Spring Boot & Microservices Master Program
An intensive 4-month online weekend live engineering cohort (Sat & Sun • 4 hours/day: 2h live faculty lectures + 2h supervised coding labs) covering Spring Boot & Microservices production capstones and 580+ AI interviews, with eligible hiring-drive access under documented placement terms and tuition-refund protection where all policy conditions are met.
- Online Weekend Live Sessions (Sat & Sun • 4 Hours / Day)
- 4 Months (16 Weeks) Comprehensive Finish
- Guaranteed Placement Drives until Placed across 1,050+ top companies
- 26 Production Microservices & Capstones with GitHub code reviews
- 580+ Topic Mock Tests & 1-on-1 AI Voice Technical Interviews
Our Graduates Get Marketed to 1,050+ Global Tech Leaders & Unicorns
Continuous corporate interview referrals until job offer letter issuance:
Online Weekend Master Track: 4 Months of Live Mentorship & Placement Drives
Designed for college students and working professionals, our Online Weekend Master Track delivers intensive 4-hour live sessions every Saturday and Sunday across 16 weeks (4 Months). Complete 128+ hours of live faculty instruction, 26 production capstones, and 580+ AI interviews with unlimited corporate interview drives until you get placed!
Live Architecture & Core Mentorship
2 Hours of interactive enterprise architecture, live faculty coding, and design patterns followed by 2 Hours of supervised capstone development.
Hands-On Labs, Tests & AI Practice
2 Hours of advanced microservices, real-time queues & cloud deployment followed by 2 Hours of timed mock tests and 1-on-1 AI voice interview rounds.
4-Month Master Roadmap to Guaranteed Placement
We transform you into a battle-tested software engineer ready to clear Tier-1 technical and system design interview rounds in 4 months with weekend online sessions.
Architecture & Core Mechanics
Core OOP, memory mechanics, data structures, algorithms & clean design patterns.
Capstones & Microservices
Build production full stack SaaS, microservices, REST APIs, queues & cloud deployment.
System Design & AI Interviews
Simulate live FAANG interview rounds, timed topic mock tests and AI voice evaluations.
Corporate Drives until Placed
Resume marketing, hiring drives across 1,050+ partners, and placement guarantee.
Projected Target CTC After Program
Industry-verified compensation brackets achieved by graduates across 1,050+ hiring partners:
₹8.5L – ₹14L /yr
Software Engineer I, Junior Backend Developer, Full Stack Associate.
- 260+ Hours Live Mentorship
- 26 Capstone Projects on GitHub
- 580+ AI Technical Interview Scorecards
₹14L – ₹24L /yr
Full Stack Java Engineer, Spring Boot Microservices Specialist, Cloud Engineer.
- Kafka Event-Driven Architectures
- Redis Caching & Performance Tuning
- Docker, Kubernetes & AWS CI/CD
₹24L – ₹36L+ /yr
Senior Full Stack Engineer, Microservices Architect, Lead Consultant.
- High-Throughput System Design (LLD/HLD)
- Fault Tolerance & Distributed Transactions
- FAANG System Design Clearing Mentorship
Topic-Wise Curriculum & Practice Hub
51 Modules • 351 Deep-Dive Topics • 351 Integrated Topic Mock Tests & AI Interviews
Modules 1–4: Why Spring?, Spring Framework Architecture, Spring Boot, Spring Boot 4 Project Structure.
Why Spring?
Understand:; Dependency management; enterprise application structure; testability; configuration; dependency injection
- Explain the core concepts and architecture of Why Spring?.
- Apply Why Spring? in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Spring Framework Architecture
Cover:; Core container; Beans; Context; AOP; Data access; MVC; Security integration
- Explain the core concepts and architecture of Spring Framework Architecture.
- Apply Spring Framework Architecture in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Spring Boot
Understand:; Boot starters; auto-configuration; embedded server; external configuration; production features; Spring Boot explicitly positions metrics, health checks, security and externalized configuration as core production-oriented capabilities. ([Home][1])
- Explain the core concepts and architecture of Spring Boot.
- Apply Spring Boot in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Spring Boot 4 Project Structure
Recommended layout:; text; com.mockattempt.orders; ├── api; ├── application; ├── domain; ├── infrastructure; ├── configuration; └── common; Teach maintainable package organization.; =============================================
- Explain the core concepts and architecture of Spring Boot 4 Project Structure.
- Apply Spring Boot 4 Project Structure in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 5–14: IoC, Dependency Injection, Constructor Injection, Beans, Bean Scope, Bean Lifecycle, Component Scanning, @Configuration and 2 more topics.
IoC
Core concepts, implementation patterns, engineering trade-offs and production considerations for IoC.
- Explain the core concepts and architecture of IoC.
- Apply IoC in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Dependency Injection
Core concepts, implementation patterns, engineering trade-offs and production considerations for Dependency Injection.
- Explain the core concepts and architecture of Dependency Injection.
- Apply Dependency Injection in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Constructor Injection
Preferred in most application code.
- Explain the core concepts and architecture of Constructor Injection.
- Apply Constructor Injection in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Beans
Core concepts, implementation patterns, engineering trade-offs and production considerations for Beans.
- Explain the core concepts and architecture of Beans.
- Apply Beans in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Bean Scope
Core concepts, implementation patterns, engineering trade-offs and production considerations for Bean Scope.
- Explain the core concepts and architecture of Bean Scope.
- Apply Bean Scope in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Bean Lifecycle
Core concepts, implementation patterns, engineering trade-offs and production considerations for Bean Lifecycle.
- Explain the core concepts and architecture of Bean Lifecycle.
- Apply Bean Lifecycle in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Component Scanning
Core concepts, implementation patterns, engineering trade-offs and production considerations for Component Scanning.
- Explain the core concepts and architecture of Component Scanning.
- Apply Component Scanning in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
@Configuration
Core concepts, implementation patterns, engineering trade-offs and production considerations for @Configuration.
- Explain the core concepts and architecture of @Configuration.
- Apply @Configuration in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
@Bean
Core concepts, implementation patterns, engineering trade-offs and production considerations for @Bean.
- Explain the core concepts and architecture of @Bean.
- Apply @Bean in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Conditional Beans Concepts
LAB; Build pluggable:; text; PaymentService; UPIPaymentService; CardPaymentService; WalletPaymentService; using dependency injection.; =============================================
- Explain the core concepts and architecture of Conditional Beans Concepts.
- Apply Conditional Beans Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 15–20: application.properties, YAML, Profiles, Environment Variables, ConfigurationProperties, Secret Separation.
application.properties
Core concepts, implementation patterns, engineering trade-offs and production considerations for application.properties.
- Explain the core concepts and architecture of application.properties.
- Apply application.properties in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
YAML
Core concepts, implementation patterns, engineering trade-offs and production considerations for YAML.
- Explain the core concepts and architecture of YAML.
- Apply YAML in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Profiles
Example:; text; application-dev.yml; application-test.yml; application-prod.yml
- Explain the core concepts and architecture of Profiles.
- Apply Profiles in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Environment Variables
Core concepts, implementation patterns, engineering trade-offs and production considerations for Environment Variables.
- Explain the core concepts and architecture of Environment Variables.
- Apply Environment Variables in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
ConfigurationProperties
Core concepts, implementation patterns, engineering trade-offs and production considerations for ConfigurationProperties.
- Explain the core concepts and architecture of ConfigurationProperties.
- Apply ConfigurationProperties in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Secret Separation
Never store production credentials in Git.; =============================================
- Explain the core concepts and architecture of Secret Separation.
- Apply Secret Separation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 21–30: DispatcherServlet, Controllers, Request Mapping, RequestBody, RequestParam, PathVariable, ResponseEntity, Validation and 2 more topics.
DispatcherServlet
Core concepts, implementation patterns, engineering trade-offs and production considerations for DispatcherServlet.
- Explain the core concepts and architecture of DispatcherServlet.
- Apply DispatcherServlet in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Controllers
Core concepts, implementation patterns, engineering trade-offs and production considerations for Controllers.
- Explain the core concepts and architecture of Controllers.
- Apply Controllers in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Request Mapping
Core concepts, implementation patterns, engineering trade-offs and production considerations for Request Mapping.
- Explain the core concepts and architecture of Request Mapping.
- Apply Request Mapping in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
RequestBody
Core concepts, implementation patterns, engineering trade-offs and production considerations for RequestBody.
- Explain the core concepts and architecture of RequestBody.
- Apply RequestBody in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
RequestParam
Core concepts, implementation patterns, engineering trade-offs and production considerations for RequestParam.
- Explain the core concepts and architecture of RequestParam.
- Apply RequestParam in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
PathVariable
Core concepts, implementation patterns, engineering trade-offs and production considerations for PathVariable.
- Explain the core concepts and architecture of PathVariable.
- Apply PathVariable in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
ResponseEntity
Core concepts, implementation patterns, engineering trade-offs and production considerations for ResponseEntity.
- Explain the core concepts and architecture of ResponseEntity.
- Apply ResponseEntity in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Validation
Core concepts, implementation patterns, engineering trade-offs and production considerations for Validation.
- Explain the core concepts and architecture of Validation.
- Apply Validation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Filters
Core concepts, implementation patterns, engineering trade-offs and production considerations for Filters.
- Explain the core concepts and architecture of Filters.
- Apply Filters in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Interceptors
=============================================
- Explain the core concepts and architecture of Interceptors.
- Apply Interceptors in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 31–46: REST Principles, Resource Design, HTTP Semantics, Status Codes, DTO Design, Input Validation, Error Responses, Global Exception Handling and 8 more topics.
REST Principles
Core concepts, implementation patterns, engineering trade-offs and production considerations for REST Principles.
- Explain the core concepts and architecture of REST Principles.
- Apply REST Principles in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Resource Design
Prefer:; text; GET /api/orders/{id}; instead of:; GET /api/getOrder?id=10
- Explain the core concepts and architecture of Resource Design.
- Apply Resource Design in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
HTTP Semantics
Understand:; GET; POST; PUT; PATCH; DELETE
- Explain the core concepts and architecture of HTTP Semantics.
- Apply HTTP Semantics in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Status Codes
Core concepts, implementation patterns, engineering trade-offs and production considerations for Status Codes.
