Aditya Padhye’s Post

🚀 Python for DevOps – Log Analysis with Metrics Today I enhanced my log automation script to not only separate logs by level but also count them for quick insights. 📂 Problem: Manually analyzing logs is slow and inefficient. 💻 Solution (Python Script): error = warning = info_count = 0 with open("app.log") as f, \ open("error.log", "w") as err, \ open("warning.log", "w") as warn, \ open("info.log", "w") as info: for line in f: if "ERROR" in line: err.write(line) error += 1 elif "WARNING" in line: warn.write(line) warning += 1 elif "INFO" in line: info.write(line) info_count += 1 print("ERROR:", error) print("WARNING:", warning) print("INFO:", info_count) 🔍 What this does: Reads app.log Splits logs into separate files Counts each log level 📊 Sample Output: ERROR: 1 WARNING: 1 INFO: 2 Output: ubuntu@satheesha:~/python$ python3 multiple-log_level.py ERROR: 1 WARNING: 1 INFO_COUNT: 2 ubuntu@satheesha:~/python$ ls -ltr error.log warning.log info.log -rw-r--r-- 1 ubuntu ubuntu 18 Apr 21 08:20 warning.log -rw-r--r-- 1 ubuntu ubuntu 44 Apr 21 08:20 info.log -rw-r--r-- 1 ubuntu ubuntu 17 Apr 21 08:20 error.log ubuntu@satheesha:~/python$ cat error.log ERROR: Disk full ubuntu@satheesha:~/python$ cat warning.log WARNING: High CPU ubuntu@satheesha:~/python$ cat info.log INFO: Service startes INFO: Service startes 🔥 Why this matters: Quick visibility into system health Helps prioritize issues (ERROR > WARNING > INFO) Reduces manual troubleshooting time 💡 Key Learning: Python can be used not just for automation, but also for real-time insights and monitoring in DevOps. 📈 Next Step: Add alerting when ERROR count exceeds threshold Integrate with monitoring tools Build real-time log monitoring (tail -f in Python) #Python #DevOps #Automation #Scripting #Monitoring #Cloud #Learning

If you’re learning Python for tech roles, here’s the progression that covers most use cases: 1. Basics Start with the fundamentals: variables, operators, data types, loops, functions, and control flow. → This stage builds the foundation for everything else. 2. Advanced Python Move into concepts like OOP, decorators, generators, regex, lambda functions, and multithreading. → This is where you understand what’s happening behind the scenes and how Python actually works. 3. Data Structures & Algorithms (DSA) Arrays, strings, linked lists, trees, recursion, and complexity analysis help strengthen problem-solving and coding interview skills. → Great for sharpening problem-solving and interview prep, but depth here can vary depending on your career path. 4. Testing Debugging, unit testing, writing test cases, mocking, and measuring code coverage. → Essential for writing reliable, production-grade Python. 5. Libraries & Databases Learn core libraries like NumPy, Pandas, and Matplotlib, and work with databases such as MySQL, SQLite, or PostgreSQL. → This is where Python starts becoming useful for real-world applications. 6. Specialize by Role From here, the path branches depending on your goals: * Web Dev: Django, Flask, REST APIs * DevOps: Docker, CI/CD, AWS Boto3, GCP SDK, Pulumi, automation * Data Science: Data cleaning, EDA, visualization, statistics * Machine Learning: Scikit-learn, model evaluation, deep learning, AI projects → Choose the direction that aligns with the problems you want to solve. Python isn’t one career path ~ it’s a gateway to many. The key is building the core layers first, then specializing. Which direction are you using Python for right now? Image Credits: yourclouddude _________________________________________ 𝐄𝐧𝐫𝐨𝐥 𝐧𝐨𝐰! 𝐃𝐞𝐯𝐎𝐩𝐬 𝐂𝐨𝐡𝐨𝐫𝐭 𝟒 𝐢𝐬 𝐧𝐨𝐰 𝐨𝐩𝐞𝐧. If you're serious about becoming a world-class DevOps engineer in 2026, this is your path. This isn't another bootcamp. This isn't a tutorial hell with a certificate at the end. This is systems-based training for engineers ready to go from good to exceptional. WHAT YOU'LL BUILD Not toy projects. Not "hello world" apps. Real production-grade systems: → Multi-environment CI/CD pipelines with DevSecOps → Infrastructure as Code that scales across 3+ environments → Production observability with Prometheus, Grafana, and OpenTelemetry Join today 👉 https://lnkd.in/eS3t5NwE

