Confluent & Syncsort: An Architecture for Streaming and At-Rest Data

What this post added

This post details the architectural pattern of integrating streaming data (via Kafka and Confluent Platform) with at-rest data (from traditional databases and file systems) to provide context for real-time event processing. It uses a fraud detection use case as a concrete example, illustrating how ATM transactions streamed through Kafka can be enriched with static customer data from at-rest stores to identify fraudulent activity. It also mentions other industry use cases like hotel inventory management and healthcare data analysis, emphasizing the versatility of this combined architecture.