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Author: Rahul Mittal

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Surviving and Thriving in the AI Era: Reducing Workplace Politics and Building Collaborative IT Teams
  • AI
  • leadership

Surviving and Thriving in the AI Era: Reducing Workplace Politics and Building Collaborative IT Teams

  • Rahul Mittal
  • June 19, 2026
  • 5 min read

Introduction The IT industry has always evolved rapidly. We moved from client-server systems to web applications, from monoliths to microservices, from data centers to cloud computing, and now into the…

SOLID Principles in Java: Building Maintainable and Scalable Applications
  • interview

SOLID Principles in Java: Building Maintainable and Scalable Applications

  • Rahul Mittal
  • June 19, 2026
  • 5 min read

Introduction As software systems grow, code often becomes difficult to maintain, extend, and test. New features introduce unexpected bugs, small changes require modifications in multiple places, and development velocity gradually…

JVM, Memory Management & Exception Handling
  • interview
  • java
  • programming
  • spring

JVM, Memory Management & Exception Handling

  • Rahul Mittal
  • June 19, 2026
  • 8 min read

MCQs Q1. Which JVM memory area stores objects? A. Stack B. Heap C. Method Area D. Program Counter Q2. What is Garbage Collection? A. Memory Allocation B. Memory Cleanup of…

Exception Handling in Java: From Throwable to Global Exception Handling in Spring Boot
  • java
  • programming

Exception Handling in Java: From Throwable to Global Exception Handling in Spring Boot

  • Rahul Mittal
  • June 17, 2026
  • 6 min read

Introduction Exception handling is one of the most fundamental concepts in Java. Every Java developer uses exceptions daily, yet many developers struggle to explain: Why exceptions exist Difference between Exception…

Building an Annotation-Driven Enterprise Cache Framework for Spring Boot Microservices
  • java
  • microservices
  • programming
  • spring

Building an Annotation-Driven Enterprise Cache Framework for Spring Boot Microservices

  • Rahul Mittal
  • June 17, 2026
  • 5 min read

Introduction In the previous articles, we built a reusable caching library that provides: L1 Cache using EhCache L2 Cache using Redis or GemFire Distributed Locking Cache Stampede Protection Cache Warming…

Cache Invalidation in Distributed Spring Boot Microservices: Spring Events vs Kafka vs Redis Pub/Sub
  • java
  • microservices
  • programming
  • spring

Cache Invalidation in Distributed Spring Boot Microservices: Spring Events vs Kafka vs Redis Pub/Sub

  • Rahul Mittal
  • June 17, 2026
  • 6 min read

Introduction Caching is one of the most effective ways to improve application performance. Whether you are using EhCache, Redis, GemFire, or a multi-level caching strategy, one challenge remains consistent across…

Extending the Platform Cache Library: Distributed Locking, Cache Warming and Cache Stampede Protection
  • java
  • microservices
  • programming
  • spring

Extending the Platform Cache Library: Distributed Locking, Cache Warming and Cache Stampede Protection

  • Rahul Mittal
  • June 17, 2026
  • 6 min read

Introduction In the previous article, we built a reusable caching library that provides: L1 Cache using EhCache L2 Cache using Redis or GemFire Strategy Pattern based provider selection Kafka and…

Building a Pluggable Multi-Level Cache Library for Spring Boot Microservices (EhCache + Redis/GemFire)
  • java
  • microservices
  • programming

Building a Pluggable Multi-Level Cache Library for Spring Boot Microservices (EhCache + Redis/GemFire)

  • Rahul Mittal
  • June 17, 2026
  • 6 min read

Introduction As organizations move toward microservices, caching becomes a critical component for performance, scalability, and cost optimization. Unfortunately, many teams implement caching differently: Different cache providers Different key conventions Different…

Redis vs GemFire vs EhCache: Choosing the Right Caching Strategy for Spring Boot Microservices
  • java
  • microservices
  • programming
  • spring

Redis vs GemFire vs EhCache: Choosing the Right Caching Strategy for Spring Boot Microservices

  • Rahul Mittal
  • June 17, 2026
  • 5 min read

Introduction Caching is one of the most effective techniques for improving application performance. However, choosing the right caching technology can be challenging. Many development teams start with local in-memory caching…

Building High-Performance Distributed Caching with Apache Geode (GemFire) in Spring Boot
  • java
  • microservices
  • programming

Building High-Performance Distributed Caching with Apache Geode (GemFire) in Spring Boot

  • Rahul Mittal
  • June 17, 2026
  • 6 min read

Introduction As enterprise applications scale, databases often become the primary bottleneck. Frequently accessed data such as customer profiles, product catalogs, pricing information, configuration settings, and reference data are repeatedly fetched…

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Recent Posts

  • Part 3: Distributed Tracing in Spring Boot with OpenTelemetry and Jaeger
  • Part 2: Centralized Logging in Spring Boot with ELK Stack — Filebeat, Logstash, Elasticsearch, and Kibana
  • Spring Boot Observability Workshop: Metrics, Logs, and Traces with Actuator, Prometheus, Grafana, ELK, and Jaeger
  • Protected: Advanced Java Interview Questions – 20 MCQs with Answers and Explanations
  • Codex, MCP, and Spring AI: Building Tool-Enabled Java Applications with the Latest OpenAI Models

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