DotNet Full Stack Development

  So, you’re diving into AWS and feeling a little overwhelmed by the sheer number of services, huh? We’ve all been there. With all the shiny options AWS gives us, it’s easy to get stuck asking: Which service should I use? Well, buckle up, because today we’re putting some AWS services head-to-head in a friendly showdown. By the end, you’ll know exactly which tool fits your project’s needs. Ready? Let’s dive in! 1: AWS Redshift vs DynamoDB Both Redshift and DynamoDB are powerful databases, but they serve very different purposes. Choosing the right one can either make your project soar—or grind it to a halt. AWS Redshift : Think of Redshift as your go-to for massive analytical workloads . If you’ve got petabytes of structured data and need to run complex queries, Redshift is your superhero. It’s built for data warehousing, ideal for companies drowning in data and needing deep insights fast. DynamoDB : On the flip side, DynamoDB is like the sleek sports car of AWS databases: super-fast, h

  In the world of big data, Amazon Redshift stands out as a powerful, fully managed data warehouse service that enables you to analyse large datasets using SQL queries. However, if not optimized properly, even the most robust systems can experience performance degradation. That's why it's essential to employ optimization techniques that ensure your Redshift cluster runs efficiently. In this blog, we’re going to explore some fancy and effective optimization techniques for AWS Redshift to help you supercharge your queries and keep your cluster running like a well-oiled machine. Let’s jump into the deep end and get your data warehouse optimized! 1. Choose the Right Distribution Style Amazon Redshift supports three types of distribution styles: KEY, EVEN, and ALL . Choosing the right one can significantly impact query performance. But how do you decide which one to use? KEY distribution : Use this if you're joining large tables. Rows with the same values will be stored on the

  Let’s be real—security in web applications is no longer optional, it’s an absolute must. With the rise of cyber threats, traditional username and password combinations are just not enough to keep intruders at bay. That’s where Multi-Factor Authentication (MFA) comes into play. In this post, we’ll dive into how to implement MFA in your .NET Core app , creating a layered defense that ensures only the right users gain access. We’ll make it simple, practical, and yes, fancy—because why not make security elegant? Why MFA? Before we get into the code, let’s answer the big question: Why MFA? MFA adds an extra step to the authentication process. It's no longer just about "what you know" (like your password), but also about "what you have" (like your phone for a one-time code) or "who you are" (like your fingerprint or face recognition). This significantly reduces the chances of unauthorized access, even if a password is compromised. Let’s break down a typic

  If you're working with .NET Core and PostgreSQL, you might have come across Dapper , a popular micro ORM (Object-Relational Mapper) that provides a fast, lightweight way to interact with databases. This blog will guide you through the process of connecting your .NET Core application to a PostgreSQL database using Dapper —an elegant combination for developers looking for high performance without the complexity of full-fledged ORMs like Entity Framework. Whether you're new to Dapper or PostgreSQL or just want a quick refresher, this blog will take you through the setup step by step. Let's dive into it! Why Dapper + PostgreSQL? 🤔 Before we dive into the code, let's briefly cover why this combo makes sense: Dapper is super fast because it doesn't abstract away SQL; you write your queries, giving you total control over performance. PostgreSQL is one of the most powerful, open-source relational databases, well known for its scalability, performance, and advanced fe

  In the fast-paced world of cloud-native development, deploying your .NET applications in a scalable, resilient way is the name of the game. And what's better than leveraging AWS EKS (Elastic Kubernetes Service) to deploy your .NET Core Item API to the cloud in a way that’s as flexible as it is powerful? Let’s walk through the process of how you can get your .NET Core Item API running in AWS EKS like a pro — and have some fun along the way! Why AWS EKS? And Why Kubernetes? 🤔 Before we dive into the nitty-gritty, let's set the stage. Kubernetes has become the de facto standard for managing containerized applications, and AWS EKS is a managed service that lets you run Kubernetes clusters without needing to maintain the control plane (AWS takes care of that!). Here’s why you’d want to use AWS EKS for your .NET Core API: Scalability : Easily scale your application up or down based on demand. Resilience : Automated recovery and failover in case of issues. Cost-Efficiency : Pay

  Working with files in distributed environments can be tricky, especially when you need to maintain high availability, scalability, and security. That’s where Amazon Elastic File System (EFS) comes into play, offering a fully managed, scalable file storage solution for use with AWS services and on-premise resources. In this blog, we’ll dive into how you can upload and download CSV files using EFS in a .NET Core application. Let’s break it down step by step, and as always, we’ll make it interactive, engaging, and packed with useful code snippets! Why Use Amazon EFS? 🤔 Amazon EFS is a great choice for: Elastic storage : It grows and shrinks as you add or remove files. Multi-AZ support : Accessible from multiple availability zones, perfect for distributed applications. Secure and reliable : EFS offers encryption and seamless integration with AWS IAM and VPCs. For scenarios where you need to manage CSV files in a multi-instance .NET Core application , EFS can act as a shared storage lay