⚖️ Consistency Models — Making Sense of Data

Consistency models define how up-to-date and synchronized data is across distributed systems.

💪 Strong Consistency

• Guarantee: Every read gets the latest write.
• Use When: Accuracy is critical (e.g., banking).
• Example: Relational databases with ACID transactions.

🕰️ Eventual Consistency

• Guarantee: All nodes will eventually have the same data.
• Use When: High availability is more important than instant accuracy (e.g., social feeds).
• Example: DNS, Amazon DynamoDB.

🔗 Causal Consistency

• Guarantee: Related operations are seen in the correct order.
• Use When: Order of operations matters, but not all need to be strictly up-to-date.
• Example: Collaborative editing tools, chat applications.

🧪 Other Models

• Read-Your-Writes Consistency: After you write, you always see your own updates.
• Monotonic Reads: Once you see a value, you never see an older value.

🧠 Final Thoughts

Pick the model that matches your system’s needs—there’s always a trade-off! Understand your application's requirements before choosing a consistency model.