Multi-Agent A2A with the Agent Development Kit(ADK), AWS Lightsail, and Gemini CLI: Leveraging the Google Agent Development Kit (ADK) and the underlying Gemini LLM to build Multi-Agent Applications with A2A protocol support using the Python programming language. Aren’t There a Billion Python ADK Demos? Yes there are. Python has traditionally been the main coding language for ML and AI tools. The goal of this article is to provide a multi-agent test bed for building, debugging, and deploying multi-agent applications. What you talkin ‘bout Willis? So what is different about this lab compared to all the others out there? This is one of the first deep dives into a Multi-Agent application leveraging the advanced tooling of Gemini CLI. The starting point for the demo was an existing Codelab- which was updated and re-engineered with Gemini CLI. The original Codelab- is here: Building a Multi-Agent System | Google Codelabs What Is Python? Python is an interpreted language that allows for rapid development and testing and has deep libraries for working with ML and AI: Welcome to Python.org Python Version Management One of the downsides of the wide deployment of Python has been managing the language versions across platforms and maintaining a supported version. The pyenv tool enables deploying consistent versions of Python: GitHub - pyenv/pyenv: Simple Python version management As of writing — the mainstream python version is 3.13. To validate your current Python:python --version Python 3.13.13 Amazon Lightsail Amazon Lightsail is an easy-to-use virtual private server (VPS) provider and cloud platform designed by AWS for simpler workloads, offering developers pre-configured compute, storage, and networking for a low, predictable monthly price. It is ideal for hosting small websites, simple web apps, or creating development environments. More information is available on the official site here: Amazon's Simple Cloud Server | Amazon Lightsail And this is the direct URL to the console:https://lnkd.in/eV7DaV8y The Lightsail console will look similar to: Gemini CLI If not pre-installed you can download the Gemini CLI to interact with the source files and provide real-time assistance:npm install -g @google/gemini-cli Testing the Gemini CLI Environment Once you have all the tools and the correct Node.js version in place- you can test the startup of Gemini CLI. You will need to authenticate with a Key or your Google Account:▝▜▄ Gemini CLI v0.33.1 ▝▜▄ ▗▟▀ Logged in with Google /auth ▝▀ Gemini Code Assist Standard /upgrade no sandbox (see /docs) /model Auto (Gemini 3) | 239.8 MB Node Version Management Gemini CLI needs a consistent, up to date version of Node. The nvm command can be used to get a standard Node environment: GitHub - nvm-sh/nvm: Node Version Manager - POSIX-compliant bash script to manage multiple… #genai #shared #ai

🚀 Python for DevOps – API Monitoring with requests Practiced using Python’s requests library to check API health, a common real-world DevOps task. 📂 Use Case: In production, services depend on APIs. We need to continuously verify if APIs are reachable and healthy. 💻 Python Script: import requests url = "https://api.github.com" try: res = requests.get(url, timeout=5) if res.status_code == 200: print("✅ GitHub API is UP") else: print("⚠️ GitHub API issue:", res.status_code) except requests.exceptions.RequestException as e: print("🚨 API call failed:", e) Output: Status_code: 200 Response: {'current_user_url': 'https://lnkd.in/guvkNT7k', 'current_user_authorizations_html_url': 'https://lnkd.in/gx-65ERd', 'authorizations_url': 'https://lnkd.in/gzcehbTu', 'code_search_url': 'https://lnkd.in/gQU8cghE', 'commit_search_url': 'https://lnkd.in/g62A-__n', 'emails_url': 'https://lnkd.in/gXaZyEkK', 'emojis_url': 'https://lnkd.in/gp3Scn2Y', 'events_url': 'https://lnkd.in/grbt4NNg', 'feeds_url': 'https://lnkd.in/gCBk-eSN', 'followers_url': 'https://lnkd.in/gQvSEXqB', 'following_url': 'https://lnkd.in/grh4YDpJ', 🔍 What this does: Sends HTTP request to API Uses timeout to avoid hanging Checks response status Handles failures gracefully 🔥 Why this matters in DevOps: Monitor service availability Validate endpoints in CI/CD pipelines Detect outages early Automate health checks 💡 Key Learning: APIs are everywhere in DevOps, and Python makes it easy to integrate, monitor, and automate systems. 📈 Next Steps: Send alerts (Slack/Email) if API fails Combine with log monitoring scripts Build a full monitoring + alerting system #Python #DevOps #API #Automation #Monitoring #Scripting #Cloud #Learning #100DaysOfCode

