Raees Qazi (RYQ's) | DevOps Engineer | Learner | Mentor | Creator

 Today, I’m going to discuss a very important concept in Python: How to raise an Exception in code and why we use it. As DevOps engineers, we often write automation scripts, CI/CD pipelines, and cloud management tools. In real-world projects, sometimes we want our program to stop execution when something unexpected happens. This is where raise Exception becomes useful. What is raise Exception ? The raise keyword is used to manually generate an exception. When Python encounters a raised exception that is not handled, it immediately stops the program. Example 1: Raising an Exception Outside a Try Block cloud_envs = [ "aws" , "gcp" , "azure" ] try: print(cloud_envs[4]) except: print("Exception handled") finally: print("I will execute anyways") print("This code should run") raise Exception("This is a new Exception") Output Exception handled I will execute anyways This code should run Exception: This is a...

Today I’m going to discuss Exception Handling in Python from a DevOps engineer’s point of view. What is Exception Handling? Before understanding exception handling, let’s first understand what an exception is. An exception is an error that occurs during program execution. If the error is not handled properly, the program stops immediately. As DevOps engineers, we deal with systems, automation, scripts, servers, cloud platforms, CI/CD pipelines, and monitoring tools daily. Because of this, errors and bugs are very common in our routine work. Sometimes the issue is fixed by developers because they have deep knowledge of the application code. But from the DevOps side, we often need to handle the error properly so the automation or service does not completely fail. This process is called Exception Handling . Why Do We Use Exception Handling? There are mainly two ways to deal with errors: Fix the actual error Handle the error gracefully using exception handling In exception handling, we ...

 Today I’m going to share a real-time interview experience that really improved my logical thinking as a DevOps engineer. During the interview, I was given a small Python task that looked simple at first, but it was actually a good brainstorming challenge. The task was to convert a list into a dictionary and count how many times each car name appeared. Interview Task cars = [ "audi" , "audi" , "audi" , "bmw" , "Jaguar" , "jaguar" ] The expected output was: { 'audi' : 3, 'bmw' : 1, 'jaguar' : 2} At first, this task was a little confusing for me because I needed to understand how counting logic works in Python dictionaries. But after practicing the logic step by step, I solved it successfully. Step-by-Step Solution cars = [ "audi" , "audi" , "audi" , "bmw" , "Jaguar" , "jaguar" ] final_car_count = {} for car in cars: # Convert all values in...

 Today I’m going to discuss one of the most famous sorting techniques in programming called Bubble Sort . As a DevOps engineer, understanding algorithms is also important because automation and scripting become easier when your logic building is strong. Bubble Sort is a simple algorithm that sorts numbers step by step by comparing values with each other and swapping them when needed. Let’s understand it in a very easy way. What is Bubble Sort? Bubble Sort is a sorting technique that compares two values again and again and places the smaller value before the bigger value. The process keeps repeating until the complete list becomes sorted. For example: [-2, -1, 0, 1, 1, 2, 3, 4, 5, 56, 89] This is called ascending order because numbers are arranged from smaller to larger. Bubble Sort Code Example list_of_num = [ 1 , 1 , 2 , 3 ,- 1 , 4 ,- 2 , 0 , 56 , 89 , 5 ] for i in range(len(list_of_num)): for j in range(len(list_of_num)): if list_of_num[i] < list_of_num[j]: ...

Raees Qazi | DevOps Engineer | Learner | Mentor | Creator | CEO-Briller Technologies Today, I will explain algorithms in a very simple way, especially from a DevOps engineer’s point of view. 🔹 Code vs Algorithm (Important Difference) Before jumping into algorithms, let’s understand the difference: Code:  A set of instructions written in a programming language that a computer can understand and execute. Algorithm:  An algorithm is the idea or step-by-step logic to solve a problem.  It is not code — it’s the thinking behind the code. 👉 In simple words: Algorithm = Plan Code = Implementation of that plan 🔍 Linear Search Algorithm (Real DevOps Example) Let’s take a simple DevOps-related example. We have environments like: env = [ "dev" , "stg" , "prd" ] key = "stg" Now, we want to find “stg” environment in the list. 🧠 Algorithm Steps (Thinking Process) As a DevOps engineer, here’s how we think: Start Loop through all environments Compare ea...

 As a DevOps engineer, we deal with automation, configuration management, and scripting almost every day. Python is one of the most commonly used languages in our field, so understanding its data structures is very important. In this article, I’ll explain Tuples and Sets in a simple and practical way. 🔹 What is a Tuple? A tuple is a data structure in Python, similar to a list. The main difference is in the syntax: List → uses square brackets [] Tuple → uses parentheses () Example: tuple_of_days = ( "mon" , "tue" , "wed" , "thur" , "fri" , "sat" , "sun" ) list_of_days = [ "mon" , "tue" , "wed" , "thur" , "fri" , "sat" , "sun" ] print(tuple_of_days[1]) print(list_of_days[1]) Output: tue tue At first glance, both behave the same. But the real difference comes when we try to modify them. 🔹 Tuple vs List (Key Difference) Let’s try updating valu...