Kafka

In traditional monolithic applications, maintaining data consistency across multiple entities is straightforward. Relational database engines provide ACID (Atomicity, Consistency, Isolation, Durability) guarantees wrapped inside local SQL transactions. If an order placement, payment deduction, or inventory reserve fails halfway through, calling ROLLBACK reverts every database modification instantaneously. However, when migrating to a modern Microservices Architecture, data management shifts fundamentally. To ensure domain autonomy and independent scalability, each microservice owns its private database. A single business operation—such as processing an e-commerce checkout—now spans multiple service boundaries and database engines (e.g., PostgreSQL for Orders, DynamoDB for Payments, Redis for Inventory).

In modern distributed software engineering, Event-Driven Architecture (EDA) has become the backbone of scalable microservice systems. Services regularly emit domain events—such as OrderCreated, PaymentProcessed, or UserRegistered—to communicate state changes across service boundaries without tight coupling. However, implementing reliable event publishing in a microservices environment introduces a subtle yet catastrophic engineering problem: How do you guarantee that a database update and its corresponding event publication both succeed or both fail together?