Data Sense LMS’ Post

No one asked for a shared package. I built one anyway. Multiple teams at a global pharmaceutical company were running the same logic. Fetch data from source. Transform it. Write to ADLS Gen2. Each team had their own version. Assumption: custom code per team is safer. Easier to change without breaking someone else’s pipeline. Reality: five codebases with five variations of the same bug. Every upstream schema change meant five separate fixes. I built an OOP-based Python package. Parameterized. Modular. One abstraction for retrieval, one for transformation, one for storage. Other teams started using it. Then more teams. It became the default pattern not because someone mandated it, but because it was simply better. Reusability isn’t about efficiency. It’s about reducing drift between what you intended and what ten teams independently decided to implement. The hardest part wasn’t the code. It was designing the interface so teams could configure it without needing to understand what was underneath. That’s the real engineering skill. Not writing a good function. Writing one that other engineers trust enough not to rewrite. What’s a pattern you built that spread further than you expected? #DataEngineering #Python #AzureDatabricks

The Architecture of Logic: Why Your "Instruction Set" Matters More Than Your Language We’ve become obsessed with the "what" of technology: What framework are you using? What library did you import? What language is the most popular this year? But if you strip away the syntax of Python, Rust, or JavaScript, you are left with the only thing that actually matters: The Algorithm. Programming is not "Coding" Coding is just the act of translating human intent into a format a machine understands. True programming is the architecture of logic. It is the ability to take a complex problem and break it down into a finite, definite set of instructions. The Library Trap Libraries are useful "black boxes." They save time, but they can also create a ceiling for your growth. A Coder knows how to call a function from a library. An Engineer knows how to replicate that function’s logic with a pen and paper. When we rely too heavily on pre-packaged tools, we lose the ability to see the system as a whole. We start solving problems by "finding a tool" instead of "designing a solution." The Universal Blueprint An algorithm is universal. If your logic is sound, it doesn't care about the substrate. It works: As a flow of electrical signals in a CPU. As a set of "If-Then" rules on a whiteboard. As a Standard Operating Procedure (SOP) for a human team. If you can’t verify your logic without a computer, you don't own the logic the library does. Final Thought Don't just be an "expert" in a specific keyword or framework. Be an architect of instructions. Master the ability to move data from State A to State B with such clarity that the language you choose becomes the least interesting part of the project. Are you building systems, or are you just calling functions? #ComputerScience #SystemDesign #SoftwareArchitecture #EngineeringMindset #ProblemSolving #Logic

Most developers use "async" and "parallel" interchangeably. They're not. And confusing them can cost you hours of debugging. 🟢 ASYNCHRONOUS — One task at a time, but smart A single thread starts a task and moves on while it waits. No blocking. No idle time. Tasks don't run simultaneously — they just don't wait around. 🟣 PARALLEL — Multiple tasks at the same time Multiple threads/cores run tasks truly simultaneously. More CPU cores = more throughput. It's about simultaneous execution, not waiting. 🍳 The kitchen analogy: One chef making coffee → toast → eggs while each item cooks = Async Three chefs each cooking a different dish at the same time = Parallel Key differences: → Async solves I/O bottlenecks — API calls, file reads, DB queries. (Node.js, Python asyncio, JS Promises) → Parallel solves CPU bottlenecks — image processing, ML training, data crunching. (Threads, multiprocessing, Go goroutines) → Can they combine? Yes. Async handles waiting. Parallel handles computing. Modern systems use both. Which one do you reach for first in your projects? Drop it below 👇 #programming #javascript #python #softwareengineering #devtips #concurrency #asyncprogramming

