DotNet Full Stack Dev

  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 E...

  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 ...

  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...

Caching is one of the most powerful ways to boost your .NET app’s performance, and when it comes to speed and scalability, Redis takes center stage. Redis, short for Remote Dictionary Server, is a popular choice for in-memory caching due to its lightning-fast speed, persistence options, and support for complex data structures. Whether you’re just getting started with Redis in .NET or looking to optimize, we’ve compiled the ultimate list of Q&As to help you elevate your Redis game. 1. What Exactly is Redis, and Why Should You Use It? Redis is an in-memory data structure store that’s often used as a cache, message broker, and data store. Why is it so popular in .NET? Simple: speed and flexibility. Redis keeps data in memory, allowing for rapid access, and its built-in data structures (like strings, lists, sets, and hashes) let you store just about anything. For a high-speed .NET app that scales, Redis is a game-changer. 2. Setting Up Redis Cache in .NET Core To get Redis working in ....

  In this blog post, we’ll go over how to write a query to find products that have been sold in every city where the company operates. This can be valuable for companies to identify popular or well-distributed products across all operational regions. Problem Statement: We want to find all products that have been sold in every city listed in the sales data. This requires identifying products that are available across all locations without any gaps. Example Schema: Assume we have a Sales table with the following columns: Sales : SaleID (Primary Key) ProductID (Foreign Key, links to product) City (Name of the city where the sale was made) SQL Query: To identify these products, we need to: Group sales data by ProductID . Count the unique cities ( DISTINCT City ) each product has been sold in. Use HAVING to filter results where this count matches the total number of unique cities in the table. Here’s the query: SELECT ProductID FROM Sales GROUP BY ProductID HAVIN...

  In this blog post, we will walk through how to calculate a running total of sales for each day within the past month. The running total is a common reporting requirement that shows the cumulative sales up to each specific day, helping businesses track trends over time. Problem Statement: We need to calculate the cumulative sales for each day over the past month. This means that for each day, the total sales up to that point in time (including all previous days) will be displayed. Example Schema: Assume we have the following table: Sales : SaleID (Primary Key) OrderDate (DateTime) TotalAmount (Decimal) SQL Query: To calculate the running total, we can use the SUM() function with the OVER() clause to perform a window function . The window function allows us to calculate cumulative totals without having to manually aggregate the data for each day. Here is the query: SELECT OrderDate, SUM (TotalAmount) OVER ( ORDER BY OrderDate ROWS BETWEEN UNBOUNDED PRECEDING AND...

  In this blog post, we will explore how to write a query that identifies customers who have made at least one purchase in every month for the last six months. This type of query is useful in analyzing customer loyalty, engagement, and purchasing frequency over time. Problem Statement: We need to find all customers who have made at least one purchase in each of the last six months. This requires tracking customer orders over a rolling six-month period and ensuring that there is a purchase recorded for each month. Example Schema: Assume we have the following table: Orders : OrderID (Primary Key) CustomerID (Foreign Key, links to customer) OrderDate (DateTime) SQL Query: To solve this, we will: Filter orders from the last six months. Group the results by CustomerID . Count the distinct months in which each customer has made a purchase. Use HAVING to ensure that the customer made a purchase in exactly six distinct months. Here’s the query: SELECT CustomerID FROM Orders...

In this blog post, we will walk through the process of calculating the total sales for each month of the current year, ensuring that months with zero sales are included in the result. This is a common reporting requirement when analyzing monthly sales data, as businesses often want to see trends over time, including months without any sales. Problem Statement: We need to calculate the total sales for each month of the current year and display the result in such a way that even months with no sales are shown with a total of zero. Example Schema: Assume we have the following two tables: Sales : SaleID (Primary Key) SaleDate (DateTime) Amount (Decimal) Calendar (optional or created on the fly): MonthNumber (Integer, 1 for January, 2 for February, etc.) MonthName (String, 'January', 'February', etc.) Never miss new posts by subscribe Subscribe Powered by SQL Query: To achieve this, we need to perform the following steps: Generate a list of all months in the...

  In this blog, we will break down how to retrieve the top 5 highest-paid employees from each department using SQL. This is a common query when working with databases that store information about employees and departments, and it involves ranking employees by their salary and returning the top earners for each department. Problem Statement: We want to retrieve the top 5 highest-paid employees in each department, sorted by salary in descending order. This means for each department, we need to rank employees by their salary, then limit the results to the top 5 for that department. Example Schema: Assume we have the following two tables: Employee : EmployeeID (Primary Key) FirstName LastName Salary DepartmentID (Foreign Key) Department : DepartmentID (Primary Key) DepartmentName SQL Query: To solve this, we can use Common Table Expressions (CTEs) or Window Functions to rank employees by their salary within each department. The ROW_NUMBER() window function is particularly useful ...