Building Reusable Systems for Data Engineers

🧱 𝗜 𝗦𝗼𝗹𝘃𝗲𝗱 𝗮 “𝗩𝗶𝘀𝘂𝗮𝗹” 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗨𝘀𝗶𝗻𝗴 𝗢𝗻𝗹𝘆 𝗮 𝗦𝘁𝗮𝗰𝗸 — 𝗡𝗼 𝗚𝗲𝗼𝗺𝗲𝘁𝗿𝘆 𝗡𝗲𝗲𝗱𝗲𝗱 Today’s problem looked visual and innocent: 𝗚𝗶𝘃𝗲𝗻 𝗯𝗮𝗿 𝗵𝗲𝗶𝗴𝗵𝘁𝘀 𝗼𝗳 𝗮 𝗵𝗶𝘀𝘁𝗼𝗴𝗿𝗮𝗺 (𝘄𝗶𝗱𝘁𝗵 = 𝟭), 𝗳𝗶𝗻𝗱 𝘁𝗵𝗲 𝗹𝗮𝗿𝗴𝗲𝘀𝘁 𝗿𝗲𝗰𝘁𝗮𝗻𝗴𝗹𝗲 𝗮𝗿𝗲𝗮. This is the classic problem from 𝗟𝗲𝗲𝘁𝗖𝗼𝗱𝗲 — and it’s a beautiful lesson in how 𝗺𝗼𝗻𝗼𝘁𝗼𝗻𝗶𝗰 𝘀𝘁𝗮𝗰𝗸𝘀 𝗰𝗼𝗻𝘃𝗲𝗿𝘁 𝗴𝗲𝗼𝗺𝗲𝘁𝗿𝘆 𝗶𝗻𝘁𝗼 𝗶𝗻𝗱𝗶𝗰𝗲𝘀. 🧠 𝗧𝗵𝗲 𝗡𝗮𝗶𝘃𝗲 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 (𝗮𝗻𝗱 𝘄𝗵𝘆 𝗶𝘁 𝗳𝗮𝗶𝗹𝘀) For every bar i, try expanding left and right until you hit a smaller bar. That’s O(n) per index -> O(n²). Too slow for n=10^5. So the real question becomes: For each bar, how far can it extend without hitting a smaller height? That is the entire problem. 💡 𝗞𝗲𝘆 𝗜𝗱𝗲𝗮 — 𝗡𝗲𝗮𝗿𝗲𝘀𝘁 𝗦𝗺𝗮𝗹𝗹𝗲𝗿 𝗘𝗹𝗲𝗺𝗲𝗻𝘁𝘀 For every index i: 1. Find the first smaller bar on the left 2. Find the first smaller bar on the right A monotonic increasing stack gives both in linear time. ▶️ 𝗟𝗲𝗳𝘁 𝗦𝗰𝗮𝗻 (𝗟 -> 𝗥) Pop until the top is strictly smaller. That index defines the left boundary. ◀️ 𝗥𝗶𝗴𝗵𝘁 𝗦𝗰𝗮𝗻 (𝗥 -> 𝗟) Same logic to get the right boundary. Now each bar knows the exact range where it is the minimum. ⏱️ 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 Each index pushed/popped once → O(n) Space → O(n) ✨ 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀 1. “Nearest smaller element” is a powerful pattern 2. Monotonic stacks turn nested loops into linear scans 3. Think: If this bar is the minimum, how wide can the rectangle be? A visual problem, solved with clean stack discipline. #DataStructures #Algorithms #MonotonicStack #Java #ProblemSolving #CodingInterview #Stack #DSA #LeetCode #Coding #Programmer #SoftwareEngineer #InterviewPrep #CompetitiveProgramming #100DaysOfCode #Tech #Learning #ProblemSolvingSkills

Streamlit aids operations, not just B.I. This Python framework builds data-centric apps. While most people use it for dashboards and predictive model interfaces, clever backend development transforms it into a workflow management platform. Here, I demonstrate an I.T. ticket management system. It shows the current status of all tickets and has a dashboard for team performance tracking. The backend is an event log - a data structure that enables time-aware analytics, transfer tracking, and more. Thankfully, Streamlit's syntax is simple, which made development quick and easy. In short, you can build fully custom workflow tools in just a few hours. How do you use Streamlit? #Operations #BuildVsBuy #Python

Is your business backend "Silent" or "Active"? 📉 vs 📈 Most founders are trapped on the "Hustle Path"—where every dollar of revenue requires an extra hour of manual intensity. At LeadAndLogic, we help you pivot to the Systems Path. By engineering Technical Integrity and Automated Audits into your backend, we turn your data into a predictable revenue system. #LeadAndLogic #GrowthEngineering #Scaling #SystemsThinking #Python #Automation #LeadAndLogicHQ #GrowthPartner #GrowthSystem #FullStackDevelopment #Marketing

