Core Concepts

Essential software engineering concepts: algorithms, data structures, databases, APIs, messaging, and system observability.

• Algorithm Complexity & Big-O

  Master Big-O notation and algorithm efficiency analysis. Learn how to evaluate code performance at scale, from constant O(1) to quadratic O(n²) complexity, with practical examples and real-world analogies.

• Caching Strategies

  Learn caching strategies to dramatically boost application performance. Explore cache layers from browser to distributed systems, invalidation patterns, cache-aside, write-through, and common pitfalls.

• Concurrency & Parallelism

  Understand concurrent programming, multithreading, and async operations. Write high-performance code while avoiding race conditions, deadlocks, and thread-safety issues with clear patterns and examples.

• Data Structures Essentials

  Essential guide to data structures: arrays, linked lists, stacks, queues, hash tables, trees, and graphs. Learn time/space complexity, use cases, and when to choose each structure for optimal performance.

• Database Design & SQL

  Database fundamentals from relational to NoSQL. Master schema design, normalization, indexing strategies, query optimization, transactions, ACID properties, and SQL vs NoSQL trade-offs.

• HTTP & RESTful APIs

  HTTP protocol and RESTful API design principles. Learn proper endpoint design, versioning strategies, authentication (OAuth, JWT), rate limiting, error handling, and building scalable APIs.

• Message Queues & Event-Driven Architecture

  Message queues and event-driven patterns for distributed systems. Explore pub/sub, message brokers (RabbitMQ, Kafka), event sourcing, CQRS, and building resilient asynchronous architectures.

• Observability: Logging, Metrics & Tracing

  The three pillars of observability for production systems. Master structured logging, metrics collection, distributed tracing, alerting strategies, and building dashboards for system health monitoring.