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

Databricks introduces a Solution Accelerator for real-time credit card fraud detection using two core technologies: Real-Time Mode (RTM) for Apache Spark Structured Streaming (sub-300ms latency) and Lakebase, a serverless Postgres database built into Databricks. The system ingests transactions from Kafka, applies stateful feature engineering, ML scoring via MLflow, and routes decisions (approve/flag/block) within sub-300ms. A Streamlit-based Databricks Apps dashboard provides live monitoring for fraud analysts. The accelerator eliminates the need for a separate streaming engine like Apache Flink, keeping everything — batch ETL, ML training, real-time scoring, and governance — on a single platform. Benchmarks show P50 latency under 40ms and P99 between 215–392ms. The open-source reference implementation is deployable via Databricks Asset Bundles.