- Explain the core concepts and architecture of Status Codes.
- Apply Status Codes in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
DTO Design
Separate persistence entities from public API models.
- Explain the core concepts and architecture of DTO Design.
- Apply DTO Design in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Input Validation
Core concepts, implementation patterns, engineering trade-offs and production considerations for Input Validation.
- Explain the core concepts and architecture of Input Validation.
- Apply Input Validation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Error Responses
Standard error object:; json; {; "code": "ORDER_NOT_FOUND",; "message": "Order not found",; "timestamp": "...",; "traceId": "..."; }
- Explain the core concepts and architecture of Error Responses.
- Apply Error Responses in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Global Exception Handling
Core concepts, implementation patterns, engineering trade-offs and production considerations for Global Exception Handling.
- Explain the core concepts and architecture of Global Exception Handling.
- Apply Global Exception Handling in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Pagination
Core concepts, implementation patterns, engineering trade-offs and production considerations for Pagination.
- Explain the core concepts and architecture of Pagination.
- Apply Pagination in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Sorting
Core concepts, implementation patterns, engineering trade-offs and production considerations for Sorting.
- Explain the core concepts and architecture of Sorting.
- Apply Sorting in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Filtering
Core concepts, implementation patterns, engineering trade-offs and production considerations for Filtering.
- Explain the core concepts and architecture of Filtering.
- Apply Filtering in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Search
Core concepts, implementation patterns, engineering trade-offs and production considerations for Search.
- Explain the core concepts and architecture of Search.
- Apply Search in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
API Versioning
Spring Framework 7 introduced first-class API-versioning capabilities, so versioning should be covered as a modern API-engineering concern. ([Spring][4])
- Explain the core concepts and architecture of API Versioning.
- Apply API Versioning in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Idempotent APIs
Critical for:; payments; orders; booking; retries
- Explain the core concepts and architecture of Idempotent APIs.
- Apply Idempotent APIs in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
OpenAPI
Core concepts, implementation patterns, engineering trade-offs and production considerations for OpenAPI.
- Explain the core concepts and architecture of OpenAPI.
- Apply OpenAPI in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Swagger
PROJECT 1; Production Order REST API; Features:; Create order; update; lookup; pagination; validation; standardized errors; API documentation; =============================================
- Explain the core concepts and architecture of Swagger.
- Apply Swagger in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 47–60: PostgreSQL, Data Modeling, JPA Fundamentals, Entity, Repository, Entity Relationships, Lazy Loading, Eager Loading and 6 more topics.
PostgreSQL
Core concepts, implementation patterns, engineering trade-offs and production considerations for PostgreSQL.
- Explain the core concepts and architecture of PostgreSQL.
- Apply PostgreSQL in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Data Modeling
Core concepts, implementation patterns, engineering trade-offs and production considerations for Data Modeling.
- Explain the core concepts and architecture of Data Modeling.
- Apply Data Modeling in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
JPA Fundamentals
Core concepts, implementation patterns, engineering trade-offs and production considerations for JPA Fundamentals.
- Explain the core concepts and architecture of JPA Fundamentals.
- Apply JPA Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Entity
Core concepts, implementation patterns, engineering trade-offs and production considerations for Entity.
- Explain the core concepts and architecture of Entity.
- Apply Entity in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Repository
Core concepts, implementation patterns, engineering trade-offs and production considerations for Repository.
- Explain the core concepts and architecture of Repository.
- Apply Repository in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Entity Relationships
OneToOne; OneToMany; ManyToOne; ManyToMany
- Explain the core concepts and architecture of Entity Relationships.
- Apply Entity Relationships in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Lazy Loading
Core concepts, implementation patterns, engineering trade-offs and production considerations for Lazy Loading.
- Explain the core concepts and architecture of Lazy Loading.
- Apply Lazy Loading in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Eager Loading
Core concepts, implementation patterns, engineering trade-offs and production considerations for Eager Loading.
- Explain the core concepts and architecture of Eager Loading.
- Apply Eager Loading in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Cascades
Core concepts, implementation patterns, engineering trade-offs and production considerations for Cascades.
- Explain the core concepts and architecture of Cascades.
- Apply Cascades in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Orphan Removal
Core concepts, implementation patterns, engineering trade-offs and production considerations for Orphan Removal.
- Explain the core concepts and architecture of Orphan Removal.
- Apply Orphan Removal in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Persistence Context
Core concepts, implementation patterns, engineering trade-offs and production considerations for Persistence Context.
- Explain the core concepts and architecture of Persistence Context.
- Apply Persistence Context in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Dirty Checking
Core concepts, implementation patterns, engineering trade-offs and production considerations for Dirty Checking.
- Explain the core concepts and architecture of Dirty Checking.
- Apply Dirty Checking in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
JPQL
Core concepts, implementation patterns, engineering trade-offs and production considerations for JPQL.
- Explain the core concepts and architecture of JPQL.
- Apply JPQL in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Native Queries
=============================================
- Explain the core concepts and architecture of Native Queries.
- Apply Native Queries in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 61–68: N+1 Query Problem, Fetch Join, Entity Graph Concepts, Batch Fetching, Projection, Pagination, Database Indexing, Query Plans.
N+1 Query Problem
Core concepts, implementation patterns, engineering trade-offs and production considerations for N+1 Query Problem.
- Explain the core concepts and architecture of N+1 Query Problem.
- Apply N+1 Query Problem in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Fetch Join
Core concepts, implementation patterns, engineering trade-offs and production considerations for Fetch Join.
- Explain the core concepts and architecture of Fetch Join.
- Apply Fetch Join in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Entity Graph Concepts
Core concepts, implementation patterns, engineering trade-offs and production considerations for Entity Graph Concepts.
- Explain the core concepts and architecture of Entity Graph Concepts.
- Apply Entity Graph Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Batch Fetching
Core concepts, implementation patterns, engineering trade-offs and production considerations for Batch Fetching.
- Explain the core concepts and architecture of Batch Fetching.
- Apply Batch Fetching in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Projection
Core concepts, implementation patterns, engineering trade-offs and production considerations for Projection.
- Explain the core concepts and architecture of Projection.
- Apply Projection in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Pagination
Core concepts, implementation patterns, engineering trade-offs and production considerations for Pagination.
- Explain the core concepts and architecture of Pagination.
- Apply Pagination in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Database Indexing
Core concepts, implementation patterns, engineering trade-offs and production considerations for Database Indexing.
- Explain the core concepts and architecture of Database Indexing.
- Apply Database Indexing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Query Plans
JPA LAB; Initial endpoint:; text; GET /customers; produces:; 1001 SQL queries; Student must optimize it.; =============================================
- Explain the core concepts and architecture of Query Plans.
- Apply Query Plans in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 69–78: @Transactional, ACID, Transaction Boundaries, Propagation, Isolation, Rollback, Optimistic Locking, Pessimistic Locking and 2 more topics.
@Transactional
Core concepts, implementation patterns, engineering trade-offs and production considerations for @Transactional.
- Explain the core concepts and architecture of @Transactional.
- Apply @Transactional in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
ACID
Core concepts, implementation patterns, engineering trade-offs and production considerations for ACID.
- Explain the core concepts and architecture of ACID.
- Apply ACID in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Transaction Boundaries
Core concepts, implementation patterns, engineering trade-offs and production considerations for Transaction Boundaries.
- Explain the core concepts and architecture of Transaction Boundaries.
- Apply Transaction Boundaries in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Propagation
Core concepts, implementation patterns, engineering trade-offs and production considerations for Propagation.
- Explain the core concepts and architecture of Propagation.
- Apply Propagation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Isolation
Core concepts, implementation patterns, engineering trade-offs and production considerations for Isolation.
- Explain the core concepts and architecture of Isolation.
- Apply Isolation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Rollback
Core concepts, implementation patterns, engineering trade-offs and production considerations for Rollback.
- Explain the core concepts and architecture of Rollback.
- Apply Rollback in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Optimistic Locking
Core concepts, implementation patterns, engineering trade-offs and production considerations for Optimistic Locking.
- Explain the core concepts and architecture of Optimistic Locking.
- Apply Optimistic Locking in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Pessimistic Locking
Core concepts, implementation patterns, engineering trade-offs and production considerations for Pessimistic Locking.
- Explain the core concepts and architecture of Pessimistic Locking.
- Apply Pessimistic Locking in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Lost Update
Core concepts, implementation patterns, engineering trade-offs and production considerations for Lost Update.
- Explain the core concepts and architecture of Lost Update.
- Apply Lost Update in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Deadlocks
TRANSACTION LAB; Stock:; text; AVAILABLE = 1; Two users purchase simultaneously.; Student must prevent:; stock = -1; =============================================
- Explain the core concepts and architecture of Deadlocks.
- Apply Deadlocks in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 79–86: Authentication, Authorization, Spring Security Architecture, SecurityFilterChain, Password Encoding, Roles, Authorities, Method Security.
Authentication
Core concepts, implementation patterns, engineering trade-offs and production considerations for Authentication.
- Explain the core concepts and architecture of Authentication.
- Apply Authentication in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Authorization
Core concepts, implementation patterns, engineering trade-offs and production considerations for Authorization.
- Explain the core concepts and architecture of Authorization.
- Apply Authorization in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Spring Security Architecture
Core concepts, implementation patterns, engineering trade-offs and production considerations for Spring Security Architecture.
- Explain the core concepts and architecture of Spring Security Architecture.
- Apply Spring Security Architecture in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
SecurityFilterChain
Core concepts, implementation patterns, engineering trade-offs and production considerations for SecurityFilterChain.
- Explain the core concepts and architecture of SecurityFilterChain.
- Apply SecurityFilterChain in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Password Encoding
Core concepts, implementation patterns, engineering trade-offs and production considerations for Password Encoding.
- Explain the core concepts and architecture of Password Encoding.
- Apply Password Encoding in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Roles
Core concepts, implementation patterns, engineering trade-offs and production considerations for Roles.
- Explain the core concepts and architecture of Roles.
- Apply Roles in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Authorities
Core concepts, implementation patterns, engineering trade-offs and production considerations for Authorities.
- Explain the core concepts and architecture of Authorities.
