Why Event sourcing pattern in Kafka? - Purpose & Use Cases

The Big Idea

What if you could rewind and replay every change in your system like a video?

The Scenario

Imagine you run a busy store and keep track of every sale by writing it down on paper. When you want to know your total sales or fix a mistake, you have to dig through piles of notes, which takes forever and can cause errors.

The Problem

Manually tracking changes or states is slow and risky. Mistakes happen easily, and fixing them means rewriting history or guessing what happened. It's hard to see how things changed over time or recover lost data.

The Solution

Event sourcing records every change as an event in order. Instead of just storing the final result, it keeps a full history. This way, you can rebuild the current state anytime, audit changes, and fix errors by replaying events.

Before vs After

✗ Before

updateAccountBalance(newBalance)
saveToDatabase(newBalance)

✓ After

appendEvent({type: 'BalanceUpdated', amount: 100})
replayEventsToGetCurrentBalance()

What It Enables

It lets you track every change clearly, recover from mistakes easily, and understand exactly how your data evolved over time.

Real Life Example

In a banking system, event sourcing helps record every deposit and withdrawal as an event. If there's a dispute or error, the bank can replay all transactions to see the exact account history.

Key Takeaways

Manual tracking is slow and error-prone.

Event sourcing stores every change as an event.

This makes data reliable, auditable, and easy to fix.