, based closely on the 1.9 alpha with a few "last mile" updates and additions.

 

MADlib Moves to ASF

On Sept. 15, 2015, MADlib became an Apache Software Foundation incubator project.

Together with Apache HAWQ (incubating), the MADlib open source project has transitioned its development and governance models to be in accordance with  ”The Apache Way.”

Apache Software Foundation is a widely recognized place for like-minded developers to collaborate on software in open and productive ways. MADlib community views it as the ideal venue to continue developing MADlib technology in innovative directions.  Please refer to the ASF incubator proposal for more details.

We invite anyone to come collaborate on the codebase.  Both software contributions and non-code contributions (documentation, events, community management, etc.) are valued.

We enthusiastically look forward to working together with all future contributors to MADlib in order to advance the state-of-the-art of scale-out data science tools.