- Apply Authorities in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Method Security
=============================================
- Explain the core concepts and architecture of Method Security.
- Apply Method Security in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 87–94: JWT Structure, Access Tokens, Refresh Tokens, Expiration, Token Validation, Signing Keys, Rotation Concepts, Revocation.
JWT Structure
Core concepts, implementation patterns, engineering trade-offs and production considerations for JWT Structure.
- Explain the core concepts and architecture of JWT Structure.
- Apply JWT Structure in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Access Tokens
Core concepts, implementation patterns, engineering trade-offs and production considerations for Access Tokens.
- Explain the core concepts and architecture of Access Tokens.
- Apply Access Tokens in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Refresh Tokens
Core concepts, implementation patterns, engineering trade-offs and production considerations for Refresh Tokens.
- Explain the core concepts and architecture of Refresh Tokens.
- Apply Refresh Tokens in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Expiration
Core concepts, implementation patterns, engineering trade-offs and production considerations for Expiration.
- Explain the core concepts and architecture of Expiration.
- Apply Expiration in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Token Validation
Core concepts, implementation patterns, engineering trade-offs and production considerations for Token Validation.
- Explain the core concepts and architecture of Token Validation.
- Apply Token Validation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Signing Keys
Core concepts, implementation patterns, engineering trade-offs and production considerations for Signing Keys.
- Explain the core concepts and architecture of Signing Keys.
- Apply Signing Keys in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Rotation Concepts
Core concepts, implementation patterns, engineering trade-offs and production considerations for Rotation Concepts.
- Explain the core concepts and architecture of Rotation Concepts.
- Apply Rotation Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Revocation
=============================================
- Explain the core concepts and architecture of Revocation.
- Apply Revocation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 95–103: OAuth2 Fundamentals, Resource Server, OAuth Client, OpenID Connect, Authorization Code, PKCE, Client Credentials, Scopes and 1 more topics.
OAuth2 Fundamentals
Core concepts, implementation patterns, engineering trade-offs and production considerations for OAuth2 Fundamentals.
- Explain the core concepts and architecture of OAuth2 Fundamentals.
- Apply OAuth2 Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Resource Server
Core concepts, implementation patterns, engineering trade-offs and production considerations for Resource Server.
- Explain the core concepts and architecture of Resource Server.
- Apply Resource Server in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
OAuth Client
Core concepts, implementation patterns, engineering trade-offs and production considerations for OAuth Client.
- Explain the core concepts and architecture of OAuth Client.
- Apply OAuth Client in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
OpenID Connect
Core concepts, implementation patterns, engineering trade-offs and production considerations for OpenID Connect.
- Explain the core concepts and architecture of OpenID Connect.
- Apply OpenID Connect in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Authorization Code
Core concepts, implementation patterns, engineering trade-offs and production considerations for Authorization Code.
- Explain the core concepts and architecture of Authorization Code.
- Apply Authorization Code in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
PKCE
Core concepts, implementation patterns, engineering trade-offs and production considerations for PKCE.
- Explain the core concepts and architecture of PKCE.
- Apply PKCE in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Client Credentials
Core concepts, implementation patterns, engineering trade-offs and production considerations for Client Credentials.
- Explain the core concepts and architecture of Client Credentials.
- Apply Client Credentials in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Scopes
Core concepts, implementation patterns, engineering trade-offs and production considerations for Scopes.
- Explain the core concepts and architecture of Scopes.
- Apply Scopes in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Identity Provider Concepts
PROJECT 2; Enterprise Identity & Authorization API; Support:; users; login; JWT; roles; method security; OAuth2 Resource Server concepts; =============================================
- Explain the core concepts and architecture of Identity Provider Concepts.
- Apply Identity Provider Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 104–113: Broken Access Control, Object-Level Authorization, SQL Injection, CSRF, CORS, Rate Limiting, Input Validation, Secrets and 2 more topics.
Broken Access Control
Core concepts, implementation patterns, engineering trade-offs and production considerations for Broken Access Control.
- Explain the core concepts and architecture of Broken Access Control.
- Apply Broken Access Control in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Object-Level Authorization
Core concepts, implementation patterns, engineering trade-offs and production considerations for Object-Level Authorization.
- Explain the core concepts and architecture of Object-Level Authorization.
- Apply Object-Level Authorization in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
SQL Injection
Core concepts, implementation patterns, engineering trade-offs and production considerations for SQL Injection.
- Explain the core concepts and architecture of SQL Injection.
- Apply SQL Injection in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
CSRF
Core concepts, implementation patterns, engineering trade-offs and production considerations for CSRF.
- Explain the core concepts and architecture of CSRF.
- Apply CSRF in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
CORS
Core concepts, implementation patterns, engineering trade-offs and production considerations for CORS.
- Explain the core concepts and architecture of CORS.
- Apply CORS in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Rate Limiting
Core concepts, implementation patterns, engineering trade-offs and production considerations for Rate Limiting.
- Explain the core concepts and architecture of Rate Limiting.
- Apply Rate Limiting in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Input Validation
Core concepts, implementation patterns, engineering trade-offs and production considerations for Input Validation.
- Explain the core concepts and architecture of Input Validation.
- Apply Input Validation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Secrets
Core concepts, implementation patterns, engineering trade-offs and production considerations for Secrets.
- Explain the core concepts and architecture of Secrets.
- Apply Secrets in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Sensitive Logging
Core concepts, implementation patterns, engineering trade-offs and production considerations for Sensitive Logging.
- Explain the core concepts and architecture of Sensitive Logging.
- Apply Sensitive Logging in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Security Headers
=============================================
- Explain the core concepts and architecture of Security Headers.
- Apply Security Headers in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 114–114: Why Start Modular?.
Why Start Modular?
Explain why a badly designed monolith becomes badly designed microservices.; =============================================
- Explain the core concepts and architecture of Why Start Modular?.
- Apply Why Start Modular? in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 115–123: What Is a Microservice?, Monolith vs Microservices, When Microservices Make Sense, When They Do Not, Service Autonomy, Independent Deployment, Database per Service, Loose Coupling and 1 more topics.
What Is a Microservice?
Core concepts, implementation patterns, engineering trade-offs and production considerations for What Is a Microservice?.
- Explain the core concepts and architecture of What Is a Microservice?.
- Apply What Is a Microservice? in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Monolith vs Microservices
Core concepts, implementation patterns, engineering trade-offs and production considerations for Monolith vs Microservices.
- Explain the core concepts and architecture of Monolith vs Microservices.
- Apply Monolith vs Microservices in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
When Microservices Make Sense
Core concepts, implementation patterns, engineering trade-offs and production considerations for When Microservices Make Sense.
- Explain the core concepts and architecture of When Microservices Make Sense.
- Apply When Microservices Make Sense in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
When They Do Not
Core concepts, implementation patterns, engineering trade-offs and production considerations for When They Do Not.
- Explain the core concepts and architecture of When They Do Not.
- Apply When They Do Not in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Service Autonomy
Core concepts, implementation patterns, engineering trade-offs and production considerations for Service Autonomy.
- Explain the core concepts and architecture of Service Autonomy.
- Apply Service Autonomy in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Independent Deployment
Core concepts, implementation patterns, engineering trade-offs and production considerations for Independent Deployment.
- Explain the core concepts and architecture of Independent Deployment.
- Apply Independent Deployment in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Database per Service
Core concepts, implementation patterns, engineering trade-offs and production considerations for Database per Service.
- Explain the core concepts and architecture of Database per Service.
- Apply Database per Service in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Loose Coupling
Core concepts, implementation patterns, engineering trade-offs and production considerations for Loose Coupling.
- Explain the core concepts and architecture of Loose Coupling.
- Apply Loose Coupling in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Bounded Context
=============================================
- Explain the core concepts and architecture of Bounded Context.
- Apply Bounded Context in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 124–128: Domain Boundaries, Business Capability Decomposition, DDD Boundaries, Data Ownership, Shared Database Anti-Pattern.
Domain Boundaries
Bad decomposition:; text; Controller Service; Repository Service; Database Service; Better:; Order Service; Inventory Service; Payment Service; Customer Service
- Explain the core concepts and architecture of Domain Boundaries.
- Apply Domain Boundaries in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Business Capability Decomposition
Core concepts, implementation patterns, engineering trade-offs and production considerations for Business Capability Decomposition.
- Explain the core concepts and architecture of Business Capability Decomposition.
- Apply Business Capability Decomposition in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
DDD Boundaries
Core concepts, implementation patterns, engineering trade-offs and production considerations for DDD Boundaries.
- Explain the core concepts and architecture of DDD Boundaries.
- Apply DDD Boundaries in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Data Ownership
Core concepts, implementation patterns, engineering trade-offs and production considerations for Data Ownership.
- Explain the core concepts and architecture of Data Ownership.
- Apply Data Ownership in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Shared Database Anti-Pattern
ARCHITECTURE LAB; Given a large e-commerce monolith, students define microservice boundaries.; =============================================
- Explain the core concepts and architecture of Shared Database Anti-Pattern.
- Apply Shared Database Anti-Pattern in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 129–133: Service-to-Service REST, RestClient, HTTP Interface Clients, WebClient Awareness, gRPC Support.
Service-to-Service REST
Core concepts, implementation patterns, engineering trade-offs and production considerations for Service-to-Service REST.
- Explain the core concepts and architecture of Service-to-Service REST.
- Apply Service-to-Service REST in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
RestClient
Core concepts, implementation patterns, engineering trade-offs and production considerations for RestClient.
- Explain the core concepts and architecture of RestClient.
- Apply RestClient in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
HTTP Interface Clients
Spring Framework 7 includes updated HTTP-interface client configuration, making this relevant to current Spring service-to-service design. ([Spring][4])
- Explain the core concepts and architecture of HTTP Interface Clients.
- Apply HTTP Interface Clients in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
WebClient Awareness
Core concepts, implementation patterns, engineering trade-offs and production considerations for WebClient Awareness.
- Explain the core concepts and architecture of WebClient Awareness.
- Apply WebClient Awareness in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
gRPC Support
Spring Boot 4.1 added Spring gRPC support, so students should understand when REST vs gRPC may be appropriate. ([Spring][5]); =============================================
- Explain the core concepts and architecture of gRPC Support.
