streamDM
A Spark Streaming library for mining big data streams with incremental learning algorithms.
What is streamDM?
streamDM is a library for mining big data streams using Spark Streaming. It provides implementations of stream learning algorithms that can handle data where distributions may change over time and examples must be processed efficiently with minimal memory footprint. The library addresses the unique challenges of stream mining compared to traditional batch learning.
Data scientists and engineers working with real-time data streams who need to perform machine learning tasks like classification, clustering, and regression on continuously arriving data.
streamDM offers a collection of theoretically sound stream mining algorithms integrated with Spark Streaming, providing scalability and efficiency for big data stream processing. It fills a gap in the Spark ecosystem by focusing specifically on incremental learning methods suitable for dynamic data streams.
Overview
Stream Data Mining Library for Spark Streaming
Use Cases
Best For
- Real-time fraud detection on financial transaction streams
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