Practical engineering notes on APIs, integration, distributed systems, Salesforce, MuleSoft, DataWeave, testing, developer tooling and architecture.
Architecture, design decisions, resilience, scalability and production engineering.
A practical architecture pattern for isolating failed integration work, preserving appropriate recovery context, and enabling safe replay without turning transient failures into permanent data loss.
Read deep dive →A practical architecture guide to API governance across design standards, ownership, contracts, security policies, traffic controls, observability and lifecycle management.
Read deep dive →Experience, Process and System APIs are architectural responsibilities, not a requirement to add three network hops to every integration.
Read deep dive →Longer tutorials, integration guides and engineering notes across Salesforce, MuleSoft, DataWeave and distributed systems.
A practical architecture and implementation guide for consuming Salesforce Change Data Capture events with MuleSoft while designing replay, idempotency, recovery, and downstream processing deliberately.
Read article →A practical guide to consuming Salesforce Platform Events with MuleSoft, with replay, checkpointing, idempotency, durable handoff, and recovery considerations.
Read article →A practical framework for choosing between retry, replay, dead-letter queues, and an Error Hospital based on failure semantics, recovery needs, and operational complexity.
Read article →Learn how to iterate over Map entries in Mule 4 by combining the For Each scope with DataWeave's entrySet function.
Read article →Why a Mule 4 logger expression can work at runtime but fail in MUnit, and how to rewrite it as a valid DataWeave expression.
Read article →A practical example of combining DataWeave with a Java date-time utility to calculate the last Monday of the previous month in Mule 3.x and Mule 4.x.
Read article →Focused answers and implementation notes designed for quick reference.