Stateless vs. Stateful Stream Processing with Kafka Streams and Apache Flink

In the world of data-driven applications, the rise of stream processing has changed how we handle and act on data. While traditional databases, data lakes, and warehouses are effective for many batch-based use cases, they fall short in scenarios demanding low latency, scalability, and real-time decision-making. This post explores the key concepts of stateless and stateful stream processing, using Kafka Streams and Apache Flink as examples. These principles apply to any stream processing engine, whether open-source or a cloud service. Let’s break down the differences, practical use cases, the relation to AI/ML, and the immense value stream processing offers compared to traditional data-at-rest methods.

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Rethinking Data Processing: From Static to Dynamic

In traditional systems, data is typically stored first in a database or data lake and queried later for computation. This works well for batch processing tasks, like generating reports or dashboards. The…