System Design Fundamentals Training

RexGalaxy Academy's System Design Fundamentals Training is a practical architecture and interview preparation program designed for learners who want to understand how scalable, reliable and secure software systems are planned, discussed and explained in real-world backend and system design interviews.

This course covers the complete system design foundation from requirement gathering and high-level architecture to databases, APIs, caching, load balancing, queues, scaling, reliability, security, observability, cloud deployment awareness and case-study presentation. Students learn how to convert a product requirement into architecture diagrams, service boundaries, API contracts, database models, capacity estimates, trade-off discussions and interview-ready explanations.

The training is practical and discussion-based. Learners practice whiteboard design, architecture diagrams, capacity estimation, API design, database selection, cache placement, queue-based workflows, monitoring plans, reliability checks, security decisions and case studies such as URL shortener, chat application, feed system, e-commerce checkout, notification system and file upload service.

Students should be able to:

• Gather functional and non-functional requirements clearly before designing a system.

• Draw high-level architecture diagrams with services, databases, caches, queues, load balancers, external services and monitoring components.

• Explain API contracts, database models, scaling choices, trade-offs, bottlenecks, failure points and reliability decisions.

• Understand core backend concepts including HTTP, DNS, load balancing, caching, databases, queues, consistency, availability, observability and cloud deployment.

• Prepare interview-ready design notes, diagrams, capacity estimates, trade-off explanations and portfolio case studies.

• Build confidence for system design interviews, backend architecture discussions and architecture-ready software roles.

Start with product thinking, requirement gathering, constraints, scope control and interview communication.

• Understand system design overview, what interviewers expect, how design rounds are evaluated and why structured thinking matters.

• Gather functional requirements such as user actions, core features, workflows, data entities, admin needs and business success criteria.

• Gather non-functional requirements such as scale, latency, availability, consistency, security, reliability, maintainability and cost expectations.

• Ask clarifying questions around scope, traffic, data size, peak load, read-write ratio, privacy, compliance and expected growth.

• Build high-level design thinking with client layer, service layer, storage, cache, queue, external services, monitoring and deployment view.

• Learn capacity estimation basics including daily active users, requests per second, reads versus writes, storage growth, bandwidth and peak traffic.

• Understand design constraints such as latency targets, throughput, fault tolerance, data retention, regional access, privacy and business priority.

• Practice trade-off thinking including simple versus scalable, cost versus performance, consistency versus availability and build versus buy decisions.

• Build diagram discipline with clean boxes, arrows, labels, boundaries, request paths, failure points and readable documentation.

• Outcome: collect requirements, control scope, estimate scale and begin a system design answer with confidence.

Design clean services, API contracts, data flow, client-server communication and modular backend boundaries.

• Compare architecture styles such as monolith, modular monolith, microservices awareness, service boundaries and choosing the right starting point.

• Understand client-server basics including request lifecycle, web client, mobile client, backend services, internal services and third-party integrations.

• Design APIs using REST endpoints, HTTP methods, request-response shape, status codes, versioning, pagination and error messages.

• Define service responsibilities such as controllers, business logic, repositories, validation, authentication checks and external service clients.

• Understand synchronous communication with direct API calls, timeout handling, retries, idempotency awareness and avoiding tight coupling.

• Understand asynchronous communication with background jobs, queues, event topics, delayed tasks and when async improves performance.

• Prepare data contracts with schemas, DTOs, input validation, backward compatibility, optional fields and clear ownership of fields.

• Compare internal APIs and public APIs with security, rate limits, documentation, monitoring, change control and consumer communication.

• Lab work includes designing API contracts for login, order creation, file upload, search, notifications and user profile services.

• Outcome: create clear service boundaries and API contracts that are simple, scalable and easy to explain.

Understand the request path from browser to backend using DNS, HTTP, TLS, proxies and load balancers.

• Trace internet request flow from browser to DNS lookup, CDN, load balancer, application server, database and response delivery.

• Understand HTTP fundamentals including methods, headers, cookies, sessions, status codes, request body, response body, keep-alive and compression.

• Review HTTPS and TLS awareness including encryption, certificates, termination, secure transport and where TLS is handled in architecture.

• Learn DNS concepts such as domain records, CNAME, A records, TTL, regional routing awareness, failover routing and common DNS mistakes.

• Understand load balancers including layer 4 versus layer 7 awareness, health checks, round-robin, least connections, sticky sessions and path routing.

• Learn reverse proxy and API gateway concepts such as request routing, authentication checks, rate limiting, logging and service discovery awareness.

• Identify latency contributors including network distance, DNS time, TLS handshake, server processing, database calls and payload size.

• Review failure points such as DNS failure, unhealthy backend, timeout, overload, certificate issue, wrong routing and bad deployment.

