DotNet Full Stack Dev

Since its debut in C# 3.0, Language-Integrated Query (LINQ) has reshaped the way C# developers handle data. LINQ allows seamless querying across various data sources like collections, databases, and XML, directly within C#. With each C# release, LINQ has been enhanced to bring more power and flexibility to developers. This blog explores the new methods and capabilities added in each version, demonstrating how LINQ has evolved to meet the demands of modern applications. What is LINQ? LINQ (Language-Integrated Query) is a set of powerful query capabilities embedded into C# that enable developers to interact with data in a SQL-like syntax. By providing direct access to query operations such as filtering, sorting, grouping, and projecting, LINQ simplifies data manipulation, making code more readable and expressive. The LINQ Journey: From C# 3.0 to C# 10.0 Let’s embark on a journey to see how LINQ has grown over the years, with each version bringing new tools that make data handling more

  If you’re wondering whether async/await and multithreading can be used interchangeably, the answer is "not quite." While both are tools for handling tasks concurrently, they serve different purposes. Let's dive into why we need both and why they aren’t suited for the same examples. Key Difference: Task Type Matters To put it simply: Async/await is best for I/O-bound tasks that require waiting (e.g., network requests, file reading). Multithreading shines with CPU-bound tasks that require parallel processing power (e.g., data calculations, algorithms). They’re not interchangeable because each is optimized for different types of work. Using one in the wrong scenario can lead to inefficient code and wasted resources. Example: Downloading Content from Multiple URLs Let’s look at a real-world scenario: downloading content from multiple URLs. Since downloading involves making HTTP requests, this is an I/O-bound task — it mostly waits for the server’s response. Async/await

 In any application, error handling is the gatekeeper of smooth user experiences. It keeps our apps functional, stable, and user-friendly. But without a well-thought-out approach, error handling can quickly become a tangle of try-catch blocks scattered throughout the codebase, leading to messy, hard-to-maintain applications. Let’s uncover the secrets to handling errors globally in .NET applications and explore multiple approaches to keeping your error management neat, powerful, and highly maintainable. By the end of this post, you’ll have a toolkit of effective strategies, leaving you more confident in maintaining resilient applications. Approach 1: Using Custom Exception Middleware Middleware in .NET is a powerful component for centralized processing, and using a custom exception-handling middleware provides a straightforward way to handle errors globally. Instead of cluttering the code with try-catch blocks, we use middleware to capture and process exceptions centrally. Why This

  Why Chain of Responsibility? And Why Should You Care? If you’re a developer maintaining a monolithic API, you've likely come across complex code blocks with endless conditionals and nested logic for things like validation, authorization, and more. And if you’ve ever thought, "There’s got to be a cleaner way to handle these responsibilities!" , you’re absolutely right. Enter the Chain of Responsibility (CoR) pattern—a simple, elegant solution to untangle your code and make it more flexible and scalable! In this blog, we’ll dive into how to implement the CoR pattern in your existing Item API using minimal code changes. We'll keep it interactive, light, and fun so you’ll walk away not just understanding CoR but also excited to implement it! The Power of the Chain of Responsibility Pattern The CoR pattern allows you to break down complex logic into individual “handlers.” Each handler only handles a specific responsibility (say, validation), and if it can’t process the r

  In this blog post, we’ll go over a query to find employees who manage the same number of subordinates as their own manager does. This scenario might arise in organizations with layered management, where managers oversee teams of similar sizes. By using Common Table Expressions (CTEs) and joins, we can compare employee counts across different levels of management. Problem Statement The goal is to identify employees who have the same number of direct reports (subordinates) as their managers. This involves: Counting the number of employees each manager oversees. Comparing the count of direct reports for each employee with their manager’s count. Example Schema We’ll work with an Employees table that has the following columns: Employees : EmployeeID (Primary Key): Unique identifier for each employee. ManagerID (Foreign Key): Identifies the manager of each employee. SQL Query We need to: Create a CTE to calculate the count of direct reports for each manager. Join this CTE with the Emplo

  This query aims to identify the top 10 customers who have made the highest single purchase amounts. In other words, for each customer, we calculate their highest single transaction amount and return the 10 highest values across all customers. Problem Statement: We need to list the top 10 customers based on their highest single transaction. This query is useful in understanding which customers make the most significant individual purchases, providing insight into high-value customers. Example Schema: Assume we have a Sales table with the following columns: Sales : SaleID (Primary Key) CustomerID (Foreign Key, identifies the customer) TotalAmount (Decimal, representing the amount spent on a single purchase) SQL Query: To find the customers with the highest single purchases, we: Use MAX(TotalAmount) to get the highest single purchase amount for each customer. Sort these maximum amounts in descending order to get the largest values at the top. Use TOP 10 to limit the result to only

  In today's digital age, data security is no longer a luxury—it's a necessity. Whether you're building a simple app or a large-scale enterprise solution, securing your SQL database should be a top priority. But how can we make sure our SQL databases are safe from prying eyes and sneaky cyber villains? Let's embark on this journey to discover the layers of security that protect our SQL databases, and by the end, you’ll have an ironclad strategy to secure your precious data! 🛡️ Why Database Security Matters Think of your SQL database as the treasure chest holding all your application's secrets—user information, payment details, business analytics, and more. Without proper security measures, that treasure chest might as well have a giant “Open for All” sign on it! Breaches can lead to severe consequences, from reputational damage to hefty fines. So, buckle up, and let's dive into the strategies that keep this data safe! 1. Authentication: Who’s Knocking at the Do