Data Consistency Patterns in Distributed Systems and .NET Core

Short summary

Article compares four distributed data consistency patterns—Transactional Outbox, CDC, Event Sourcing, and Saga Pattern—with trade-offs in complexity, consistency, and performance. Provides production-ready C# code examples and Azure-specific implementation guidance, including best practices for monitoring, idempotency, retry logic, and failure recovery.

• •Transactional Outbox ensures atomicity by storing events in the same database transaction as business data
• •CDC (Change Data Capture) offers strong consistency with excellent performance for existing systems
• •Event Sourcing and Saga patterns support complex workflows with eventual consistency trade-offs
• •Azure implementation uses SQL Database, Service Bus, Functions, and Durable Functions

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