• Lab work includes drawing request flow for web app, API app, video app and e-commerce checkout with load balancer placement.

• Outcome: explain how traffic reaches services and where to improve performance, routing and availability.

Choose storage systems, design data models and explain database trade-offs for scalable applications.

• Compare relational databases and NoSQL databases using schema structure, relationships, query patterns, scaling, transactions and flexibility.

• Design database models for users, products, orders, payments, posts, comments, messages, files and activity logs.

• Understand normalization and denormalization trade-offs for read performance, write complexity, duplication and maintainability.

• Learn indexing basics for faster lookup, sorting, filtering, joins, composite indexes and query performance awareness.

• Discuss consistency concepts including ACID, transactions, eventual consistency, read replicas, replication lag and business correctness.

• Understand sharding and partitioning awareness including key selection, horizontal scaling, hot partitions and operational complexity.

• Compare object storage, file storage, blob storage, database storage and CDN delivery for media and large files.

• Plan data lifecycle including retention, archival, deletion, backups, restore, privacy and audit requirements.

• Lab work includes database models for URL shortener, chat app, feed system, e-commerce orders and file upload platform.

• Outcome: select suitable storage and explain database design decisions with practical trade-off reasoning.

Improve system speed using caching layers, CDN strategy, invalidation and performance-aware architecture.

• Understand caching purpose including reduced latency, lower database load, faster reads, cost control and better user experience.

• Compare client-side cache, browser cache, CDN cache, application cache, distributed cache and database query cache awareness.

• Learn cache patterns such as cache-aside, read-through awareness, write-through awareness, write-back awareness and refresh strategies.

• Understand cache keys, TTL, eviction policy, stale data, cache stampede, hot keys and cache invalidation challenges.

• Use CDN concepts for static assets, images, videos, global delivery, edge locations, cache headers and origin protection.

• Place caches for product catalogues, user sessions, feed pages, search results, profile data and rate-limit counters.

• Discuss performance optimization with pagination, compression, batching, lazy loading, database indexing and response size reduction.

• Identify when not to cache, especially sensitive data, frequently changing records and strict consistency workflows.

• Lab work includes cache design for product pages, news feed, URL redirection, login sessions and media delivery.

• Outcome: design practical caching and CDN strategies while explaining speed, freshness and consistency trade-offs.

Use queues and event-driven patterns to decouple systems, smooth traffic spikes and improve reliability.

• Understand why queues are used for background processing, load smoothing, retry handling, decoupling and long-running tasks.

• Compare synchronous and asynchronous workflows with examples such as order confirmation, email sending, image processing and notifications.

• Learn queue concepts including producer, consumer, broker, topic, message, acknowledgement, retry, dead-letter queue and idempotent processing.

• Understand event-driven design awareness including event publishing, subscribers, fan-out, audit streams and eventual consistency.

• Design background jobs for email, SMS, payment confirmation, invoice generation, report generation and media conversion.

• Handle duplicate messages, failed processing, poison messages, retry limits, ordering needs and back-pressure awareness.

• Use queues in scaling workflows where API response should be fast but processing can continue later.

• Discuss monitoring for queue length, consumer lag, failed messages, retry count and processing time.

• Lab work includes designing notification pipeline, order processing flow, file upload processing and event-based analytics pipeline.

• Outcome: design asynchronous systems with better resilience, scalability and clearer failure handling.

Plan systems that handle growth through horizontal scaling, replicas, partitioning, redundancy and failover.

• Understand vertical scaling and horizontal scaling, when each helps and why horizontal scaling becomes important at high traffic.

• Design stateless application servers so multiple instances can serve traffic behind a load balancer.

• Use database replicas for read scaling, backup support, failover awareness and read-write separation.

• Learn partitioning and sharding concepts including shard key, range-based partitioning, hash-based partitioning and hot-spot avoidance.

• Understand high availability using redundancy, multiple instances, health checks, auto-restart, failover and multi-zone awareness.

• Plan rate limiting and throttling to protect services from abuse, traffic spikes and expensive operations.

• Discuss capacity planning using RPS, peak load, storage growth, bandwidth, queue size and cache hit rate.

• Identify bottlenecks in CPU, memory, database, network, disk, external APIs and shared dependencies.

• Lab work includes scaling a chat app, URL shortener, feed system and e-commerce checkout from small to large traffic.

• Outcome: explain how a system grows and what changes are needed at each scaling stage.

Design systems that handle failures gracefully using redundancy, retries, timeouts and recovery planning.

• Understand reliability goals including uptime, availability, durability, fault tolerance, graceful degradation and recovery time.

• Learn failure types such as server crash, database outage, slow dependency, network timeout, queue backlog, bad deployment and regional failure.

• Use timeouts, retries, exponential backoff, circuit breaker awareness and fallback responses to prevent cascading failures.

