Streaming Fraud Detection Pipeline
End-to-end streaming pipeline built for real-time transaction scoring at scale. From Kafka ingestion through in-stream ML inference to production monitoring — every layer designed for sub-100ms decisioning and zero-downtime deployments.
<100ms
Scoring Latency
p99 end-to-end
10K+
Throughput
events / sec
99.9%
Uptime
SLA-grade
Fixed-layout pipeline topology — data flows left to right
Real-time fraud decisioning that reduces false positives, protects revenue, and scales horizontally — built on battle-tested open-source infrastructure.
Medallion Layer Throughput
Bronze
Silver
Gold
Ingestion
Feature Engineering
ML Inference
Storage
Serving & Monitoring
Real-Time Stream Processing
Kafka topics receive raw transaction events. PySpark Structured Streaming validates schemas, deduplicates, and computes windowed aggregations with exactly-once semantics.
In-Stream ML Inference
Feature vectors feed XGBoost models versioned in MLflow. Redis caches hot features for sub-100ms scoring. Champion/challenger routing compares model versions live.
Decisioning and Alerting
Scored transactions route through rule gates. High-risk events trigger alerts, block actions, and write to Delta Lake for audit. Grafana dashboards surface drift and latency in real time.
This pipeline demonstrates production-grade streaming architecture — from ingestion through ML inference to real-time monitoring. Every component runs on self-hosted infrastructure with zero vendor lock-in.