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

Summary

Card fraud operates in seconds, while organizations need to score and block suspicious transactions before settlement without adding a separate streaming stack. This Solution Accelerator presents an open-source, end-to-end implementation on Databricks, covering transaction ingestion, Spark Real-Time Mode processing, fraud scoring, Lakebase feature serving, and a Databricks Apps monitoring dashboard. RTM processes streaming workloads in milliseconds and targets sub-300ms latency, while Lakebase supplies a managed, serverless Postgres serving layer with sub-millisecond reads. The design keeps batch ETL, training, streaming, and governance within the same platform. Deployment progresses from a synthetic rate-source quick start to Kafka-based parsing, stateful per-card velocity tracking with TTL, enrichment, weighted scoring, routing, and optional MLflow-trained RandomForest scoring, followed by a Streamlit app for live decisions.

Takeaways

1. 1

   Real-Time Mode runs inside Spark Structured Streaming and is described as up to 92% faster than Apache Flink across stateless transformation, join-based enrichment, and aggregation workloads.

2. 2

   The pipeline uses transformWithState to maintain per-card transaction state, including 60-second velocity counts, with TTL-based expiration to prevent unbounded memory growth.

3. 3

   Lakebase receives continuously streamed per-card features with upsert semantics and provides sub-millisecond reads for real-time feature serving.