🔍 Solved: Search in a 2D Matrix II (LeetCode 240) Today I worked on an interesting problem that looks simple at first but really tests your understanding of matrix properties. Instead of going with brute force or applying binary search on each row, I used the elimination approach to optimize the search. 💡 Key Insight: The matrix is sorted both row-wise and column-wise. So, starting from the bottom-left corner, we can eliminate either: 1. an entire row (if current element > target), or 2. an entire column (if current element < target) This reduces the search space efficiently at every step. ⚡ Approach Highlights: 1. Start from bottom-left 2. Move up if the value is too large 3. Move right if the value is too small ⏱️ Time Complexity: O(m + n) 📦 Space Complexity: O(1) This approach is much more efficient than checking every element and is a great example of how recognizing patterns in sorted data structures can lead to optimal solutions. #DataStructures #Algorithms #DSA #ProblemSolving #CodingInterview #LeetCode #LeetCodeDaily #CodingPractice #SoftwareEngineering #Programming #Coding #Developer #Tech #ComputerScience #CodeNewbie #Cpp #LearningInPublic #100DaysOfCode #CodeEveryday #KeepLearning #TechJourney #SelfImprovement

🧱 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

 Automating Network Lab Testing with Python — IXIA & Velocity  Network validation used to mean hours of manual CLI work and spreadsheet  results. Not anymore.  what are these tools?  IXIA (Keysight IxNetwork)  An industry-standard traffic generator that simulates real-world network  traffic at massive scale — line-rate packet generation across 100G/400G  ports, protocol emulation (BGP, OSPF, LACP), and precision latency/loss  measurement down to nanoseconds. When you need to know exactly how a switch behaves under stress, IXIA network traffic generates that stress and measures the result.  Velocity (Keysight Lab Orchestration)  A lab management platform that handles your physical inventory — switches,  routers, traffic generators. Teams reserve typologies, track equipment, and   automate lab bring-up/tear-down. Think of it as the scheduler and resource   manager for your entire test lab.  With Python connecting both — here's what you get:  🔹 Reserve lab topology via Velocity API — devices booked, cables  confirmed, released automatically when done  🔹 Configure IXIA traffic streams, frame sizes, load %, protocol emulations   — all via ixnetwork_restpy  🔹 Run soak tests (hours or days) unattended — poll results at each  checkpoint, export to Excel/HTML  🔹 Full audit trail — every result timestamped automatically, not scribbled   on a notepad  from ixnetwork_restpy import SessionAssistant # IXIA  import requests  # Velocity REST API  import openpyxl # results export  The real power is the glue — Python sitting between lab orchestration,  traffic generation, and reporting. What used to be a 3-day manual test  cycle becomes a fully automated, repeatable pipeline.  If you're still running network acceptance tests by hand in 2026, there's a   better way. Are you automating your lab testing? What's your stack? Drop it in the comments.  #NetworkAutomation #Python #IXIA #IxNetwork #Velocity #NetworkEngineering  #Automation #DevNetOps #NetworkValidation #Keysight

Just wrapped up a web scraping project and honestly… it still amazes me 🤯 A task that would take days (or even weeks) to do manually-going through pages, copying company details one by one was completed in just a few minutes with Python. That’s the power of programming in today’s world ⚡ We’re living in a time where: Data is everywhere Speed is everything And automation is the real game changer With tools like web scraping, APIs, and automation workflows, what used to be “hard work” is now about working smart. Built a quick scraper that pulled structured company data across multiple pages and turned it into a clean dataset automatically.❤️✨ #WebScraping #Python #Automation #Data #Developers #Programming #Tech #Productivity

