MDUAL - Real-Time Outlier Detection in Data Streams
A real-time anomaly detection system leveraging Apache Kafka and Java to process high-velocity data streams. The solution applies ML-driven outlier detection to identify irregularities in continuous data streams efficiently.
Release date
Feb 5, 2025
Location
Chicago
Client
Framer
Category
Development
01. The Challenge
Traditional anomaly detection methods were inefficient in processing large-scale, high-velocity data streams, causing delays and inaccuracies in detecting irregularities.
02. The Solution
A real-time anomaly detection system was built using Java, Apache Kafka, and Object-Oriented Programming (OOP). The system leveraged the "Multiple Dynamic Outlier-Detection from a Data Stream" framework, ensuring high-speed processing of large datasets.
03. The Result
The system efficiently processed 1TB+ of data daily, handling 8,000 messages per second, and improved anomaly detection accuracy through optimized data streaming and predictive analytics.