Microservices & Event-Driven Architecture (EDA) Mastery

Event Sourcing: Capturing every state change

1 Views Updated 5/4/2026

Deep Dive: Event Sourcing

In a normal database, you store the 'Current State'. In Event Sourcing, you store the 'History of Changes'. The current state is just a result of replaying those changes.

1. Why use it?

  • **Infinite Audit:** You know exactly who changed what and when.
  • **Time Travel:** You can see what the system looked like at 2 PM last Tuesday.
  • **Performance:** Writes are 'Append-Only', which is the fastest possible DB operation.

2. Snapshots

If an account has 10,000 transactions, replaying all of them every time you want to check the balance is slow. We use **Snapshots**. Every 100 events, we save the current state. When loading, we load the last snapshot and then only replay the events that happened after it.

4. Interview Mastery

Q: "What is the biggest challenge of Event Sourcing?"

Architect Answer: "**Versioning**. If you change a field name in an event today, you must still be able to read that event from 3 years ago. You either need to maintain 'Upcasters' (code that migrates old events to the new format on the fly) or keep your event schema extremely stable. Event Sourcing is a 'Marriage to your Data'—you have to be careful what you commit to."

Microservices & Event-Driven Architecture (EDA) Mastery
1. Foundations of Microservices
The Monolith to Microservices transition: When and why? Domain Driven Design (DDD): Bounded Contexts and Aggregates Database Per Service: Managing data consistency Service Discovery and Health Checks in .NET
2. Communication Patterns
Synchronous Communication: HTTP/gRPC and Service Mesh Asynchronous Communication: Message Brokers (RabbitMQ/Kafka) API Gateways: YARP (Yet Another Reverse Proxy) vs Ocelot Protobuf and Shared Contracts: Managing breaking changes
3. Event-Driven Architecture (EDA)
Introduction to EDA: Producers, Consumers, and Topics The Publisher/Subscriber Pattern in .NET Event Sourcing: Capturing every state change CQRS (Command Query Responsibility Segregation) with MediatR
4. Distributed Transactions & Resiliency
The Saga Pattern: Orchestration vs Choreography The Outbox Pattern: Ensuring reliable message delivery Idempotency: Preventing duplicate message processing Distributed Locking with Redis (Redlock)
5. Observability & Monitoring
Distributed Tracing with OpenTelemetry Centralized Logging: ELK Stack (Elasticsearch, Logstash, Kibana) Metrics and Dashboards: Prometheus and Grafana Correlation IDs: Tracking requests across services
6. Security & Identity
Centralized Authentication: IdentityServer4 & Duende Identity OAuth2 and OIDC Flow for Microservices API Key Management and Rate Limiting Mutual TLS (mTLS) for Internal Service-to-Service Security
7. Infrastructure & Deployment
Containerization: Production-grade Dockerfiles Kubernetes for .NET: Pods, Services, and Ingress Helm Charts: Managing complex deployments Blue-Green and Canary Deployments in K8s
8. FAANG Microservices Case Studies
Case Study: Designing a Global Notification Engine (Reliability at Scale) Case Study: Building a High-Performance Logging Pipeline (PB/Day)