Write-Through Caching with Azure Managed Redis and Azure SQL Database

I authored a Microsoft Learn reference architecture for implementing write-through caching with Azure Managed Redis and Azure SQL Database.

The architecture addresses a common problem in high-traffic applications: SQL Database needs to remain the authoritative system of record, but repeated reads can increase latency and database load as traffic grows. Azure Managed Redis provides the low-latency distributed cache layer, while Azure Functions coordinates the write-through path so application-controlled writes update SQL first, then update the corresponding Redis cache entry before the operation is treated as complete.

The design uses Azure App Service for the application tier, Azure Functions for the write-through and repair logic, Azure SQL Database as the system of record, Azure Managed Redis as the distributed cache, Azure Private Link for private connectivity, and Azure Monitor for observability across the full request path.

• Designing a caching pattern that improves read latency without pretending Redis is the authoritative data store.

• Preserving read-after-write behavior for application-controlled writes by coordinating SQL commits and Redis cache updates through a single write-through function.

• Handling partial failure cases where SQL commits successfully but Redis cannot be updated immediately.

• Using a durable SQL outbox table and repair function so failed cache updates can be retried idempotently after the authoritative write succeeds.

• Defining clear guidance for when write-through caching is appropriate versus simpler patterns like cache-aside or asynchronous refresh.

• Mapping the architecture to Azure Well-Architected pillars, including reliability, security, cost optimization, operational excellence, and performance efficiency.

• Published a production-minded Azure Architecture Center pattern for write-through caching with Azure Managed Redis and Azure SQL Database.

• Provided implementation guidance for teams that need low-latency reads while keeping SQL Database as the durable source of truth.

• Clarified failure handling, idempotency, TTL strategy, cache key design, private networking, observability, and operational guardrails for real-world Azure workloads.

• Created reusable field guidance that helps customers, Microsoft teams, and Redis teams reason about caching consistency, write-path ownership, and Redis adoption patterns on Azure.