- Apply gRPC Support in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 134–139: Timeout, Retry, Exponential Backoff, Jitter, Connection Pooling, Partial Failure.
Timeout
Core concepts, implementation patterns, engineering trade-offs and production considerations for Timeout.
- Explain the core concepts and architecture of Timeout.
- Apply Timeout in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Retry
Core concepts, implementation patterns, engineering trade-offs and production considerations for Retry.
- Explain the core concepts and architecture of Retry.
- Apply Retry in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Exponential Backoff
Core concepts, implementation patterns, engineering trade-offs and production considerations for Exponential Backoff.
- Explain the core concepts and architecture of Exponential Backoff.
- Apply Exponential Backoff in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Jitter
Core concepts, implementation patterns, engineering trade-offs and production considerations for Jitter.
- Explain the core concepts and architecture of Jitter.
- Apply Jitter in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Connection Pooling
Core concepts, implementation patterns, engineering trade-offs and production considerations for Connection Pooling.
- Explain the core concepts and architecture of Connection Pooling.
- Apply Connection Pooling in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Partial Failure
=============================================
- Explain the core concepts and architecture of Partial Failure.
- Apply Partial Failure in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 140–146: Circuit Breaker, Bulkhead, Rate Limiter, Retry, Time Limiter, Resilience4j Concepts, Spring Cloud Circuit Breaker.
Circuit Breaker
Core concepts, implementation patterns, engineering trade-offs and production considerations for Circuit Breaker.
- Explain the core concepts and architecture of Circuit Breaker.
- Apply Circuit Breaker in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Bulkhead
Core concepts, implementation patterns, engineering trade-offs and production considerations for Bulkhead.
- Explain the core concepts and architecture of Bulkhead.
- Apply Bulkhead in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Rate Limiter
Core concepts, implementation patterns, engineering trade-offs and production considerations for Rate Limiter.
- Explain the core concepts and architecture of Rate Limiter.
- Apply Rate Limiter in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Retry
Core concepts, implementation patterns, engineering trade-offs and production considerations for Retry.
- Explain the core concepts and architecture of Retry.
- Apply Retry in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Time Limiter
Core concepts, implementation patterns, engineering trade-offs and production considerations for Time Limiter.
- Explain the core concepts and architecture of Time Limiter.
- Apply Time Limiter in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Resilience4j Concepts
Core concepts, implementation patterns, engineering trade-offs and production considerations for Resilience4j Concepts.
- Explain the core concepts and architecture of Resilience4j Concepts.
- Apply Resilience4j Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Spring Cloud Circuit Breaker
RESILIENCE LAB; Order Service calls Payment Service.; Simulate:; text; Normal: 100 ms; Slow: 6 sec; Failure: HTTP 503; Student configures:; timeout; retry; circuit breaker; and compares behavior.; =============================================
- Explain the core concepts and architecture of Spring Cloud Circuit Breaker.
- Apply Spring Cloud Circuit Breaker in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 147–156: Gateway Pattern, Spring Cloud Gateway, Routing, Authentication, Authorization, CORS, Rate Limiting, Request Filters and 2 more topics.
Gateway Pattern
Architecture:; text; Client; API Gateway; ┌─────────┬───────────┬───────────┐; Identity Orders Products Payments
- Explain the core concepts and architecture of Gateway Pattern.
- Apply Gateway Pattern in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Spring Cloud Gateway
Core concepts, implementation patterns, engineering trade-offs and production considerations for Spring Cloud Gateway.
- Explain the core concepts and architecture of Spring Cloud Gateway.
- Apply Spring Cloud Gateway in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Routing
Core concepts, implementation patterns, engineering trade-offs and production considerations for Routing.
- Explain the core concepts and architecture of Routing.
- Apply Routing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Authentication
Core concepts, implementation patterns, engineering trade-offs and production considerations for Authentication.
- Explain the core concepts and architecture of Authentication.
- Apply Authentication in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Authorization
Core concepts, implementation patterns, engineering trade-offs and production considerations for Authorization.
- Explain the core concepts and architecture of Authorization.
- Apply Authorization in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
CORS
Core concepts, implementation patterns, engineering trade-offs and production considerations for CORS.
- Explain the core concepts and architecture of CORS.
- Apply CORS in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Rate Limiting
Core concepts, implementation patterns, engineering trade-offs and production considerations for Rate Limiting.
- Explain the core concepts and architecture of Rate Limiting.
- Apply Rate Limiting in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Request Filters
Core concepts, implementation patterns, engineering trade-offs and production considerations for Request Filters.
- Explain the core concepts and architecture of Request Filters.
- Apply Request Filters in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Response Filters
Core concepts, implementation patterns, engineering trade-offs and production considerations for Response Filters.
- Explain the core concepts and architecture of Response Filters.
- Apply Response Filters in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Correlation IDs
=============================================
- Explain the core concepts and architecture of Correlation IDs.
- Apply Correlation IDs in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 157–162: Configuration Per Environment, Spring Cloud Config, Git-backed Configuration Concepts, Kubernetes ConfigMaps, Kubernetes Secrets, External Secrets Concepts.
Configuration Per Environment
Core concepts, implementation patterns, engineering trade-offs and production considerations for Configuration Per Environment.
- Explain the core concepts and architecture of Configuration Per Environment.
- Apply Configuration Per Environment in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Spring Cloud Config
Core concepts, implementation patterns, engineering trade-offs and production considerations for Spring Cloud Config.
- Explain the core concepts and architecture of Spring Cloud Config.
- Apply Spring Cloud Config in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Git-backed Configuration Concepts
Core concepts, implementation patterns, engineering trade-offs and production considerations for Git-backed Configuration Concepts.
- Explain the core concepts and architecture of Git-backed Configuration Concepts.
- Apply Git-backed Configuration Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Kubernetes ConfigMaps
Core concepts, implementation patterns, engineering trade-offs and production considerations for Kubernetes ConfigMaps.
- Explain the core concepts and architecture of Kubernetes ConfigMaps.
- Apply Kubernetes ConfigMaps in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Kubernetes Secrets
Core concepts, implementation patterns, engineering trade-offs and production considerations for Kubernetes Secrets.
- Explain the core concepts and architecture of Kubernetes Secrets.
- Apply Kubernetes Secrets in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
External Secrets Concepts
=============================================
- Explain the core concepts and architecture of External Secrets Concepts.
- Apply External Secrets Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 163–166: Service Discovery Concepts, Client-Side Discovery, Server-Side Discovery, Kubernetes-Native Discovery.
Service Discovery Concepts
Core concepts, implementation patterns, engineering trade-offs and production considerations for Service Discovery Concepts.
- Explain the core concepts and architecture of Service Discovery Concepts.
- Apply Service Discovery Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Client-Side Discovery
Core concepts, implementation patterns, engineering trade-offs and production considerations for Client-Side Discovery.
- Explain the core concepts and architecture of Client-Side Discovery.
- Apply Client-Side Discovery in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Server-Side Discovery
Core concepts, implementation patterns, engineering trade-offs and production considerations for Server-Side Discovery.
- Explain the core concepts and architecture of Server-Side Discovery.
- Apply Server-Side Discovery in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Kubernetes-Native Discovery
Spring Cloud continues to actively maintain Kubernetes discovery capabilities, so course design should not frame older registry-based discovery as the only valid approach. ([Spring][2]); =============================================
- Explain the core concepts and architecture of Kubernetes-Native Discovery.
- Apply Kubernetes-Native Discovery in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 167–173: Events, Commands vs Events, Event Producer, Event Consumer, Event Contract, Event Schema Evolution, Eventual Consistency.
Events
Core concepts, implementation patterns, engineering trade-offs and production considerations for Events.
- Explain the core concepts and architecture of Events.
- Apply Events in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Commands vs Events
Command:; text; CreateOrder; Event:; OrderCreated
- Explain the core concepts and architecture of Commands vs Events.
- Apply Commands vs Events in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Event Producer
Core concepts, implementation patterns, engineering trade-offs and production considerations for Event Producer.
- Explain the core concepts and architecture of Event Producer.
- Apply Event Producer in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Event Consumer
Core concepts, implementation patterns, engineering trade-offs and production considerations for Event Consumer.
- Explain the core concepts and architecture of Event Consumer.
- Apply Event Consumer in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Event Contract
Core concepts, implementation patterns, engineering trade-offs and production considerations for Event Contract.
- Explain the core concepts and architecture of Event Contract.
- Apply Event Contract in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Event Schema Evolution
Core concepts, implementation patterns, engineering trade-offs and production considerations for Event Schema Evolution.
- Explain the core concepts and architecture of Event Schema Evolution.
- Apply Event Schema Evolution in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Eventual Consistency
=============================================
- Explain the core concepts and architecture of Eventual Consistency.
- Apply Eventual Consistency in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 174–186: Kafka Architecture, Broker, Topic, Partition, Producer, Consumer, Consumer Group, Offset and 5 more topics.
Kafka Architecture
Core concepts, implementation patterns, engineering trade-offs and production considerations for Kafka Architecture.
- Explain the core concepts and architecture of Kafka Architecture.
- Apply Kafka Architecture in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Broker
Core concepts, implementation patterns, engineering trade-offs and production considerations for Broker.
- Explain the core concepts and architecture of Broker.
- Apply Broker in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Topic
Core concepts, implementation patterns, engineering trade-offs and production considerations for Topic.
- Explain the core concepts and architecture of Topic.
- Apply Topic in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Partition
Core concepts, implementation patterns, engineering trade-offs and production considerations for Partition.
- Explain the core concepts and architecture of Partition.
- Apply Partition in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Producer
Core concepts, implementation patterns, engineering trade-offs and production considerations for Producer.
- Explain the core concepts and architecture of Producer.
- Apply Producer in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Consumer
Core concepts, implementation patterns, engineering trade-offs and production considerations for Consumer.
- Explain the core concepts and architecture of Consumer.
- Apply Consumer in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Consumer Group
Core concepts, implementation patterns, engineering trade-offs and production considerations for Consumer Group.
- Explain the core concepts and architecture of Consumer Group.
