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It provides data-parallel implementations of mathematical, statistical and machine learning methods for structured and unstructured data.

Quick Start Guides

Get going with a minimum of fuss. 

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• Architecture
• Module Anatomy

Release Notes

Historical release notes for releases prior to move to ASF. 

Third Party Components

MADlib incorporates material from the following third-party components:

1. argparse 1.2.1 "provides an easy, declarative interface for creating command line tools"
2. Boost 1.47.0 (or newer) "provides peer-reviewed portable C++ source libraries"
3. doxypy 0.4.2 "is an input filter for Doxygen"
4. Eigen 3.2.2 "is a C++ template library for linear algebra"
5. PyYAML 3.10 "is a YAML parser and emitter for Python"
6. PyXB 1.2.4 "is a Python library for XML Schema Bindings"

 Licensing

License information regarding MADlib and included third-party libraries can be found inside the license directory.

 Release Notes

Historical release notes for releases prior to move to ASF. 

Papers

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• MAD Skills : New Analysis Practices for Big Data (VLDB 2009)
• Hybrid In-Database Inference for Declarative Information Extraction (SIGMOD 2011)
• Towards a Unified Architecture for In-Database Analytics (SIGMOD 2012)
• The MADlib Analytics Library or MAD Skills, the SQL (VLDB 2012)

 Related Software

• PivotalR - lets the user run the functions of the open-source big-data machine learning package MADlib directly from R.

• PyMADlib  - a nascent Python wrapper for MADlib, which brings you the power and flexibility of python with the number crunching power of MADlib.

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