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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

FAANG interviewers evaluate problem understanding, architecture, scalability, and communication skills.

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

HLD provides an overview of system architecture, while LLD details implementation specifics.

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

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

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

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

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

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

Graceful degradation allows apps to function partially during service failures.

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

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

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

Data warehouses help businesses analyze large amounts of data for better decision-making.

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

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

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

CDC with Kafka allows real-time updates from databases to multiple services.

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

Availability is about access; reliability is about correct performance.

OLTP handles transactions while OLAP is for data analysis and reporting.

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

The choice between SQL and NoSQL databases depends on your application's specific needs.

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

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

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

Container data can be lost unless stored in persistent volumes.

Database replication enhances data availability and read performance but has challenges like replication lag.

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

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

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

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