Choosing between Python and Go for your next microservice is one of the most common architectural decisions backend teams face in 2026. Both languages power microservices at massive scale. But the real answer isn't "always pick X." It depends on what your service actually does. THE PERFORMANCE REALITY JSON API Benchmark (4-core VM, 100 concurrent connections): → Go: 95,000 req/s, 1.05ms avg latency, 12MB memory → Python (FastAPI): 12,500 req/s, 8.1ms avg latency, 52MB memory → 7.6x throughput gap But context matters: → <1,000 req/s: Both languages adequate → 1,000-10,000 req/s: Python works, Go more efficient → >10,000 req/s: Go's efficiency = fewer instances, lower costs CONTAINER COMPARISON → Go: 8-15MB images, 10-50ms startup, 5-10MB idle memory → Python: 180-350MB images, 1-3s startup, 35-60MB idle memory → Cost impact: 5-10x reduction for high-traffic services with Go WHEN TO CHOOSE PYTHON → ML/AI integration (PyTorch, TensorFlow ecosystem) → Data processing and ETL (pandas, NumPy unmatched) → Rapid prototyping (FastAPI auto-docs, no compile step) → Your team knows Python well WHEN TO CHOOSE GO → High-throughput API services (>10K req/s) → Infrastructure and platform services → Low-latency requirements (sub-5ms p99) → Small container footprint matters (edge, serverless) REAL-WORLD PATTERN Companies like Uber, Dropbox, Spotify use BOTH strategically: → Go handles the HOT PATH (performance-critical user requests) → Python handles the SMART PATH (ML models, data analytics) DECISION FRAMEWORK 1. ML/AI model serving? → Python 2. >10K req/s or <5ms p99 latency? → Go 3. Data processing/ETL? → Python 4. Infrastructure service? → Go 5. Team expertise? → Use what your team knows KEY INSIGHT The best microservices architectures use both languages strategically. Python and Go aren't competitors — they're complements. Start with the language your team knows. If a Python service hits performance limits, profile first (most issues are algorithmic, not language-related). If you genuinely need Go-level throughput, rewrite that specific service. Microservices exist precisely to make this targeted migration possible. The right question isn't "Python or Go?" It's "Which services need Python's strengths and which need Go's?" Complete performance comparison: https://lnkd.in/d9WXQ_vA What's your team's approach to language selection? #Python #Go #Microservices #Performance #SoftwareArchitecture #BackendDevelopment

🚀 Learning Python for DevOps – Hands-on Practice with Code Today I worked on a practical DevOps task: Log File Analysis using Python Started with a sample app.log: ERROR: Disk full WARNING: High CPU usage INFO: Service started INFO: Health check passed 🔍 Step 1: Read log file with open("app.log", "r") as f: data = f.read() print(data) ⚠️ Step 2: Filter only ERROR logs with open("app.log", "r") as f: for line in f: if "ERROR" in line: print(line) 📊 Step 3: Count total errors error_count = 0 with open("app.log") as f: for line in f: if "ERROR" in line: error_count += 1 print("Total Errors:", error_count) 🧠 Step 4: Handle multiple log levels (Real-world scenario) error = 0 warning = 0 info = 0 with open("app.log") as f: for line in f: if "ERROR" in line: error += 1 elif "WARNING" in line: warning += 1 elif "INFO" in line: info += 1 print("ERROR:", error) print("WARNING:", warning) print("INFO:", info) 🚨 Step 5: DevOps-style alert output with open("app.log") as f: for line in f: if "ERROR" in line: print("ALERT:", line.strip()) 💡 Key Learning: Python is a powerful tool in DevOps for log monitoring, automation, and faster troubleshooting. 🔥 Even simple scripts like this can help in: Production monitoring CI/CD pipelines Incident detection 📈 Next Goal: Build a real-time log monitoring script (like tail -f) using Python #Python #DevOps #Automation #Scripting #Learning #Cloud #100DaysOfCode