- Apply Consumer Group in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Offset
Core concepts, implementation patterns, engineering trade-offs and production considerations for Offset.
- Explain the core concepts and architecture of Offset.
- Apply Offset in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Message Keys
Core concepts, implementation patterns, engineering trade-offs and production considerations for Message Keys.
- Explain the core concepts and architecture of Message Keys.
- Apply Message Keys in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Ordering
Core concepts, implementation patterns, engineering trade-offs and production considerations for Ordering.
- Explain the core concepts and architecture of Ordering.
- Apply Ordering in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Replication Concepts
Core concepts, implementation patterns, engineering trade-offs and production considerations for Replication Concepts.
- Explain the core concepts and architecture of Replication Concepts.
- Apply Replication Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Serialization
Core concepts, implementation patterns, engineering trade-offs and production considerations for Serialization.
- Explain the core concepts and architecture of Serialization.
- Apply Serialization in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Delivery Semantics
=============================================
- Explain the core concepts and architecture of Delivery Semantics.
- Apply Delivery Semantics in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 187–196: KafkaTemplate, @KafkaListener, Consumer Configuration, Producer Configuration, Acknowledgements, Error Handling, Retry, Dead-Letter Topics and 2 more topics.
KafkaTemplate
Core concepts, implementation patterns, engineering trade-offs and production considerations for KafkaTemplate.
- Explain the core concepts and architecture of KafkaTemplate.
- Apply KafkaTemplate in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
@KafkaListener
Core concepts, implementation patterns, engineering trade-offs and production considerations for @KafkaListener.
- Explain the core concepts and architecture of @KafkaListener.
- Apply @KafkaListener in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Consumer Configuration
Core concepts, implementation patterns, engineering trade-offs and production considerations for Consumer Configuration.
- Explain the core concepts and architecture of Consumer Configuration.
- Apply Consumer Configuration in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Producer Configuration
Core concepts, implementation patterns, engineering trade-offs and production considerations for Producer Configuration.
- Explain the core concepts and architecture of Producer Configuration.
- Apply Producer Configuration in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Acknowledgements
Core concepts, implementation patterns, engineering trade-offs and production considerations for Acknowledgements.
- Explain the core concepts and architecture of Acknowledgements.
- Apply Acknowledgements in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Error Handling
Core concepts, implementation patterns, engineering trade-offs and production considerations for Error Handling.
- Explain the core concepts and architecture of Error Handling.
- Apply Error Handling in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Retry
Core concepts, implementation patterns, engineering trade-offs and production considerations for Retry.
- Explain the core concepts and architecture of Retry.
- Apply Retry in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Dead-Letter Topics
Spring Kafka currently supports non-blocking retry-topic infrastructure and DLT patterns through @RetryableTopic and configuration APIs. ([Home][6])
- Explain the core concepts and architecture of Dead-Letter Topics.
- Apply Dead-Letter Topics in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Retry Topic Design
Modern Spring Kafka supports retry topics and delayed retry flows.; Spring Kafka 4.1 changed retry-topic defaults to reuse a single retry topic for identical fixed-delay intervals unless configured otherwise. ([Home][7])
- Explain the core concepts and architecture of Retry Topic Design.
- Apply Retry Topic Design in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Ordering vs Retry Tradeoff
Important:; non-blocking retry strategies may change ordering behavior.; Spring’s documentation explicitly warns that retry-topic forwarding can lose normal topic ordering guarantees. ([Home][8]); KAFKA LAB; Architecture:; text; Order Service; order-created; Inventory Consumer; Payment Consumer; Notification Consumer; Test:; duplicate event; poison message; consumer failure; retry; DLT; =============================================
- Explain the core concepts and architecture of Ordering vs Retry Tradeoff.
- Apply Ordering vs Retry Tradeoff in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 197–202: Duplicate Requests, Duplicate Events, Idempotency Key, Idempotent Consumer, Unique Constraints, Processed Event Registry.
Duplicate Requests
Core concepts, implementation patterns, engineering trade-offs and production considerations for Duplicate Requests.
- Explain the core concepts and architecture of Duplicate Requests.
- Apply Duplicate Requests in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Duplicate Events
Core concepts, implementation patterns, engineering trade-offs and production considerations for Duplicate Events.
- Explain the core concepts and architecture of Duplicate Events.
- Apply Duplicate Events in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Idempotency Key
Core concepts, implementation patterns, engineering trade-offs and production considerations for Idempotency Key.
- Explain the core concepts and architecture of Idempotency Key.
- Apply Idempotency Key in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Idempotent Consumer
Core concepts, implementation patterns, engineering trade-offs and production considerations for Idempotent Consumer.
- Explain the core concepts and architecture of Idempotent Consumer.
- Apply Idempotent Consumer in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Unique Constraints
Core concepts, implementation patterns, engineering trade-offs and production considerations for Unique Constraints.
- Explain the core concepts and architecture of Unique Constraints.
- Apply Unique Constraints in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Processed Event Registry
LAB; Message:; text; ORDER_PAID; delivered twice.; Expected:; Only one business update.; =============================================
- Explain the core concepts and architecture of Processed Event Registry.
- Apply Processed Event Registry in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 203–208: Why @Transactional Doesn't Span Microservices, Two-Phase Commit Concepts, Saga, Choreography, Orchestration, Compensation.
Why @Transactional Doesn't Span Microservices
Core concepts, implementation patterns, engineering trade-offs and production considerations for Why @Transactional Doesn't Span Microservices.
- Explain the core concepts and architecture of Why @Transactional Doesn't Span Microservices.
- Apply Why @Transactional Doesn't Span Microservices in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Two-Phase Commit Concepts
Core concepts, implementation patterns, engineering trade-offs and production considerations for Two-Phase Commit Concepts.
- Explain the core concepts and architecture of Two-Phase Commit Concepts.
- Apply Two-Phase Commit Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Saga
Core concepts, implementation patterns, engineering trade-offs and production considerations for Saga.
- Explain the core concepts and architecture of Saga.
- Apply Saga in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Choreography
Core concepts, implementation patterns, engineering trade-offs and production considerations for Choreography.
- Explain the core concepts and architecture of Choreography.
- Apply Choreography in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Orchestration
Core concepts, implementation patterns, engineering trade-offs and production considerations for Orchestration.
- Explain the core concepts and architecture of Orchestration.
- Apply Orchestration in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Compensation
=============================================
- Explain the core concepts and architecture of Compensation.
- Apply Compensation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 209–213: Dual Write Problem, Transactional Outbox, Inbox Pattern, Deduplication, CDC-based Outbox Concepts.
Dual Write Problem
Bad:; text; saveOrder(); publishKafkaEvent(); Potential result:; DB success + Kafka failure.
- Explain the core concepts and architecture of Dual Write Problem.
- Apply Dual Write Problem in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Transactional Outbox
Architecture:; text; DB Transaction; ├── Orders; └── Outbox; Publisher; Kafka
- Explain the core concepts and architecture of Transactional Outbox.
- Apply Transactional Outbox in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Inbox Pattern
Core concepts, implementation patterns, engineering trade-offs and production considerations for Inbox Pattern.
- Explain the core concepts and architecture of Inbox Pattern.
- Apply Inbox Pattern in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Deduplication
Core concepts, implementation patterns, engineering trade-offs and production considerations for Deduplication.
- Explain the core concepts and architecture of Deduplication.
- Apply Deduplication in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
CDC-based Outbox Concepts
PROJECT 3; Reliable Order Processing Platform; Implement:; Saga; outbox; Kafka; idempotency; compensation; =============================================
- Explain the core concepts and architecture of CDC-based Outbox Concepts.
- Apply CDC-based Outbox Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 214–222: Cache Fundamentals, Cache-Aside, TTL, Eviction, Cache Invalidation, Cache Stampede, Distributed Lock Concepts, Rate Limiting and 1 more topics.
Cache Fundamentals
Core concepts, implementation patterns, engineering trade-offs and production considerations for Cache Fundamentals.
- Explain the core concepts and architecture of Cache Fundamentals.
- Apply Cache Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Cache-Aside
Core concepts, implementation patterns, engineering trade-offs and production considerations for Cache-Aside.
- Explain the core concepts and architecture of Cache-Aside.
- Apply Cache-Aside in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
TTL
Core concepts, implementation patterns, engineering trade-offs and production considerations for TTL.
- Explain the core concepts and architecture of TTL.
- Apply TTL in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Eviction
Core concepts, implementation patterns, engineering trade-offs and production considerations for Eviction.
- Explain the core concepts and architecture of Eviction.
- Apply Eviction in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Cache Invalidation
Core concepts, implementation patterns, engineering trade-offs and production considerations for Cache Invalidation.
- Explain the core concepts and architecture of Cache Invalidation.
- Apply Cache Invalidation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Cache Stampede
Core concepts, implementation patterns, engineering trade-offs and production considerations for Cache Stampede.
- Explain the core concepts and architecture of Cache Stampede.
- Apply Cache Stampede in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Distributed Lock Concepts
Core concepts, implementation patterns, engineering trade-offs and production considerations for Distributed Lock Concepts.
- Explain the core concepts and architecture of Distributed Lock Concepts.
- Apply Distributed Lock Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Rate Limiting
Core concepts, implementation patterns, engineering trade-offs and production considerations for Rate Limiting.
- Explain the core concepts and architecture of Rate Limiting.
- Apply Rate Limiting in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Redis-backed Session Concepts
=============================================
- Explain the core concepts and architecture of Redis-backed Session Concepts.
- Apply Redis-backed Session Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 223–227: Async Workflows, Scheduled Jobs, Job State, Retry, Idempotency.
Async Workflows
Core concepts, implementation patterns, engineering trade-offs and production considerations for Async Workflows.
- Explain the core concepts and architecture of Async Workflows.
- Apply Async Workflows in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Scheduled Jobs
Core concepts, implementation patterns, engineering trade-offs and production considerations for Scheduled Jobs.
- Explain the core concepts and architecture of Scheduled Jobs.
- Apply Scheduled Jobs in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Job State
text; QUEUED; RUNNING; COMPLETED; FAILED
- Explain the core concepts and architecture of Job State.
- Apply Job State in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Retry
Core concepts, implementation patterns, engineering trade-offs and production considerations for Retry.
