Here’s your structured 365-day learning roadmap broken down clearly:
Each day will be a mix of:
✅ Java + DSA (Problem Solving & Optimized Coding)
✅ SQL/Database Optimization (Indexes, Query Tuning)
✅ LLD + Microservices (Design, Scalability)
✅ DevOps & System Design (Cloud, CI/CD, Kubernetes)
✅ Tough Assignments & Hands-on Projects
✅ OOP Principles
✅ Design Patterns
✅ Deep Learning & AI
🔹 Java Mastery → JVM, Concurrency, Performance Optimization, OOP Principles
🔹 DSA Daily → Trees, Graphs, Dynamic Programming (Leetcode Hard-Level)
🔹 SQL & Query Optimization → Transactions, Indexing, Query Plans
🔹 LLD & Microservices → SOLID Principles, API Design, Event-Driven Architecture
🔹 DevOps Setup → Docker, Kubernetes, CI/CD Basics
🔹 Design Patterns → Factory, Singleton, Observer, Strategy
📌 Day 1-7: JVM Deep Dive, DSA (Recursion + DP), SQL Indexing, API Gateway, Docker
📌 Day 8-14: Java Profiling, Graphs in DSA, Advanced SQL Joins, Kafka Basics, CI/CD Setup
📌 Day 15-21: Multithreading, DP Problems, Query Optimization, Microservices Scaling, Kubernetes Pods
📌 Day 22-28: Java Memory Management, System Design (TinyURL), Redis Caching, Kafka Streaming
✅ Project 1 (Month 1): Build a URL Shortener (Java + Spring Boot + Redis)
✅ Assignment (Month 2): Implement a Distributed Rate Limiter (Redis + Java Concurrency)
✅ Project 2 (Month 3): Build a Scalable Chat Application (WebSockets + Kafka + MySQL)
🔹 Advanced Java → Reflection, Annotation Processors, Bytecode Manipulation
🔹 DSA Advanced → Segment Trees, Tries, Graph Algorithms
🔹 SQL + NoSQL → Sharding, Partitioning, MongoDB, Replication
🔹 Microservices → API Security (JWT, OAuth2), Circuit Breaker, SAGA Pattern
🔹 DevOps Scaling → Kubernetes Helm, Observability (Prometheus, Grafana)
🔹 Design Patterns → Composite, Proxy, Adapter, Builder
✅ Project 3 (Month 4): E-Commerce Checkout System (Spring Boot + Kafka + Redis + PostgreSQL)
✅ Project 4 (Month 5): Stock Trading System (Event-Driven Architecture using Kafka + CQRS Pattern)
✅ System Design Assignment (Month 6): Design & Implement a Distributed Logging System (ELK Stack, Kafka, Java)
🔹 Java Performance Tuning → JFR, GC Logs, Profiling, JVM Internals
🔹 DSA Hard-Level → LRU Cache, Heap Optimization, Advanced DP Problems
🔹 SQL at Scale → Query Parallelism, Columnar Databases, Data Warehousing
🔹 Microservices Advanced → Kubernetes Operators, Service Mesh (Istio)
🔹 Cloud Native DevOps → Terraform, Kubernetes Autoscaling, AWS Lambda
🔹 Design Patterns → Chain of Responsibility, State, Memento, Prototype
🔹 Deep Learning → Neural Networks, CNNs, RNNs, Frameworks (TensorFlow, PyTorch)
✅ Project 5 (Month 7): Build a Video Streaming Platform (HLS Streaming, AWS S3, DynamoDB)
✅ Assignment (Month 8): Implement a Scalable Notification Service (Kafka + Redis Streams + Spring Boot)
✅ Project 6 (Month 9): Design a Netflix-like Recommendation Engine (Machine Learning + Java + GraphDB)
🔹 Advanced Java → AI/ML with Java, Async Processing (Vert.x, Reactive Streams)
🔹 DSA Final Challenges → FAANG-Level System Design Problems
🔹 SQL + Big Data → Apache Spark, Hadoop, Clickhouse, PrestoDB
🔹 Microservices & Cloud → Kubernetes Security, Multi-Region Deployment
🔹 DevOps Final → GitOps (ArgoCD), CI/CD with Kubernetes, Terraform
🔹 Design Patterns → Strategy, Command, Interpreter, Visitor
🔹 Deep Learning → Reinforcement Learning, GANs, NLP with Transformers
