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Choosing between single and multi-region architecture affects system resilience and cost.

Cassandra uses a masterless architecture to distribute and replicate data efficiently.

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

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

Active-Active Architecture uses multiple regions to serve traffic simultaneously for better resilience.

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

Fault domains help prevent multiple servers from failing together in an architecture.

Stateless servers are easier to scale because they don't store user session data locally.

FAANG engineers use high-level design principles to create scalable systems.

Active-passive architecture ensures a standby system takes over if the main system fails.

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

API Gateway, Proxy, and Load Balancer serve different roles in managing requests and traffic in systems.

Monolithic and microservices architectures each have unique advantages and challenges for software development.

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

A CDN speeds up web applications by caching content closer to users.

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

Data lakes store large volumes of diverse data for analytics and processing.

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

Data residency is crucial for compliance with laws about where data can be stored.

Data Lakehouse architecture merges flexible data storage with reliable analytics capabilities.

Microservices and modular monoliths differ mainly in deployment and communication methods.

Container data can be lost unless stored in persistent volumes.

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

Distributed caching spreads data across multiple servers for better performance and reliability.

Load balancers distribute traffic across multiple servers to enhance performance and reliability.

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

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

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

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

The CAP Theorem explains the trade-offs in distributed systems during network failures.

Elasticsearch uses distributed architecture to search data quickly across multiple servers.

Global Load Balancing helps route users to the nearest and healthiest server region.

Choosing between a shared database and a database per service affects system design significantly.

Dynamo-style databases are designed for high availability and fault tolerance in distributed systems.

gRPC allows efficient communication between services using a structured request-response model.

High Availability ensures your application remains operational even during server failures.

DNS helps microservices find each other, but it has limitations that may require a service registry.

A reverse proxy routes client requests to different backend services efficiently.

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

Zero Trust Architecture ensures that no user or device is trusted by default.

L4 and L7 load balancers differ in how they route network traffic based on layers of the OSI model.

An API Gateway simplifies client access to backend services in microservices.

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

Search engines collect, index, retrieve, and rank documents to provide relevant results.

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

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

CQRS separates how we change data from how we read data, improving scalability.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

REST API design focuses on creating clean, efficient, and scalable web services.

gRPC supports four communication patterns: Unary, Server Streaming, Client Streaming, and Bidirectional Streaming.

Dependency failures can disrupt applications, but resilience patterns can help manage them.

Disaster recovery ensures systems can restore services and data after major disruptions.

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

Secure API design requires multiple strategies to protect sensitive data and access.

Caching helps applications respond faster by serving data from memory instead of hitting the database.

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

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

Availability is about access; reliability is about correct performance.

Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

Sharding splits databases into smaller parts to improve performance and scalability.