Improving your Python skills is not just about writing code that works. It is about writing code that is efficient, readable, scalable, and production ready. These Python tips and tricks focus on practical improvements that make a real difference: ➜ Writing clean and Pythonic code using best practices ➜ Using list, dict, and set comprehensions effectively ➜ Leveraging built in functions for faster execution ➜ Optimizing loops and reducing time complexity ➜ Understanding memory usage and performance tuning ➜ Mastering functions, lambda expressions, and closures ➜ Applying object oriented design properly ➜ Handling exceptions and debugging efficiently ➜ Working smartly with files and data processing ➜ Using generators and iterators for memory efficiency ➜ Structuring projects with modules and virtual environments ➜ Writing reusable, maintainable, and testable code ➜ Avoiding common mistakes that slow down applications Perfect for developers who want to move from basic scripting to writing professional level Python code. Learn more from w3schools.com Code smarter. Build faster. Think like a pro. 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐫𝐞𝐠𝐫𝐞𝐭 𝐧𝐨𝐭 𝐭𝐚𝐤𝐢𝐧𝐠 𝐢𝐧 𝟐𝟎𝟐𝟔. 1 Meta Front-End Developer 🔗 imp.i384100.net/g1KEQ5 2. Programming with JavaScript 🔗 imp.i384100.net/XYDqvg 3. Machine Learning Specialization 🔗 imp.i384100.net/XYQ9jy 4. Deep Learning Specialization 🔗 imp.i384100.net/jroLxe 5. IBM Data Science Professional Certificate 🔗 imp.i384100.net/LXbNjj 6. Python for Data Science, AI & Development 🔗 imp.i384100.net/1rq3Km 7. Google Data Analytics 🔗 imp.i384100.net/KjnNrn 8. Google Cybersecurity 🔗 imp.i384100.net/Or5L6G 9. Google Project Management 🔗 imp.i384100.net/OeRLoP 10. Meta Social Media Marketing 🔗 imp.i384100.net/RGyDYv 11. Google Cloud imp.i384100.net/19Pz7D 12. Data Structures and Algorithm imp.i384100.net/VxYdN6 13. IBM Full STACK Developer imp.i384100.net/JKVZ22 14. Full Stack Java Developer imp.i384100.net/o4LJOo 15. Mean Stack Developer imp.i384100.net/BnykPB Happy Learning 🌟 ♻️Repost and share to help others. #python #aicourses #aicommunity #linkdin #upskill #career #growth #freecourses #microsoft LinkedIn Learning

Improving your Python skills is not just about writing code that works. It is about writing code that is efficient, readable, scalable, and production ready. These Python tips and tricks focus on practical improvements that make a real difference: ➜ Writing clean and Pythonic code using best practices ➜ Using list, dict, and set comprehensions effectively ➜ Leveraging built in functions for faster execution ➜ Optimizing loops and reducing time complexity ➜ Understanding memory usage and performance tuning ➜ Mastering functions, lambda expressions, and closures ➜ Applying object oriented design properly ➜ Handling exceptions and debugging efficiently ➜ Working smartly with files and data processing ➜ Using generators and iterators for memory efficiency ➜ Structuring projects with modules and virtual environments ➜ Writing reusable, maintainable, and testable code ➜ Avoiding common mistakes that slow down applications Perfect for developers who want to move from basic scripting to writing professional level Python code. Learn more from w3schools.com 💚 Code smarter. Build faster. Think like a pro. 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐫𝐞𝐠𝐫𝐞𝐭 𝐧𝐨𝐭 𝐭𝐚𝐤𝐢𝐧𝐠 𝐢𝐧 𝟐𝟎𝟐𝟔. 1 Meta Front-End Developer 🔗 imp.i384100.net/g1KEQ5 2. Programming with JavaScript 🔗 imp.i384100.net/XYDqvg 3. Machine Learning Specialization 🔗 imp.i384100.net/XYQ9jy 4. Deep Learning Specialization 🔗 imp.i384100.net/jroLxe 5. IBM Data Science Professional Certificate 🔗 imp.i384100.net/LXbNjj 6. Python for Data Science, AI & Development 🔗 imp.i384100.net/1rq3Km 7. Google Data Analytics 🔗 imp.i384100.net/KjnNrn 8. Google Cybersecurity 🔗 imp.i384100.net/Or5L6G 9. Google Project Management 🔗 imp.i384100.net/OeRLoP 10. Meta Social Media Marketing 🔗 imp.i384100.net/RGyDYv 11. Google Cloud imp.i384100.net/19Pz7D 12. Data Structures and Algorithm imp.i384100.net/VxYdN6 13. IBM Full STACK Developer imp.i384100.net/JKVZ22 14. Full Stack Java Developer imp.i384100.net/o4LJOo 15. Mean Stack Developer imp.i384100.net/BnykPB Happy Learning 🌟 ♻️Repost and share to help others. #python #aicourses #aicommunity #linkdin #upskill #career #growth #freecourses #microsoft LinkedIn Learning JavaScript Mastery

