Apache MADlib

aid: apache-madlib
name: Apache MADlib
description: Apache MADlib is an open-source library for scalable in-database analytics. It provides data-parallel implementations
  of mathematical, statistical, and machine learning methods for structured and unstructured data, executed within PostgreSQL
  or Greenplum Database. MADlib enables data scientists to run machine learning algorithms directly in the database using
  SQL.
type: Index
accessModel:
  pricing: freemium
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Freemium
  confidence: medium
  source:
  - plans
  generated: '2026-07-22'
  method: derived
position: Consumer
access: 3rd-Party
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/apache-madlib.png
tags:
- In-Database Analytics
- Machine Learning
- PostgreSQL
- SQL
- Statistics
- Deep Learning
created: '2026-03-16'
modified: '2026-04-19'
url: https://raw.githubusercontent.com/api-evangelist/apache-madlib/refs/heads/main/apis.yml
specificationVersion: '0.19'
apis:
- aid: apache-madlib:apache-madlib
  name: Apache MADlib
  description: MADlib provides SQL-callable functions for classification, regression, clustering, dimensionality reduction,
    graph analytics, time series analysis, deep learning with Keras/TensorFlow backend, and other machine learning algorithms
    running directly within PostgreSQL or Greenplum Database with GPU acceleration support.
  humanURL: https://madlib.apache.org/docs/latest/index.html
  tags:
  - Machine Learning
  - PostgreSQL
  - SQL
  - Deep Learning
  - Statistics
  properties:
  - type: Documentation
    url: https://madlib.apache.org/docs/latest/index.html
  - type: GettingStarted
    url: https://cwiki.apache.org/confluence/display/MADLIB/Installation+Guide
  - type: GitHubRepository
    url: https://github.com/apache/madlib
common:
- type: VulnerabilityDisclosure
  url: security/apache-madlib-vulnerability-disclosure.yml
- type: DomainSecurity
  url: security/apache-madlib-domain-security.yml
- type: Portal
  url: https://madlib.apache.org/
- type: GitHubOrganization
  url: https://github.com/apache
- type: GitHubRepository
  url: https://github.com/apache/madlib
- type: Wiki
  url: https://cwiki.apache.org/confluence/display/MADLIB/
- type: IssueTracker
  url: https://issues.apache.org/jira/browse/MADLIB
- type: TermsOfService
  url: https://www.apache.org/licenses/LICENSE-2.0
- type: Features
  data:
  - name: In-Database Machine Learning
    description: Run machine learning algorithms directly within PostgreSQL or Greenplum Database using SQL, eliminating data
      movement overhead.
  - name: Classification and Regression
    description: Support for logistic regression, linear regression, naive Bayes, decision trees, random forests, support
      vector machines, and more.
  - name: Clustering Algorithms
    description: K-Means, DBSCAN, and other clustering algorithms for unsupervised learning within the database.
  - name: Deep Learning with Keras/TensorFlow
    description: Train and serve deep learning models using Keras and TensorFlow backends with GPU acceleration support.
  - name: Graph Analytics
    description: Built-in graph algorithms for network analysis, path finding, and community detection on graph data stored
      in the database.
  - name: Time Series Analysis
    description: ARIMA, SARIMA, and other time series forecasting models running in-database.
  - name: Dimensionality Reduction
    description: PCA and SVD implementations for dimensionality reduction and feature extraction.
  - name: Model Selection and Hyperparameter Tuning
    description: Cross-validation and hyperparameter optimization frameworks for model selection.
  - name: Association Rules
    description: FP-Growth and Apriori algorithms for market basket analysis and association rule mining.
- type: UseCases
  data:
  - name: Predictive Analytics
    description: Build predictive models for churn prediction, fraud detection, and demand forecasting directly on database
      data.
  - name: Recommendation Systems
    description: Implement collaborative filtering and content-based recommendation algorithms using in-database machine learning.
  - name: Customer Segmentation
    description: Cluster customers using K-Means and other algorithms to identify segments for targeted marketing.
  - name: Anomaly Detection
    description: Detect anomalies in time series and transactional data using statistical models running in-database.
  - name: Network Analysis
    description: Analyze social networks, supply chains, and communication graphs using built-in graph algorithms.
- type: Integrations
  data:
  - name: PostgreSQL
    description: Primary execution environment supporting PostgreSQL versions 11 through 15.
  - name: Greenplum Database
    description: Native support for Greenplum Database GP6 and GP7 for massively parallel processing.
  - name: TensorFlow
    description: Deep learning backend integration for training neural networks within the database.
  - name: Keras
    description: High-level deep learning API integration for building and training models with GPU acceleration.
  - name: XGBoost
    description: Gradient boosting framework integration for high-performance tree-based models.
maintainers:
- FN: Kin Lane
  email: info@apievangelist.com