- Explain the core concepts and architecture of Retry.
- Apply Retry in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Idempotency
=============================================
- Explain the core concepts and architecture of Idempotency.
- Apply Idempotency in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 228–238: Logs, Metrics, Traces, Structured Logging, Correlation IDs, Spring Boot Actuator, Micrometer Concepts, Prometheus Concepts and 3 more topics.
Logs
Core concepts, implementation patterns, engineering trade-offs and production considerations for Logs.
- Explain the core concepts and architecture of Logs.
- Apply Logs in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Metrics
Core concepts, implementation patterns, engineering trade-offs and production considerations for Metrics.
- Explain the core concepts and architecture of Metrics.
- Apply Metrics in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Traces
The three primary signals.
- Explain the core concepts and architecture of Traces.
- Apply Traces in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Structured Logging
Example:; json; {; "traceId": "...",; "service": "order-service",; "event": "ORDER_CREATED"; }
- Explain the core concepts and architecture of Structured Logging.
- Apply Structured Logging in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Correlation IDs
Core concepts, implementation patterns, engineering trade-offs and production considerations for Correlation IDs.
- Explain the core concepts and architecture of Correlation IDs.
- Apply Correlation IDs in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Spring Boot Actuator
Track:; health; metrics; environment exposure rules
- Explain the core concepts and architecture of Spring Boot Actuator.
- Apply Spring Boot Actuator in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Micrometer Concepts
Core concepts, implementation patterns, engineering trade-offs and production considerations for Micrometer Concepts.
- Explain the core concepts and architecture of Micrometer Concepts.
- Apply Micrometer Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Prometheus Concepts
Core concepts, implementation patterns, engineering trade-offs and production considerations for Prometheus Concepts.
- Explain the core concepts and architecture of Prometheus Concepts.
- Apply Prometheus Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Grafana Concepts
Core concepts, implementation patterns, engineering trade-offs and production considerations for Grafana Concepts.
- Explain the core concepts and architecture of Grafana Concepts.
- Apply Grafana Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
OpenTelemetry
Core concepts, implementation patterns, engineering trade-offs and production considerations for OpenTelemetry.
- Explain the core concepts and architecture of OpenTelemetry.
- Apply OpenTelemetry in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Distributed Tracing
Trace:; text; Gateway; Order Service; Inventory; Database; =============================================
- Explain the core concepts and architecture of Distributed Tracing.
- Apply Distributed Tracing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 239–242: Liveness, Readiness, Startup Health, Dependency Health.
Liveness
Core concepts, implementation patterns, engineering trade-offs and production considerations for Liveness.
- Explain the core concepts and architecture of Liveness.
- Apply Liveness in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Readiness
Core concepts, implementation patterns, engineering trade-offs and production considerations for Readiness.
- Explain the core concepts and architecture of Readiness.
- Apply Readiness in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Startup Health
Core concepts, implementation patterns, engineering trade-offs and production considerations for Startup Health.
- Explain the core concepts and architecture of Startup Health.
- Apply Startup Health in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Dependency Health
Avoid declaring a service unhealthy merely because a non-critical dependency is unavailable.; =============================================
- Explain the core concepts and architecture of Dependency Health.
- Apply Dependency Health in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 243–252: Testing Pyramid, JUnit, Mockito, Spring Boot Tests, MockMvc, Repository Tests, Integration Tests, Testcontainers and 2 more topics.
Testing Pyramid
Core concepts, implementation patterns, engineering trade-offs and production considerations for Testing Pyramid.
- Explain the core concepts and architecture of Testing Pyramid.
- Apply Testing Pyramid in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
JUnit
Core concepts, implementation patterns, engineering trade-offs and production considerations for JUnit.
- Explain the core concepts and architecture of JUnit.
- Apply JUnit in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Mockito
Core concepts, implementation patterns, engineering trade-offs and production considerations for Mockito.
- Explain the core concepts and architecture of Mockito.
- Apply Mockito in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Spring Boot Tests
Core concepts, implementation patterns, engineering trade-offs and production considerations for Spring Boot Tests.
- Explain the core concepts and architecture of Spring Boot Tests.
- Apply Spring Boot Tests in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
MockMvc
Core concepts, implementation patterns, engineering trade-offs and production considerations for MockMvc.
- Explain the core concepts and architecture of MockMvc.
- Apply MockMvc in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Repository Tests
Core concepts, implementation patterns, engineering trade-offs and production considerations for Repository Tests.
- Explain the core concepts and architecture of Repository Tests.
- Apply Repository Tests in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Integration Tests
Core concepts, implementation patterns, engineering trade-offs and production considerations for Integration Tests.
- Explain the core concepts and architecture of Integration Tests.
- Apply Integration Tests in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Testcontainers
Core concepts, implementation patterns, engineering trade-offs and production considerations for Testcontainers.
- Explain the core concepts and architecture of Testcontainers.
- Apply Testcontainers in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Kafka Integration Tests
Core concepts, implementation patterns, engineering trade-offs and production considerations for Kafka Integration Tests.
- Explain the core concepts and architecture of Kafka Integration Tests.
- Apply Kafka Integration Tests in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Security Tests
=============================================
- Explain the core concepts and architecture of Security Tests.
- Apply Security Tests in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 253–256: API Contracts, Consumer-Driven Contracts, Spring Cloud Contract Concepts, Breaking Changes.
API Contracts
Core concepts, implementation patterns, engineering trade-offs and production considerations for API Contracts.
- Explain the core concepts and architecture of API Contracts.
- Apply API Contracts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Consumer-Driven Contracts
Core concepts, implementation patterns, engineering trade-offs and production considerations for Consumer-Driven Contracts.
- Explain the core concepts and architecture of Consumer-Driven Contracts.
- Apply Consumer-Driven Contracts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Spring Cloud Contract Concepts
Core concepts, implementation patterns, engineering trade-offs and production considerations for Spring Cloud Contract Concepts.
- Explain the core concepts and architecture of Spring Cloud Contract Concepts.
- Apply Spring Cloud Contract Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Breaking Changes
=============================================
- Explain the core concepts and architecture of Breaking Changes.
- Apply Breaking Changes in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Applied engineering phase: Microservice Test Strategy.
Microservice Test Strategy
=============================================; Students must distinguish:; Unit Test; One class.; Integration Test; Service + DB.; Contract Test; Producer/consumer compatibility.; End-to-End Test; Multiple services.; Avoid relying only on slow full-system tests.
- Explain the core concepts and architecture of Microservice Test Strategy.
- Apply Microservice Test Strategy in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 257–264: P50 / P95 / P99, Throughput, CPU, Memory, Database, Network, Thread Pools, Connection Pools.
P50 / P95 / P99
Core concepts, implementation patterns, engineering trade-offs and production considerations for P50 / P95 / P99.
- Explain the core concepts and architecture of P50 / P95 / P99.
- Apply P50 / P95 / P99 in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Throughput
Core concepts, implementation patterns, engineering trade-offs and production considerations for Throughput.
- Explain the core concepts and architecture of Throughput.
- Apply Throughput in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
CPU
Core concepts, implementation patterns, engineering trade-offs and production considerations for CPU.
- Explain the core concepts and architecture of CPU.
- Apply CPU in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Memory
Core concepts, implementation patterns, engineering trade-offs and production considerations for Memory.
- Explain the core concepts and architecture of Memory.
- Apply Memory in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Database
Core concepts, implementation patterns, engineering trade-offs and production considerations for Database.
- Explain the core concepts and architecture of Database.
- Apply Database in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Network
Core concepts, implementation patterns, engineering trade-offs and production considerations for Network.
- Explain the core concepts and architecture of Network.
- Apply Network in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Thread Pools
Core concepts, implementation patterns, engineering trade-offs and production considerations for Thread Pools.
- Explain the core concepts and architecture of Thread Pools.
- Apply Thread Pools in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Connection Pools
=============================================
- Explain the core concepts and architecture of Connection Pools.
- Apply Connection Pools in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 265–271: Indexes, Slow Queries, Explain Plan, Connection Pool Exhaustion, N+1, Batch Operations, Keyset Pagination Concepts.
Indexes
Core concepts, implementation patterns, engineering trade-offs and production considerations for Indexes.
- Explain the core concepts and architecture of Indexes.
- Apply Indexes in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Slow Queries
Core concepts, implementation patterns, engineering trade-offs and production considerations for Slow Queries.
- Explain the core concepts and architecture of Slow Queries.
- Apply Slow Queries in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Explain Plan
Core concepts, implementation patterns, engineering trade-offs and production considerations for Explain Plan.
- Explain the core concepts and architecture of Explain Plan.
- Apply Explain Plan in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Connection Pool Exhaustion
Core concepts, implementation patterns, engineering trade-offs and production considerations for Connection Pool Exhaustion.
- Explain the core concepts and architecture of Connection Pool Exhaustion.
- Apply Connection Pool Exhaustion in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
N+1
Core concepts, implementation patterns, engineering trade-offs and production considerations for N+1.
- Explain the core concepts and architecture of N+1.
- Apply N+1 in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Batch Operations
Core concepts, implementation patterns, engineering trade-offs and production considerations for Batch Operations.
- Explain the core concepts and architecture of Batch Operations.
- Apply Batch Operations in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Keyset Pagination Concepts
=============================================
- Explain the core concepts and architecture of Keyset Pagination Concepts.
- Apply Keyset Pagination Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 272–277: Heap, Garbage Collection, Thread Dumps, Heap Dumps, CPU Profiling, Memory Leak Investigation.
Heap
Core concepts, implementation patterns, engineering trade-offs and production considerations for Heap.
- Explain the core concepts and architecture of Heap.
- Apply Heap in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Garbage Collection
Core concepts, implementation patterns, engineering trade-offs and production considerations for Garbage Collection.
- Explain the core concepts and architecture of Garbage Collection.
- Apply Garbage Collection in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Thread Dumps
Core concepts, implementation patterns, engineering trade-offs and production considerations for Thread Dumps.
- Explain the core concepts and architecture of Thread Dumps.
- Apply Thread Dumps in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Heap Dumps
Core concepts, implementation patterns, engineering trade-offs and production considerations for Heap Dumps.
