How to Build Real-Time Fraud Detection using Spark Real-Time Mode and Lakebase

This article demonstrates how to build a complete real-time fraud detection system using Spark Real-Time Mode and Lakebase, eliminating the need for separate streaming infrastructure.

• •RTM enables sub-second processing with up to 92% faster performance than Apache Flink for stateless transformations and enrichment workloads.
• •Lakebase serves as a managed PostgreSQL database for sub-millisecond feature serving without external infrastructure.
• •The solution processes card transactions through five stages: parsing, velocity tracking, enrichment, scoring, and routing.
• •transformWithState operator maintains per-card stateful tracking with automatic TTL-based memory cleanup to prevent unbounded memory growth.
• •End-to-end latency testing achieved P50 latency under 40ms and P99 latency between 215-392ms across varying transaction rates.