 𝐋𝐞𝐞𝐭𝐂𝐨𝐝𝐞 𝐃𝐚𝐢𝐥𝐲 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 – 𝐃𝐞𝐜𝐨𝐝𝐞 𝐭𝐡𝐞 𝐒𝐥𝐚𝐧𝐭𝐞𝐝 𝐂𝐢𝐩𝐡𝐞𝐫𝐭𝐞𝐱𝐭 (𝐌𝐞𝐝𝐢𝐮𝐦) Today’s problem was a 𝐟𝐮𝐧 𝐦𝐚𝐭𝐫𝐢𝐱 𝐭𝐫𝐚𝐯𝐞𝐫𝐬𝐚𝐥 + 𝐬𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞. 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐋𝐢𝐧𝐤 : https://lnkd.in/gsSCrJbr 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐋𝐢𝐧𝐤 : https://lnkd.in/gMhqhD6M The idea behind the encoding process: The original text is written 𝐝𝐢𝐚𝐠𝐨𝐧𝐚𝐥𝐥𝐲 𝐢𝐧 𝐚 𝐦𝐚𝐭𝐫𝐢𝐱 with a fixed number of rows. The matrix is then read row by row to produce the encoded string. To decode it, we reverse the process: 🔹 𝐊𝐞𝐲 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐭𝐢𝐨𝐧𝐬 If n = encodedText.length() and we know rows, then cols = n / rows Characters of the original text lie on diagonals starting from each column in the first row. Traverse diagonally (row++, col++) from each starting column. 🔹 𝐒𝐭𝐞𝐩𝐬 Compute the number of columns. For every column c, traverse diagonally down-right. Map the matrix index using r * cols + c. Build the result string. Remove trailing spaces at the end. 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲 Time: O(n) Space: O(n) 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲: Whenever you see a matrix encoding problem, think about how the data was written and reverse the traversal pattern. #LeetCode #CodingInterview #DataStructures #Algorithms #Java #ProblemSolving #DailyCodingChallenge

While exploring encrypted ML inference, what stood out to me was the lack of a clear, implementation-independent wire contract. Protocol expectations often lived inside particular libraries, toolchains, or deployment workflows rather than being defined as a clear, versioned interface. Without such an interface, validation rules and backend assumptions become coupled, limiting interoperability and making systems harder to reason about, test, and generalize. This project takes a different approach: define a versioned wire contract first, then build a Python reference implementation that shows one correct way to satisfy it. Here’s what’s packed into this release: 🔷 Formal JSON Schema and OpenAPI 3.1 contracts 🔷Plaintext and CKKS-based encrypted inference paths 🔷 Layered validation pipelines for schema, envelope, encoding, and ciphertext compatibility 🔷 A Pyfhel-based reference backend and Python client SDK workflow 🔷 Automated testing & benchmarking I found that the real challenge was getting the architecture right: designing a clean client/server boundary and enforcing layered validation for schema, encoding, and compatibility before execution. Getting here took a refactor 🙂↕️ I also added architecture and API documentation, request examples, and benchmark tooling to make the system easier to understand, optimize and build upon. Check out the repo in the comments! #MachineLearning #MLSystems #PrivacyEngineering #HomomorphicEncryption #Python #FastAPI #SoftwareEngineering

🚀 Efficient Duplicate Detection with Hash Sets | LeetCode Today, I tackled the Contains Duplicate problem. While the brute force approach is often the first instinct, optimizing for time complexity is where the real fun begins! 💡 The Problem: Given an integer array nums, return true if any value appears at least twice in the array, and return false if every element is distinct. ⚡ My Approach: I utilized a Hash Set to track elements as I traversed the array. This allows for near-instantaneous lookups compared to nested loops. 👉 The Logic: Initialize an empty set seen. Iterate through the array once. For each number, check: "Have I seen this before?" (Is it in the set?) If Yes → Return True immediately. If No → Add the number to the set and keep moving. 🔥 Complexity Analysis: ⏱ Time Complexity: $O(n)$ – We only pass through the list once. 📦 Space Complexity: $O(n)$ – In the worst case (all unique elements), we store all $n$ elements in the set. 🏆 The Result: ✔️ Accepted: All 77 test cases passed. ✔️ Performance: 9 ms runtime, beating 73.44% of Python3 submissions! 📌 Key Takeaway: Using a Set turns a potential $O(n^2)$ search into a sleek $O(n)$ operation. Choosing the right data structure isn't just about passing tests; it's about writing scalable, "production-ready" code. 💻 Tech Stack: #Python | #DataStructures | #Algorithms #leetcode #dsa #coding #programming #softwareengineering #100DaysOfCode #pythonprogramming #tech #growthmindset