Apache Eagle: eBay’s Open‑Source Real‑Time Hadoop Data Security Platform

In the era of data‑driven business, eBay processes petabytes of data daily using Hadoop, requiring robust security and real‑time monitoring solutions.

Distributed systems have become essential for large‑scale internet services, and Hadoop is a core component of eBay’s data infrastructure.

Apache Eagle is eBay’s open‑source, distributed, real‑time security monitoring platform for Hadoop, designed to address the lack of existing solutions for massive, real‑time data‑behavior monitoring.

The platform provides access control, isolation, data classification, encryption, and real‑time behavior monitoring, and has been donated to the Apache Software Foundation.

Key challenges include monitoring user behavior, cluster metrics, and job logs across thousands of nodes with sub‑second alerting requirements.

Eagle’s architecture consists of a distributed streaming policy engine, a metadata‑driven data layer, scalable storage, and integration with machine‑learning models to build user profiles for anomaly detection.

Core components include a high‑throughput data collection layer (e.g., Kafka), a stream processing layer (default Apache Storm), a flexible rule engine supporting stateful and stateless policies, and a storage layer optimized for large‑scale queries.

Typical scenarios cover monitoring Hadoop data access traffic, detecting illegal intrusions, preventing sensitive data loss, and providing policy‑based real‑time alerts.

Key features are ultra‑low latency alerting, horizontal scalability, ease of use via a sandbox UI, built‑in user profiling with machine‑learning algorithms (kernel density estimation and feature‑value decomposition), and full open‑source licensing.

The alerting framework comprises a stream metadata API, a policy engine service API (e.g., WSO2 Siddhi CEP), and a partitioner API to handle high‑volume event streams.

Machine‑learning algorithms employed include kernel density estimation for probability modeling and feature‑value decomposition for dimensionality reduction and noise filtering.

Service components provide a strategy manager UI and REST API, single‑event and window‑based policies, and a SQL‑like query service for large‑scale data analysis.

Future work aims to extend machine‑learning support to Hive and HBase, integrate with external monitoring tools (e.g., Ganglia, Nagios), and open additional Hadoop monitoring modules such as HBase and job performance metrics.