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Container networking enables communication between services in different containers.

Kubernetes uses a Control Plane to manage Worker Nodes and ensure application reliability.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Kubernetes uses Pods, ReplicaSets, and Deployments to manage application availability and updates.

Choosing the right boundaries for microservices is crucial for effective architecture.

Container data can be lost unless stored in persistent volumes.

Kubernetes Ingress allows one entry point for multiple applications, simplifying routing.

The Saga Pattern manages distributed transactions using local transactions and compensating actions.

ZooKeeper coordinates distributed systems using an ensemble of servers and the ZAB protocol.

Service Discovery helps microservices locate each other dynamically without fixed IP addresses.

Stream Processing Architecture helps turn incoming events into real-time insights efficiently.

Containers share the host OS kernel, while VMs virtualize hardware with separate OS.

Observability helps engineers understand system behavior to debug production issues effectively.

Availability Zones help prevent downtime by isolating failures across multiple locations.

Kafka uses brokers for data handling and KRaft controllers for metadata management.

MongoDB uses sharding to distribute data across multiple servers for better scalability.

Container health checks ensure your app is running and ready to serve users.

Kafka maintains message order within partitions using keys for routing.

Microservices can communicate using five key patterns based on their needs.

Distributed backpressure helps manage the flow of work between services to prevent overload.

A single component's failure can take down your entire application, even with redundancy.

MongoDB uses replica sets for data redundancy and sharding for scalability.

Microservices are useful when you need independent scaling, deployment, and clear boundaries in software design.

You can replay Kafka events safely by separating live and replay processes.

Database per Service ensures each microservice owns its data, reducing dependencies.

Older offsets can overwrite newer ones in Kafka if not handled properly.