However, the industry changes fast. What was standard five years ago (REST-only APIs, monolithic deployments) has evolved into GraphQL, gRPC, serverless architectures, and edge computing.
: Flexible schemas, horizontal scaling, and high-throughput key-value or document storage. 4. Concurrency, Networking, and Caching
As databases grow, query performance drops. Indexing creates a fast lookup path for data, similar to an index at the back of a textbook. While indexes speed up read operations significantly, they slow down write operations because the index must update with every new entry. Engineers must balance index usage to optimize overall database performance. Caching Strategies fundamentals of backend engineering free download upd
: Understanding the trade-offs between reliable, connection-oriented streams (TCP) and fast, connectionless datagrams (UDP).
Allows clients to request exactly the data they need in a single query. Developed by Google, built on top of HTTP/2. However, the industry changes fast
The backbone of any backend system.
Caching stores frequently accessed data in high-speed memory to reduce database load. Implementing tools like Redis or Memcached significantly speeds up response times for read-heavy applications. Microservices vs. Monoliths While indexes speed up read operations significantly, they
: Understand database design, normalization, indexing, and writing efficient queries.
[Client Request] ---> [Load Balancer] ---> [App Server 1] ---> [Cache (Redis)] ---> [App Server 2] ---> [Database (SQL)] Implement Load Balancing
The industry standard. It uses standard HTTP methods (GET, POST, PUT, DELETE) and relies on stateless communication. It is highly cacheable but can suffer from over-fetching or under-fetching data.