• Design idempotent APIs so repeated requests do not create duplicate orders, payments or messages.

• Plan backup and restore strategy for databases, files, configuration, secrets awareness and disaster recovery drills.

• Understand replication, failover, health checks, rolling deployment awareness and rollback strategy.

• Use graceful degradation when non-critical features fail while core service remains available.

• Prepare incident thinking with alerting, logs, dashboards, ownership, communication and post-incident learning.

• Lab work includes failure analysis for payment service, notification service, database outage and traffic spike scenarios.

• Outcome: add reliability controls and explain how systems continue working during failures.

Add practical security thinking to system designs using authentication, authorization, encryption and abuse prevention.

• Understand authentication and authorization difference, login flow, session tokens, JWT awareness, role-based access and permission checks.

• Design secure API access using HTTPS, input validation, rate limiting, authentication middleware and safe error messages.

• Protect data with encryption in transit, encryption at rest awareness, password hashing, secrets management awareness and secure storage choices.

• Review common threats such as injection, broken access control, credential leakage, brute force, replay awareness and insecure direct object references.

• Plan privacy and data protection with least privilege, audit logs, data minimization, retention rules and user data deletion awareness.

• Add rate limiting, CAPTCHA awareness, device/session tracking and suspicious activity monitoring for abuse prevention.

• Secure file upload workflows with file validation, size limits, virus scan awareness, private buckets and signed URLs.

• Discuss security trade-offs between usability, performance, cost and protection level.

• Lab work includes designing secure login, role-based admin panel, file upload security and API rate limiting.

• Outcome: include security controls naturally in system designs and explain them clearly in interviews.

Design systems that can be monitored, debugged and operated safely in real production environments.

• Understand observability through logs, metrics, traces, dashboards, alerts and business health indicators.

• Plan application logging with request ID, user action, error details, timestamps, service name and privacy-safe log content.

• Track metrics such as latency, error rate, throughput, CPU, memory, database connections, cache hit rate and queue lag.

• Use tracing awareness to follow requests across services and identify slow dependencies or failing components.

• Design alerting for critical errors, downtime, high latency, queue backlog, database issues and failed background jobs.

• Prepare dashboards for API health, infrastructure health, business events and user-facing performance.

• Understand deployment awareness including CI/CD, environments, feature flags, rollback, versioning and release notes.

• Review operational ownership, runbooks, incident response, postmortems and continuous improvement.

• Lab work includes monitoring plan for URL shortener, chat app, feed service and e-commerce checkout.

• Outcome: design systems that teams can observe, troubleshoot, operate and improve over time.

Connect architecture concepts with cloud deployment awareness and practical design case studies.

• Understand cloud building blocks such as compute, managed database, object storage, CDN, load balancer, queue, monitoring and IAM awareness.

• Compare local deployment, VM deployment, container awareness, managed services and serverless awareness at a high level.

• Plan environment separation including development, staging, production, configuration, secrets and access control.

• Design case study one: URL shortener with API, database, cache, redirect flow, analytics, rate limit and monitoring.

• Design case study two: chat application with real-time messaging awareness, message storage, delivery status, notification and scaling concerns.

• Design case study three: social feed with post storage, fan-out awareness, caching, ranking basics and pagination.

• Design case study four: e-commerce checkout with cart, order, payment, inventory, notifications, idempotency and reliability.

• Design case study five: file upload service with object storage, metadata database, signed URLs, processing queue and CDN.

• Prepare architecture diagrams, trade-off notes, bottleneck analysis and presentation flow for each case study.

• Outcome: connect theory with practical portfolio case studies that are easy to present in interviews.

Prepare a final system design portfolio with diagrams, trade-offs, mock interviews and professional explanation practice.

• Learn interview answer structure: clarify requirements, estimate scale, propose high-level design, deep dive, discuss bottlenecks and summarize trade-offs.

• Prepare reusable templates for requirements, capacity estimation, API design, database schema, cache plan, queue plan and monitoring plan.

• Practice whiteboard communication with assumptions, labelled diagrams, request flow, data flow, failure points and decision explanations.

• Build portfolio case studies for URL shortener, chat app, feed system, e-commerce checkout, notification service and file upload platform.

• Improve trade-off explanation around SQL versus NoSQL, cache freshness, queue delay, consistency, availability, cost and complexity.

• Practice mock interview rounds with time limits, interviewer questions, design correction and final presentation feedback.

• Prepare final capstone documentation with architecture diagrams, API contracts, database models, estimates, security plan and observability plan.

• Review common mistakes such as jumping to tools too early, missing requirements, overengineering, unclear diagrams and ignoring failure cases.

• Build a personal revision checklist for core concepts, common systems, diagrams, bottlenecks and interview communication.

• Outcome: deliver an interview-ready system design capstone with clear architecture thinking, diagrams and confident explanation.