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Container data can be lost unless stored in persistent volumes.

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

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

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

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

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

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

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

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

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

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

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

Container networking enables communication between services in different containers.

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

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

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

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

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

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

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

Availability is about access; reliability is about correct performance.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Kafka uses state stores to remember previous events in stateful stream processing.

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

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

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

Kafka Streams uses local state for quick data access and changelogs for recovery.

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

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

Kubernetes Services provide stable endpoints for applications despite changing Pod IPs.

Backup strategies include Full, Incremental, and Differential, each with unique benefits.

Global failover ensures applications remain available by redirecting traffic from failed regions to functioning ones.

Cassandra uses clustering keys to sort rows within a partition for efficient querying.

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

Cross-region database replication keeps databases synchronized across different geographical locations.

Batch processing collects data to process later, while stream processing handles data in real-time.

Multi-region data consistency ensures that users in different locations see the same data.

RPO and RTO are key metrics for planning disaster recovery strategies.

Kafka's static membership allows consumers to maintain stable identities, reducing unnecessary rebalances.

DNS resolution translates a URL into an IP address for web access.

Encryption at rest and in transit are essential for protecting data both stored and in motion.

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

Fallbacks allow systems to handle failures safely and maintain user experience.

Alerting helps notify the right people about production issues quickly.

Network partitions occur when servers are operational but can't communicate, affecting system performance.

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