𝐂𝐥𝐚𝐮𝐝𝐞’𝐬 𝐏𝐲𝐭𝐡𝐨𝐧 𝐜𝐨𝐝𝐞 𝐚𝐠𝐞𝐧𝐭 𝐢𝐬 𝐝𝐨𝐢𝐧𝐠 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐦𝐨𝐬𝐭 𝐭𝐞𝐚𝐦𝐬 𝐡𝐚𝐯𝐞 𝐫𝐞𝐚𝐥𝐢𝐳𝐞𝐝 𝐲𝐞𝐭. ⚙️ It doesn’t just write code. It holds context, catches logic gaps early, and suggests meaningful refactors. This isn’t faster coding. It’s a different workflow.   The gap is already visible: Faster releases, fewer review cycles, less firefighting, more building. 📈 Teams getting it right treat it as a collaborator, not a shortcut. 𝐀𝐈 𝐡𝐚𝐧𝐝𝐥𝐞𝐬 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧. 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬 𝐝𝐫𝐢𝐯𝐞 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬. The real question isn’t adoption. It’s how much delay you can afford. ⏳ #KarmickSolutions #Python #AIinDevelopment #ClaudeAI #SoftwareDevelopment #TechTrends #Developers #Automation #FutureOfWork

I spent 3 months designing a multi-agent Python system called FORGE. It never shipped. I kept redesigning it. Then it hit me. Claude Code already is the runtime. I don't need Python. I need Markdown files that tell Claude what to do. So I rebuilt it as forge-skills. Open source. 12 skills, 5 agent personas, 7 slash commands. /grill > /spec > /architect > /plan > /build > /review > /ship Each stage produces a .forge/ artifact the next stage reads. The whole pipeline runs on structured Markdown. Zero Python. The part I'm most proud of: /architect writes typed interface contracts per module. Precise enough that two AI agents can build different modules in parallel and integrate without surprises. Andrej Karpathy calls this "Agentic Engineering". Humans write specs and architecture. Agents implement. Humans review. That's exactly what this is. https://lnkd.in/gvMik-Gw #AgenticEngineering #ClaudeCode #OpenSource

Automation = more human time Automation isn’t about replacing people — it’s about removing boring, repetitive work so humans can focus on creativity and strategy. I use Python + Playwright to automate web tasks and free up hours every week for higher-value work. If your team still does manual browser checks, start small and measure the time you get back. Hashtags: #Automation #Python #Playwright #Productivity Truly yours Bot.

🧠 A system that watches itself… and fixes itself. In this demo, the system continuously monitors its own state. When a service stops, it doesn’t wait for manual intervention. It detects the issue and automatically restores it. No clicks. No commands. Just a feedback loop: observe → decide → act. This is a simple prototype, but it reflects a bigger idea: Systems shouldn’t just report problems — they should handle them. #Automation #DevOps #SelfHealing #Python #Ansible #radwan_so

marimo discussed at Software Engineering Daily. Akshay Agrawal talks to Kevin Ball (the host) about marimo history, its purpose, the problems it intends to solve and effort toward parity with Jupyter notebooks. #marimo #python #datascience #softwaredevelopment https://lnkd.in/dCJgRKtt

Most people think debugging is about finding the bug. It's not. It's about building the system that finds the next one faster. At my current role, when I joined, critical backend issues took about 48 hours to resolve. Not because people were slow — because there was no structure around how issues got triaged, reproduced, and traced through the pipeline. I didn't write a magic tool. I just built a repeatable debugging workflow. Structured logging in the right places. Clear escalation steps. A habit of writing down what broke and why after every incident. Resolution time dropped to about 12 hours. The lesson I keep relearning: the highest-leverage engineering work is often not building new features. It's making the system easier to understand when something goes wrong at midnight. That applies to every backend I've worked on — Java microservices, Python pipelines, LLM-integrated workflows. The stack changes. The need for structured observability never does. #SoftwareEngineering #BackendEngineering #Debugging #Python #ProductionSystems #AIEngineering #BuildInPublic

I’ve just wrapped up a major milestone in my backend journey — implementing asynchronous processing in my Task Manager project, and the results are What I built: Sync vs Async API comparison endpoints Concurrent request handling using async routes External API integration with parallel calls Clean UI dashboard to visualize performance differences Results: Sync execution: 2160 ms Async execution: 1586 ms ~574 ms faster with async! This clearly shows how asynchronous programming can significantly improve performance when dealing with multiple I/O operations. Key Takeaways: Async = better scalability & responsiveness Perfect for external API calls & high-load systems Clean architecture makes debugging & scaling easier Tech Stack: FastAPI | Python | Async/Await | HTTPX | SQLite | Custom UI This phase really helped me understand how modern backend systems handle concurrency efficiently. #BackendDevelopment #Python #FastAPI #AsyncProgramming #WebDevelopment #SoftwareEngineering #LearningInPublic #100DaysOfCode