Kafka is fast, but lag is everywhere. Data falls behind, consumers can’t keep up, and alerts keep firing. The usual reaction? Blame Kafka. The real issue? Kafka does exactly what it’s built to do: decouple producers and consumers. Lag isn’t a bug, it’s a side effect. Tracking offsets won’t save you. The real problem is time lag: the gap between when data is produced and when it’s actually processed. Consumer rebalances, inefficient commits, slow APIs, and bad scaling decisions all make it worse. Little’s Law predicts when lag will spiral, but most teams ignore it. This talk breaks down what’s really happening when Kafka "falls behind", why, and what you can do about it. Batching, commit strategies, parallel consumption, dropping messages, many options are available. Start controlling lag before it controls you.