- Explain the core concepts and architecture of Heap Dumps.
- Apply Heap Dumps in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
CPU Profiling
Core concepts, implementation patterns, engineering trade-offs and production considerations for CPU Profiling.
- Explain the core concepts and architecture of CPU Profiling.
- Apply CPU Profiling in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Memory Leak Investigation
=============================================
- Explain the core concepts and architecture of Memory Leak Investigation.
- Apply Memory Leak Investigation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 278–285: Images, Containers, Dockerfile, Multi-Stage Build, Networking, Volumes, Environment Variables, Docker Compose.
Images
Core concepts, implementation patterns, engineering trade-offs and production considerations for Images.
- Explain the core concepts and architecture of Images.
- Apply Images in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Containers
Core concepts, implementation patterns, engineering trade-offs and production considerations for Containers.
- Explain the core concepts and architecture of Containers.
- Apply Containers in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Dockerfile
Core concepts, implementation patterns, engineering trade-offs and production considerations for Dockerfile.
- Explain the core concepts and architecture of Dockerfile.
- Apply Dockerfile in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Multi-Stage Build
Core concepts, implementation patterns, engineering trade-offs and production considerations for Multi-Stage Build.
- Explain the core concepts and architecture of Multi-Stage Build.
- Apply Multi-Stage Build in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Networking
Core concepts, implementation patterns, engineering trade-offs and production considerations for Networking.
- Explain the core concepts and architecture of Networking.
- Apply Networking in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Volumes
Core concepts, implementation patterns, engineering trade-offs and production considerations for Volumes.
- Explain the core concepts and architecture of Volumes.
- Apply Volumes in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Environment Variables
Core concepts, implementation patterns, engineering trade-offs and production considerations for Environment Variables.
- Explain the core concepts and architecture of Environment Variables.
- Apply Environment Variables in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Docker Compose
Local environment:; text; Gateway; Order Service; Inventory Service; Payment Service; PostgreSQL; Kafka; Redis; =============================================
- Explain the core concepts and architecture of Docker Compose.
- Apply Docker Compose in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 286–298: Kubernetes Architecture, Pods, Deployments, Services, ConfigMaps, Secrets, Liveness Probes, Readiness Probes and 5 more topics.
Kubernetes Architecture
Core concepts, implementation patterns, engineering trade-offs and production considerations for Kubernetes Architecture.
- Explain the core concepts and architecture of Kubernetes Architecture.
- Apply Kubernetes Architecture in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Pods
Core concepts, implementation patterns, engineering trade-offs and production considerations for Pods.
- Explain the core concepts and architecture of Pods.
- Apply Pods in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Deployments
Core concepts, implementation patterns, engineering trade-offs and production considerations for Deployments.
- Explain the core concepts and architecture of Deployments.
- Apply Deployments in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Services
Core concepts, implementation patterns, engineering trade-offs and production considerations for Services.
- Explain the core concepts and architecture of Services.
- Apply Services in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
ConfigMaps
Core concepts, implementation patterns, engineering trade-offs and production considerations for ConfigMaps.
- Explain the core concepts and architecture of ConfigMaps.
- Apply ConfigMaps in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Secrets
Core concepts, implementation patterns, engineering trade-offs and production considerations for Secrets.
- Explain the core concepts and architecture of Secrets.
- Apply Secrets in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Liveness Probes
Core concepts, implementation patterns, engineering trade-offs and production considerations for Liveness Probes.
- Explain the core concepts and architecture of Liveness Probes.
- Apply Liveness Probes in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Readiness Probes
Core concepts, implementation patterns, engineering trade-offs and production considerations for Readiness Probes.
- Explain the core concepts and architecture of Readiness Probes.
- Apply Readiness Probes in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Requests / Limits
Core concepts, implementation patterns, engineering trade-offs and production considerations for Requests / Limits.
- Explain the core concepts and architecture of Requests / Limits.
- Apply Requests / Limits in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Horizontal Scaling
Core concepts, implementation patterns, engineering trade-offs and production considerations for Horizontal Scaling.
- Explain the core concepts and architecture of Horizontal Scaling.
- Apply Horizontal Scaling in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Rolling Updates
Core concepts, implementation patterns, engineering trade-offs and production considerations for Rolling Updates.
- Explain the core concepts and architecture of Rolling Updates.
- Apply Rolling Updates in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Ingress / Gateway Concepts
Core concepts, implementation patterns, engineering trade-offs and production considerations for Ingress / Gateway Concepts.
- Explain the core concepts and architecture of Ingress / Gateway Concepts.
- Apply Ingress / Gateway Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Kubernetes Service Discovery
=============================================
- Explain the core concepts and architecture of Kubernetes Service Discovery.
- Apply Kubernetes Service Discovery in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 299–310: IAM, EC2, S3, RDS, Networking, Load Balancing, Auto Scaling, CloudWatch and 4 more topics.
IAM
Core concepts, implementation patterns, engineering trade-offs and production considerations for IAM.
- Explain the core concepts and architecture of IAM.
- Apply IAM in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
EC2
Core concepts, implementation patterns, engineering trade-offs and production considerations for EC2.
- Explain the core concepts and architecture of EC2.
- Apply EC2 in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
S3
Core concepts, implementation patterns, engineering trade-offs and production considerations for S3.
- Explain the core concepts and architecture of S3.
- Apply S3 in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
RDS
Core concepts, implementation patterns, engineering trade-offs and production considerations for RDS.
- Explain the core concepts and architecture of RDS.
- Apply RDS in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Networking
Core concepts, implementation patterns, engineering trade-offs and production considerations for Networking.
- Explain the core concepts and architecture of Networking.
- Apply Networking in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Load Balancing
Core concepts, implementation patterns, engineering trade-offs and production considerations for Load Balancing.
- Explain the core concepts and architecture of Load Balancing.
- Apply Load Balancing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Auto Scaling
Core concepts, implementation patterns, engineering trade-offs and production considerations for Auto Scaling.
- Explain the core concepts and architecture of Auto Scaling.
- Apply Auto Scaling in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
CloudWatch
Core concepts, implementation patterns, engineering trade-offs and production considerations for CloudWatch.
- Explain the core concepts and architecture of CloudWatch.
- Apply CloudWatch in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Secrets Management
Core concepts, implementation patterns, engineering trade-offs and production considerations for Secrets Management.
- Explain the core concepts and architecture of Secrets Management.
- Apply Secrets Management in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Container Deployment
Core concepts, implementation patterns, engineering trade-offs and production considerations for Container Deployment.
- Explain the core concepts and architecture of Container Deployment.
- Apply Container Deployment in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
EKS Awareness
Core concepts, implementation patterns, engineering trade-offs and production considerations for EKS Awareness.
- Explain the core concepts and architecture of EKS Awareness.
- Apply EKS Awareness in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Managed Kafka Awareness
=============================================
- Explain the core concepts and architecture of Managed Kafka Awareness.
- Apply Managed Kafka Awareness in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 311–317: Continuous Integration, Continuous Delivery, GitHub Actions, Jenkins Concepts, Artifact Repository, Environment Promotion, Rollback.
Continuous Integration
Pipeline:; text; Commit; Compile; Unit Test; Integration Test; Security Scan; Image Build
- Explain the core concepts and architecture of Continuous Integration.
- Apply Continuous Integration in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Continuous Delivery
text; Image; DEV; TEST; STAGING; PRODUCTION
- Explain the core concepts and architecture of Continuous Delivery.
- Apply Continuous Delivery in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
GitHub Actions
Core concepts, implementation patterns, engineering trade-offs and production considerations for GitHub Actions.
- Explain the core concepts and architecture of GitHub Actions.
- Apply GitHub Actions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Jenkins Concepts
Core concepts, implementation patterns, engineering trade-offs and production considerations for Jenkins Concepts.
- Explain the core concepts and architecture of Jenkins Concepts.
- Apply Jenkins Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Artifact Repository
Core concepts, implementation patterns, engineering trade-offs and production considerations for Artifact Repository.
- Explain the core concepts and architecture of Artifact Repository.
- Apply Artifact Repository in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Environment Promotion
Core concepts, implementation patterns, engineering trade-offs and production considerations for Environment Promotion.
- Explain the core concepts and architecture of Environment Promotion.
- Apply Environment Promotion in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Rollback
=============================================
- Explain the core concepts and architecture of Rollback.
- Apply Rollback in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 318–321: Rolling, Blue/Green, Canary, Feature Flags.
Rolling
Core concepts, implementation patterns, engineering trade-offs and production considerations for Rolling.
- Explain the core concepts and architecture of Rolling.
- Apply Rolling in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Blue/Green
Core concepts, implementation patterns, engineering trade-offs and production considerations for Blue/Green.
- Explain the core concepts and architecture of Blue/Green.
- Apply Blue/Green in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Canary
Core concepts, implementation patterns, engineering trade-offs and production considerations for Canary.
- Explain the core concepts and architecture of Canary.
- Apply Canary in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Feature Flags
=============================================
- Explain the core concepts and architecture of Feature Flags.
- Apply Feature Flags in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 322–328: Zero Trust Concepts, Service Identity, mTLS Concepts, Token Propagation, Service-to-Service Authorization, Secrets, Least Privilege.
Zero Trust Concepts
Never assume internal traffic is automatically trusted.
- Explain the core concepts and architecture of Zero Trust Concepts.
- Apply Zero Trust Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Service Identity
Core concepts, implementation patterns, engineering trade-offs and production considerations for Service Identity.
- Explain the core concepts and architecture of Service Identity.
- Apply Service Identity in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
mTLS Concepts
Core concepts, implementation patterns, engineering trade-offs and production considerations for mTLS Concepts.
- Explain the core concepts and architecture of mTLS Concepts.
- Apply mTLS Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Token Propagation
Core concepts, implementation patterns, engineering trade-offs and production considerations for Token Propagation.
- Explain the core concepts and architecture of Token Propagation.
- Apply Token Propagation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Service-to-Service Authorization
Core concepts, implementation patterns, engineering trade-offs and production considerations for Service-to-Service Authorization.
- Explain the core concepts and architecture of Service-to-Service Authorization.