Improving your Python skills is not just about writing code that works. It is about writing code that is efficient, readable, scalable, and production ready. These Python tips and tricks focus on practical improvements that make a real difference: ➜ Writing clean and Pythonic code using best practices ➜ Using list, dict, and set comprehensions effectively ➜ Leveraging built in functions for faster execution ➜ Optimizing loops and reducing time complexity ➜ Understanding memory usage and performance tuning ➜ Mastering functions, lambda expressions, and closures ➜ Applying object oriented design properly ➜ Handling exceptions and debugging efficiently ➜ Working smartly with files and data processing ➜ Using generators and iterators for memory efficiency ➜ Structuring projects with modules and virtual environments ➜ Writing reusable, maintainable, and testable code ➜ Avoiding common mistakes that slow down applications Perfect for developers who want to move from basic scripting to writing professional level Python code. Learn more from w3schools.com 💚 Code smarter. Build faster. Think like a pro. 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐫𝐞𝐠𝐫𝐞𝐭 𝐧𝐨𝐭 𝐭𝐚𝐤𝐢𝐧𝐠 𝐢𝐧 𝟐𝟎𝟐𝟔. 1 Meta Front-End Developer 🔗 imp.i384100.net/g1KEQ5 2. Programming with JavaScript 🔗 imp.i384100.net/XYDqvg 3. Machine Learning Specialization 🔗 imp.i384100.net/XYQ9jy 4. Deep Learning Specialization 🔗 imp.i384100.net/jroLxe 5. IBM Data Science Professional Certificate 🔗 imp.i384100.net/LXbNjj 6. Python for Data Science, AI & Development 🔗 imp.i384100.net/1rq3Km 7. Google Data Analytics 🔗 imp.i384100.net/KjnNrn 8. Google Cybersecurity 🔗 imp.i384100.net/Or5L6G 9. Google Project Management 🔗 imp.i384100.net/OeRLoP 10. Meta Social Media Marketing 🔗 imp.i384100.net/RGyDYv 11. Google Cloud imp.i384100.net/19Pz7D 12. Data Structures and Algorithm imp.i384100.net/VxYdN6 13. IBM Full STACK Developer imp.i384100.net/JKVZ22 14. Full Stack Java Developer imp.i384100.net/o4LJOo 15. Mean Stack Developer imp.i384100.net/BnykPB Happy Learning 🌟 ♻️Repost and share to help others. #python #aicourses #aicommunity #linkdin #upskill #career #growth #freecourses #microsoft LinkedIn Learning

Improving your Python skills is not just about writing code that works. It is about writing code that is efficient, readable, scalable, and production ready. These Python tips and tricks focus on practical improvements that make a real difference: ➜ Writing clean and Pythonic code using best practices ➜ Using list, dict, and set comprehensions effectively ➜ Leveraging built in functions for faster execution ➜ Optimizing loops and reducing time complexity ➜ Understanding memory usage and performance tuning ➜ Mastering functions, lambda expressions, and closures ➜ Applying object oriented design properly ➜ Handling exceptions and debugging efficiently ➜ Working smartly with files and data processing ➜ Using generators and iterators for memory efficiency ➜ Structuring projects with modules and virtual environments ➜ Writing reusable, maintainable, and testable code ➜ Avoiding common mistakes that slow down applications Perfect for developers who want to move from basic scripting to writing professional level Python code. Learn more from w3schools.com 💚 Code smarter. Build faster. Think like a pro. 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐫𝐞𝐠𝐫𝐞𝐭 𝐧𝐨𝐭 𝐭𝐚𝐤𝐢𝐧𝐠 𝐢𝐧 𝟐𝟎𝟐𝟔. 1 Meta Front-End Developer 🔗 imp.i384100.net/g1KEQ5 2. Programming with JavaScript 🔗 imp.i384100.net/XYDqvg 3. Machine Learning Specialization 🔗 imp.i384100.net/XYQ9jy 4. Deep Learning Specialization 🔗 imp.i384100.net/jroLxe 5. IBM Data Science Professional Certificate 🔗 imp.i384100.net/LXbNjj 6. Python for Data Science, AI & Development 🔗 imp.i384100.net/1rq3Km 7. Google Data Analytics 🔗 imp.i384100.net/KjnNrn 8. Google Cybersecurity 🔗 imp.i384100.net/Or5L6G 9. Google Project Management 🔗 imp.i384100.net/OeRLoP 10. Meta Social Media Marketing 🔗 imp.i384100.net/RGyDYv 11. Google Cloud imp.i384100.net/19Pz7D 12. Data Structures and Algorithm imp.i384100.net/VxYdN6 13. IBM Full STACK Developer imp.i384100.net/JKVZ22 14. Full Stack Java Developer imp.i384100.net/o4LJOo 15. Mean Stack Developer imp.i384100.net/BnykPB Happy Learning 🌟 #python #aicourses #aicommunity #linkdin #upskill #career #growth #freecourses #microsoft LinkedIn Learning JavaScript Mastery