Kafka Streams 101: A Developer’s Guide to Real-Time Application Logic

<h2> TL;DR </h2> <p>Apache Kafka is a high-throughput, fault-tolerant distributed event streaming platform ideal for real-time data processing. It organizes data into replicated

<p>Wait, the rule says no H1. Let me write this properly.</p> <h2> The Problem Nobody Talks About When They Pitch Kafka Streams </h2> <p>Kafka Streams is a genuinely elegant way

<p>A typical Kafka console reads the cluster: topics, partitions, consumer groups, committed offsets, lag. That is the right picture for most of what a console is for.</p> <p>A

<p>Every data pipeline makes one foundational decision before a single line of code is written.</p> <p>Does it process data in scheduled chunks, or does it process data as events

<blockquote> <p><em>I hadn’t planned a two-part series, but as I write it’s become clear the topic can’t be contained in a single article.</em></p> </blockquote> <p>Modern backend