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Clean Code – Summary of Key Ideas from Robert C. Martin
Clean Code is a classic book by Robert C. Martin that outlines the principles and practices of writing code that is readable, maintainable, and efficient. The book is considered a foundational read for software engineers who take pride in craftsmanship. What Is Clean Code Clean code is code that is easy to understand and change.…
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Useful and structured summary of “Clean Architecture” by Robert C. Martin (Uncle Bob)
1. Objective of the Book Uncle Bob’s Clean Architecture lays out timeless software design principles and architectural guidelines that help developers build robust, maintainable, and scalable systems — regardless of language or platform. 2. Core Philosophy The primary goal of architecture is to support system behavior and preserve options. Good architecture: 3. Fundamental Concepts A.…
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Setting Up a Production-Grade Kubernetes Cluster
Kubernetes is a powerful system for managing containerized applications, but setting up a production-grade Kubernetes cluster involves more than just running kubeadm init. This article walks you through the key considerations and steps to deploy a secure, scalable, and maintainable cluster that’s ready for real-world workloads. 1. Define Your Requirements Before provisioning anything, you must…
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Kubernetes vs OpenShift (OCP): What You Need to Know
In the world of container orchestration, Kubernetes has emerged as the de facto standard. However, when it comes to enterprise-grade solutions, Red Hat OpenShift often enters the conversation. While both platforms revolve around Kubernetes at their core, there are key differences in terms of features, usability, and enterprise readiness. This article explores what Kubernetes and…
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Understanding the CAP Theorem: Consistency, Availability, and Partition Tolerance in Distributed Systems
Distributed systems are designed to be scalable, resilient, and efficient. However, building such systems often involves trade-offs. One of the most important theoretical frameworks for understanding these trade-offs is the CAP Theorem. In this article, we will explore what the CAP Theorem is, what each of its components means, and how real-world systems navigate the…
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Understanding Geohashing: A Compact Way to Encode Geographic Coordinates
Geohashing is a fascinating and efficient technique used to encode geographic coordinates such as latitude and longitude into a short alphanumeric string. This method is widely used in geospatial applications where compactness, comparison efficiency, or location-based indexing is important. In this article, we explore the fundamentals of geohashing, how it works, its practical uses, and…
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What Is Reactive Spring Boot?
Reactive Spring Boot (via Spring WebFlux) is a non-blocking, asynchronous programming model built on Project Reactor. It supports event-driven execution, making it ideal for handling thousands of concurrent requests without being limited by traditional thread-per-request constraints. It is different from the traditional Spring MVC (which is synchronous/blocking and servlet-based). Key Benefits of Reactive Spring Boot…
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gRPC vs REST – High-Level Overview
Feature gRPC REST Protocol HTTP/2 + Protocol Buffers (binary) HTTP/1.1 + JSON (text-based) Data Format Protobuf (compact, fast) JSON (human-readable) Performance High (binary, multiplexed, compressed) Moderate (verbose JSON) Communication Bi-directional streaming, multiplexing Only request-response Contract Strongly typed via .proto files Loose, relies on conventions Language Support Auto-codegen in many languages Manual structure, less automation Browser…
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CDN – Content Delivery Network
A CDN (Content Delivery Network) is a geographically distributed network of proxy servers (edge servers or Points-of-Presence, PoPs) that cache and deliver web content (images, videos, scripts, etc.) to users with low latency and high availability. Its core goals are to: High-Level Architecture & Components Key Components: Additional Critical Layers: Building Blocks for CDN Deployment…
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Functional Approximation – Deep Neural Network
Functional approximation in reinforcement learning (RL) is a way to handle complex environments where keeping track of every possible situation (state) is impractical. Instead of storing exact values for every state, we use mathematical models (functions) to estimate them. Why is it needed? In RL, an agent learns by interacting with an environment and trying…