✅ End of Year: Build a Scalable Ride-Sharing System (Uber Clone) with 1M+ Users Load Testing
🔹 OOP Deep Dive → Advanced SOLID principles, design patterns, real-world applications
🔹 Machine Learning → Introduction to ML algorithms, regression, clustering, and their Java implementations
🔹 Blockchain → Basics of Blockchain & integrating it with Java applications
🔹 Graph Databases → Explore Neo4j & its integration with Java for real-time applications
🔹 Cloud Platforms → AWS, Google Cloud, Azure for app integration & DevOps pipelines
📖 Java: Effective Java by Joshua Bloch
📖 Design Patterns: Head First Design Patterns
📖 DBMS: Database System Concepts by Silberschatz
📖 Spring Boot: Official Docs + Spring in Action
📖 DevOps: The Phoenix Project
📅 Daily Tough Assignments (DSA + Java + SQL)
🚀 Industry-Level Projects Every Month
🏆 Final System Design Challenge (Uber, Netflix, WhatsApp Clone)
This roadmap ensures you master Java, SQL, Microservices, DSA, DevOps, AI/ML, and System Design with real-world projects and tough assignments. 🚀🔥
✅ Core Docker Topics for Backend Developers
- Docker Basics What is Docker?
Containers vs. Virtual Machines
Docker Architecture (Docker CLI, Docker Daemon, Docker Images, Containers)
- Working with Docker CLI docker build, docker run, docker exec, docker ps, docker stop, docker rm, docker rmi
Managing containers, images, volumes, and networks via CLI
- Dockerfile Writing optimized Dockerfile for Java/Spring Boot apps
Best practices (e.g., multi-stage builds, minimal base images like alpine)
Environment variables, health checks, custom entry points
- Docker Compose Writing and understanding docker-compose.yml
Spinning up multi-container environments (app + DB + cache)
Linking services (depends_on, volumes, ports)
- Volumes Mounting volumes for persistent data
Bind mount vs named volumes
Use cases like mounting log, config, and data directories
- Networking Container communication using Docker bridge network
Custom networks (docker network create)
Ports and exposing services (EXPOSE, -p, -P)
- Building and Running Java Backend in Docker Creating a Docker image for your Spring Boot app
Running PostgreSQL/MySQL/Redis via Docker in local/dev
application.properties tuning for container environments
- Debugging Dockerized Apps Using docker logs, docker exec, and docker inspect
Troubleshooting common issues: image build errors, container not starting, port conflicts
- Multi-stage Builds (Advanced but Important) Build in one stage, run in another (to reduce image size)
Common for Java: Maven/Gradle build stage → runtime stage with only JAR
- Dockerignore .dockerignore usage to prevent unwanted files (e.g., target/, .git/)
⚙️ Optional But Helpful (Intermediate Level) Connecting to Dockerized Databases for integration testing
Containerizing CI/CD pipelines using Docker (Jenkins, GitHub Actions, GitLab)
Using Docker with Testcontainers for JUnit integration testing
Dockerizing Microservices: Common patterns and pitfalls
🧠 Pro Tips Learn how to optimize image size (avoid bloated JDKs, clean layers).
Always use .dockerignore.
Get comfortable with docker-compose for local development.
Practice building and running your app using Docker on another system (clean environment test).
Understand the difference between EXPOSE and -p, and how localhost behaves inside and outside the container.