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"When would you use Kafka over RabbitMQ?" — Every interviewer loves comparison questions. These aren't generic articles. Each comparison is framed for the interview: key differences, trade-offs, when to pick which, and exactly what to say. Includes real-world use cases and architecture implications.
The comparison questions with highest search volume — every interviewer asks at least one of these.
Quick verdict: Kafka for high-throughput streaming & event replay. RabbitMQ for traditional task queues with routing.
Quick verdict: SQL for structured data with complex joins. NoSQL for flexible schemas and horizontal scaling.
Quick verdict: PostgreSQL for advanced features (JSON, CTE, window functions). MySQL for simplicity and read-heavy workloads.
Quick verdict: Docker packages apps into containers. Kubernetes orchestrates containers at scale. They complement each other.
Quick verdict: Redis for data structures, persistence, pub/sub. Memcached for pure key-value caching at maximum simplicity.
Quick verdict: REST for simple, cacheable APIs. GraphQL for complex UIs needing flexible data fetching from multiple resources.
Quick verdict: AWS for breadth, GCP for data/ML, Azure for enterprise/Microsoft stack. All viable for most workloads.
Quick verdict: React for ecosystem/jobs. Vue for simplicity. Angular for opinionated enterprise apps with TypeScript.
Complete list across all categories. Each comparison includes trade-off tables, when-to-use flowcharts, and interview phrasing.
Kafka for high-throughput streaming & event replay. RabbitMQ for traditional task queues with routing.
SQL for structured data with complex joins. NoSQL for flexible schemas and horizontal scaling.
PostgreSQL for advanced features (JSON, CTE, window functions). MySQL for simplicity and read-heavy workloads.
Docker packages apps into containers. Kubernetes orchestrates containers at scale. They complement each other.
Redis for data structures, persistence, pub/sub. Memcached for pure key-value caching at maximum simplicity.
REST for simple, cacheable APIs. GraphQL for complex UIs needing flexible data fetching from multiple resources.
Start with monolith, decompose when needed. Microservices add complexity that's only worth it at scale.
AWS for breadth, GCP for data/ML, Azure for enterprise/Microsoft stack. All viable for most workloads.
Spring Boot for ecosystem maturity. Quarkus for cloud-native, fast startup, and GraalVM native compilation.
Django for full-featured web apps with ORM. FastAPI for high-performance async APIs with auto-docs.
Maven for convention-over-configuration simplicity. Gradle for flexibility, speed, and multi-project builds.
gRPC for internal service-to-service communication. REST for public-facing APIs and browser clients.
PostgreSQL for relational data with ACID. MongoDB for document-oriented data with schema flexibility.
React for ecosystem/jobs. Vue for simplicity. Angular for opinionated enterprise apps with TypeScript.
JWTs for stateless microservices. Server sessions for traditional apps needing easy revocation.
Sync for CPU-bound sequential logic. Async for I/O-bound concurrent operations (network, disk).
Kubernetes for production-scale orchestration. Docker Swarm for simple, small-scale deployments.
Terraform for infrastructure provisioning (declarative). Ansible for configuration management (procedural).
Next.js for React ecosystem with SSR/SSG. Nuxt.js for Vue ecosystem with similar capabilities.
JUnit 5 for modern Java testing (Spring default). TestNG for data-driven tests and parallel execution.
These comparisons appear as interview questions in your domain prep. Select your path and get them mapped alongside Q&A, system design, DSA, and behavioral.
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