Building a Real-Time Fraud Detection Pipeline with Kinesis and SageMaker

Quick Answer

The design challenge in this scenario is that invoking a SageMaker model for every transaction is inherently an extra processing step layered onto a streaming pipeline, and the architecture needs to handle that enrichment without blocking or losing the continuous flow of ingested data. Kinesis Data Analytics for Apache Flink is well suited to this because Flink is a stream-processing engine capable of making external calls, such as invoking a SageMaker endpoint per record, as part of its processing logic, enriching each transaction record with the model's fraud score while still operating within a continuous streaming application rather than a batch job. Once records have been enriched with predictions, routing them to Kinesis Data Firehose for delivery to S3 hands off the final storage step to a service purpose-built for reliable, buffered delivery, Firehose batches and compresses records before writing them, and provides automatic retry behavior, which is a more robust way to land data in S3 than writing directly from the stream-processing layer itself. This separation of concerns, Flink for real-time enrichment logic, Firehose for durable delivery, lets each service do what it's built for, rather than asking a single component to handle both model invocation and reliable S3 writes. Whenever a scenario needs a streaming pipeline to enrich each record with a model prediction and then durably store the results in S3, expect the correct architecture to pair a stream-processing engine capable of calling external services with Firehose for the final storage step, rather than combining both responsibilities into one component.