Apache MADlib
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.
APIs
Apache MADlib
MADlib provides SQL-callable functions for classification, regression, clustering, dimensionality reduction, graph analytics, time series analysis, deep learning with Keras/Tens...
Features
Run machine learning algorithms directly within PostgreSQL or Greenplum Database using SQL, eliminating data movement overhead.
Support for logistic regression, linear regression, naive Bayes, decision trees, random forests, support vector machines, and more.
K-Means, DBSCAN, and other clustering algorithms for unsupervised learning within the database.
Train and serve deep learning models using Keras and TensorFlow backends with GPU acceleration support.
Built-in graph algorithms for network analysis, path finding, and community detection on graph data stored in the database.
ARIMA, SARIMA, and other time series forecasting models running in-database.
PCA and SVD implementations for dimensionality reduction and feature extraction.
Cross-validation and hyperparameter optimization frameworks for model selection.
FP-Growth and Apriori algorithms for market basket analysis and association rule mining.
Use Cases
Build predictive models for churn prediction, fraud detection, and demand forecasting directly on database data.
Implement collaborative filtering and content-based recommendation algorithms using in-database machine learning.
Cluster customers using K-Means and other algorithms to identify segments for targeted marketing.
Detect anomalies in time series and transactional data using statistical models running in-database.
Analyze social networks, supply chains, and communication graphs using built-in graph algorithms.
Integrations
Primary execution environment supporting PostgreSQL versions 11 through 15.
Native support for Greenplum Database GP6 and GP7 for massively parallel processing.
Deep learning backend integration for training neural networks within the database.
High-level deep learning API integration for building and training models with GPU acceleration.
Gradient boosting framework integration for high-performance tree-based models.