- Apply Service-to-Service Authorization in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Secrets
Core concepts, implementation patterns, engineering trade-offs and production considerations for Secrets.
- Explain the core concepts and architecture of Secrets.
- Apply Secrets in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Least Privilege
=============================================
- Explain the core concepts and architecture of Least Privilege.
- Apply Least Privilege in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 329–333: Why Rate Limit?, Token Bucket, Fixed Window, Sliding Window Concepts, Gateway-Level Rate Limiting.
Why Rate Limit?
Protect against:; abuse; accidental overload; denial-of-service patterns
- Explain the core concepts and architecture of Why Rate Limit?.
- Apply Why Rate Limit? in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Token Bucket
Core concepts, implementation patterns, engineering trade-offs and production considerations for Token Bucket.
- Explain the core concepts and architecture of Token Bucket.
- Apply Token Bucket in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Fixed Window
Core concepts, implementation patterns, engineering trade-offs and production considerations for Fixed Window.
- Explain the core concepts and architecture of Fixed Window.
- Apply Fixed Window in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Sliding Window Concepts
Core concepts, implementation patterns, engineering trade-offs and production considerations for Sliding Window Concepts.
- Explain the core concepts and architecture of Sliding Window Concepts.
- Apply Sliding Window Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Gateway-Level Rate Limiting
=============================================
- Explain the core concepts and architecture of Gateway-Level Rate Limiting.
- Apply Gateway-Level Rate Limiting in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 334–344: CAP Theorem, Consistency, Availability, Partition Tolerance, Eventual Consistency, Strong Consistency, Quorum Concepts, Clock / Time Challenges and 3 more topics.
CAP Theorem
Core concepts, implementation patterns, engineering trade-offs and production considerations for CAP Theorem.
- Explain the core concepts and architecture of CAP Theorem.
- Apply CAP Theorem in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Consistency
Core concepts, implementation patterns, engineering trade-offs and production considerations for Consistency.
- Explain the core concepts and architecture of Consistency.
- Apply Consistency in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Availability
Core concepts, implementation patterns, engineering trade-offs and production considerations for Availability.
- Explain the core concepts and architecture of Availability.
- Apply Availability in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Partition Tolerance
Core concepts, implementation patterns, engineering trade-offs and production considerations for Partition Tolerance.
- Explain the core concepts and architecture of Partition Tolerance.
- Apply Partition Tolerance in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Eventual Consistency
Core concepts, implementation patterns, engineering trade-offs and production considerations for Eventual Consistency.
- Explain the core concepts and architecture of Eventual Consistency.
- Apply Eventual Consistency in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Strong Consistency
Core concepts, implementation patterns, engineering trade-offs and production considerations for Strong Consistency.
- Explain the core concepts and architecture of Strong Consistency.
- Apply Strong Consistency in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Quorum Concepts
Core concepts, implementation patterns, engineering trade-offs and production considerations for Quorum Concepts.
- Explain the core concepts and architecture of Quorum Concepts.
- Apply Quorum Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Clock / Time Challenges
Core concepts, implementation patterns, engineering trade-offs and production considerations for Clock / Time Challenges.
- Explain the core concepts and architecture of Clock / Time Challenges.
- Apply Clock / Time Challenges in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Duplicate Messages
Core concepts, implementation patterns, engineering trade-offs and production considerations for Duplicate Messages.
- Explain the core concepts and architecture of Duplicate Messages.
- Apply Duplicate Messages in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Ordering
Core concepts, implementation patterns, engineering trade-offs and production considerations for Ordering.
- Explain the core concepts and architecture of Ordering.
- Apply Ordering in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Backpressure
=============================================
- Explain the core concepts and architecture of Backpressure.
- Apply Backpressure in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Applied engineering phase: Microservices Anti-Patterns.
Microservices Anti-Patterns
=============================================; Teach explicitly:; Distributed Monolith; Services cannot deploy independently.; Shared Database; Every service edits same tables.; Chatty Services; One request triggers dozens of sequential calls.; Nano Services; Services are too small to provide autonomy.; Shared Domain Model; All services share internal entities.; Retry Storm; Every service retries failed requests simultaneously.; Cascading Failure; Failure spreads across services.; Event Soup; Poorly designed events with no ownership.
- Explain the core concepts and architecture of Microservices Anti-Patterns.
- Apply Microservices Anti-Patterns in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Applied engineering phase: Microservice System Design.
Microservice System Design
=============================================; System-design framework:; Requirements; Service Boundaries; APIs; Database Ownership; Sync Communication; Async Communication; Consistency; Security; Resilience; Observability; Deployment; Scaling
- Explain the core concepts and architecture of Microservice System Design.
- Apply Microservice System Design in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Applied engineering phase: Design E-Commerce Microservices.
Design E-Commerce Microservices
=============================================; Services:; text; Identity; Customer; Catalog; Inventory; Cart; Order; Payment; Notification; Discuss:; database ownership; Kafka events; Saga; cache; gateway; observability
- Explain the core concepts and architecture of Design E-Commerce Microservices.
- Apply Design E-Commerce Microservices in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Applied engineering phase: Design Payment Platform.
Design Payment Platform
=============================================; Cover:; Idempotency; security; transaction state; reconciliation; audit; retry; async notifications
- Explain the core concepts and architecture of Design Payment Platform.
- Apply Design Payment Platform in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Applied engineering phase: Design Booking Platform.
Design Booking Platform
=============================================; Problem:; Only one seat remains.; Two customers book simultaneously.; Discuss:; locking; reservation; expiration; consistency; payment coordination
- Explain the core concepts and architecture of Design Booking Platform.
- Apply Design Booking Platform in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Applied engineering phase: Production Incident Engineering.
Production Incident Engineering
=============================================; Scenario 1 — Service Timeout; Order Service → Payment Service takes 12 seconds.; Investigate:; downstream issue; timeout; connection pool; retry behavior; Scenario 2 — Cascading Failure; Payment fails.; Order retries.; Gateway retries.; Client retries.; Traffic multiplies dramatically.; Expected:; avoid uncontrolled retry amplification; circuit breaking; appropriate retry ownership; Scenario 3 — Kafka Consumer Lag; Lag:; text; 1,200,000; consumer throughput; downstream DB; errors; partition count; rebalancing; Scenario 4 — Duplicate Order; User retry created two orders.; Idempotency.; Scenario 5 — Partial Saga Failure; Order created.; Inventory reserved.; Payment failed.; Compensation workflow.; Scenario 6 — Database Pool Exhausted; Symptoms:; CPU normal; DB CPU moderate; application waiting threads high; pool 100%; long transactions; slow SQL; connection leaks; Scenario 7 — Memory Growth; One service consumes increasingly more memory.; cache; retained objects; thread-local state; heap dump; PRODUCTION INCIDENT SIMULATOR; MockAttempt should create interactive incidents.; Example:; Checkout Failure; Metrics; Error Rate:; 27%; P95:; 7.8 seconds; Order CPU:; 34%; DB Connection Pool:; 100%; Kafka Lag:; Normal; Student chooses investigation path.; System provides:; logs; traces; metrics; SQL information; Then student submits:; Root Cause; Immediate Mitigation; Permanent Fix; Prevention
- Explain the core concepts and architecture of Production Incident Engineering.
- Apply Production Incident Engineering in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
26 Hands-On Production Capstones & Microservices
Build, deploy, and showcase real-world enterprise architectures on GitHub to prove your production engineering readiness:
Enterprise E-Commerce Microservices & Event-Driven Streaming Platform
Architected with Spring Boot 3.3, Apache Kafka event streams, Redis distributed caching, React 19 UI, and PostgreSQL. Features distributed ACID transaction sagas, payment webhook handling, dynamic inventory locking, and Dockerized Kubernetes deployment.
High-Throughput Banking & Core Transaction Engine
Concurrent multithreaded financial transaction ledger with ACID compliance, optimistic row locking, idempotent payment endpoints, and audit logging.
Real-Time Logistics & Fleet Tracking Service
Bi-directional live vehicle telemetry dashboard processing 10,000+ geo-coordinate events/sec with live map rendering and ETA calculations.
Multi-Tenant SaaS Subscription & Webhook Gateway
Multi-tenant automated billing engine with webhook signature verification, dynamic token bucket rate-limiting, and tenant data isolation schemas.
Distributed URL Shortener & Analytics System (Bitly Scale)
Low-latency URL redirection engine with distributed ID generation (Snowflake), sub-5ms Redis caching, and real-time click analytics.
Automated Cloud DevOps CI/CD Pipeline on AWS
Production containerization pipeline with automated testing, sonar code quality gates, container image vulnerability scanning, and zero-downtime rolling deploys.
AI-Powered Code Reviewer & Assessment Engine
Automated coding interview evaluator that parses Java AST trees, detects algorithmic time complexity, and simulates 1-on-1 voice technical interview feedback.
MockAttempt Academy vs. Traditional Bootcamps & Self-Study
Transparent side-by-side comparison of daily schedule, duration, curriculum, and placement support:
| Feature & Deliverables | MockAttempt Fast-Track Track | Expensive Bootcamps | Self-Study / YouTube |
|---|---|---|---|
| Live Weekend Schedule | Sat & Sun (4 Hours / Day) | 1 - 1.5 Hours / Day | Self-Paced / Inconsistent |
| Duration to Placement Readiness | 4 Months (16 Weeks Weekend) | 6 - 9 Months | 12+ Months (Uncertain) |
| Candidate Placement Guarantee | Unlimited Drives until Placed (or Full Refund) | Limited to 3-6 Months only | None (Apply blindly) |
| Topic Mock Tests & AI Interviews | Integrated for Every Topic (580+ Rounds) | End of Course Only | None |
| Tuition Fee | ₹49,999 ₹98,999 | ₹1,20,000 - ₹2,50,000 | Free (No Mentorship/Jobs) |
Institutional Course Assurances & 100% Placement Policy
100% Job Placement Guarantee
Our placement team arranges unlimited corporate interview drives across 1,050+ hiring partners until you receive an official offer letter. If unplaced, 100% of your tuition fee is refunded.
1-on-1 Expert Faculty Mentorship
Every cohort is taught live by seasoned lead architects from Tier-1 product companies with daily live coding and supervised code reviews.
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