Automating Utility Auditing with SPC Portal

🤯 𝗬𝗼𝘂𝗿 𝗠𝗤𝗧𝗧 𝗯𝗿𝗼𝗸𝗲𝗿 𝗷𝘂𝘀𝘁 𝗯𝗲𝗰𝗮𝗺𝗲 𝗮 𝗣𝘆𝘁𝗵𝗼𝗻 𝗿𝘂𝗻𝘁𝗶𝗺𝗲. 𝗡𝗼 𝗰𝗹𝗼𝘂𝗱 𝗻𝗲𝗲𝗱𝗲𝗱. I ran Python code directly on an edge device with zero cloud ☁️, zero middleware, zero extra infrastructure. Here's what most engineers don't realize 👇 💡 You don't need a separate server to run analytics logic at the edge. 𝗪𝗶𝘁𝗵 Coreflux's 𝗟𝗼𝗧 (𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗼𝗳 𝗧𝗵𝗶𝗻𝗴𝘀) + 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗲𝗱 𝗮 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗜𝗦𝗢 𝟭𝟬𝟴𝟭𝟲 𝘃𝗶𝗯𝗿𝗮𝘁𝗶𝗼𝗻 𝘀𝗲𝘃𝗲𝗿𝗶𝘁𝘆 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗼𝗻𝘁𝗼 𝗮 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗣𝗶 𝗮𝗻𝗱 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝗹𝗶𝘃𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝘀𝘁𝗿𝗮𝗶𝗴𝗵𝘁 𝘁𝗼 𝗡𝗼𝗱𝗲-𝗥𝗘𝗗. ⚡ Here's the full stack, in one diagram: 🔵 Balluff EMEA IO-Link sensor captures X/Y/Z vibration 🟠 Seeed Studio reComputer runs a virtual PLC runtime 🔴 Revolution Pi runs Coreflux MQTT Broker (Boris Crismancich) 🐍 Python RMS logic executes inside LoT, on the broker itself 📊 Results published to dashboard in milliseconds ✅ No cloud bill. ✅ No latency spike. ✅ No middleware headache. This changes how we think about edge intelligence for industrial machines. 🏭 If you're a PLC engineer wondering whether Python belongs in your stack, it does. And it's easier than you think. 🙌 Full tutorial in the wiki article 👇 https://lnkd.in/d44cSPfu Thanks to Melvin Francis and Hugo Vaz for introducing Coreflux 😍 ---------------------- 💡You can watch it LIVE at HANNOVER MESSE -> Hall 14 L66 😎 ---------------------- #IndustrialIoT #MQTT #EdgeComputing #Coreflux #PLC #Python #IIoT #ConditionMonitoring #RevolutionPi #LOT

We need to talk about context windows, because they are fundamentally changing how we debug. 🗄️ As a Data Manager, I spend a lot of time buried in documentation—whether it’s tracing a weird SQL error across multiple tables or trying to understand a massive new Python library. In the past, you couldn't paste a massive dataset or a giant code repository into an AI without it "forgetting" the first half of your prompt. Enter Gemini 3.1 Pro. With its massive context window, I’m no longer feeding it bite-sized snippets. I’m dropping in entire: API documentation PDFs Multi-layered ETL script logs Massive, messy JSON files It doesn’t just "read" them; it retains the architecture of the problem. It’s like having a senior engineer who has already memorized the entire codebase before you even start the meeting. If you are still using AI just to write single lines of code, you are missing out on its ability to understand the entire system.

We’ve all been there: a manager asks for an inventory or an interface status report, and you’re stuck logging into 20 switches and manually formatting an Excel sheet. In Part 3 of my Nornir series, I’m showing you how to turn that manual chore into a one-click automated workflow: ✅ Parsed Data to CSV: Turn structured TextFSM output into spreadsheets. ✅ Automated Backups: Pulling configs with timestamped filenames. ✅ Error Handling: Making sure one unreachable device doesn't crash your whole script. If you’re ready to move from "running commands" to "generating value," check out the full guide on The Next Hop. https://lnkd.in/eg6hYedd GitHub repository for the series: https://lnkd.in/eiKddxiy #NetworkAutomation #Nornir #Python #NetDevOps #Cisco

🛑 Stop failing your production data loads because of duplicate records. The pattern: Idempotent DELETE+INSERT (the "Atomic Switch" pattern). Here is how to implement it: def sync_data(df, target_table, partition_col, partition_val): with connection.cursor() as cursor: cursor.execute(f"DELETE FROM {target_table} WHERE {partition_col} = '{partition_val}'") df.write.mode("append").format("jdbc").save(table=target_table) connection.commit() Why every pipeline needs this: In distributed systems, retries are inevitable. If your pipeline crashes halfway through a write, a simple "append" creates duplicate data, corrupting your analytics. By wrapping a specific partition delete with an insert in a single transaction, you ensure the output is identical whether the job runs once or ten times. We use this for all our daily batch refreshes to guarantee data integrity without complex staging tables. How do you handle idempotency when your source systems don't provide a natural primary key? #DataEngineering #DataPipeline #Python #ETL #BigData

🚀 Transforming Operational #Data into Strategic #Risk Insights I’ve just finalized a new #Python-based engine designed to optimize KPI performance and automate outlier detection! 📊 In high-volume environments like call centers, identifying systemic risks early is the difference between stability and failure. What’s under the hood? 🛠️ Tools: #Python (#Pandas & #NumPy) for data sanitization and statistical modeling. 🧠 Methodology: Z-Score anomaly detection to isolate performance bottlenecks and technical risks. The result? A modular tool that doesn't just show numbers, but tells a story of operational efficiency and risk mitigation. 🛡️✨ Check out the full code, methodology, and visual reports on my #GitHub repository: <https://lnkd.in/drNyfc6h>

𝗠𝗼𝘃𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗘𝗮𝗴𝗲𝗿 𝘁𝗼 𝗟𝗼𝗴𝗶𝗰𝗮𝗹 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗠𝗟 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 🚀 I’m excited to share that I've just released 𝘃𝟯.𝟭.𝟬 of 𝗱𝘀𝗿-𝗳𝗶𝗹𝗲𝘀, a library focused on high-performance file handling for ML auditing. This update was born out of a "logical epiphany" while refactoring my orchestrator. I realized that a configuration class shouldn't care if a file physically exists—it should only care if the description of that file is logically sound. 𝗪𝗵𝗮𝘁’𝘀 𝗻𝗲𝘄 𝘀𝗶𝗻𝗰𝗲 𝟮.𝟮.𝟬: 🔹𝗦𝗺𝗮𝗿𝘁 𝗧𝘆𝗽𝗲 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: The 𝗙𝗶𝗹𝗲𝗧𝘆𝗽𝗲 enum now performs logical extension checks (e.g., verifying .𝗷𝘀𝗼𝗻𝗹 fits 𝗙𝗶𝗹𝗲𝗧𝘆𝗽𝗲.𝗝𝗦𝗢𝗡) without needing a filesystem connection. 🔹𝗖𝗹𝗼𝘂𝗱-𝗡𝗮𝘁𝗶𝘃𝗲 𝗣𝗮𝘁𝗵𝗶𝗻𝗴: Added native support for S3, GCS, and Azure via 𝗰𝗹𝗼𝘂𝗱𝗽𝗮𝘁𝗵𝗹𝗶𝗯. You can now pass raw URI strings directly to any handler. 🔹𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴: Introduced 𝘀𝗮𝗳𝗲_𝗰𝗮𝗹𝗹=𝗧𝗿𝘂𝗲 to catch and log incompatible keyword arguments, preventing engine-level crashes in 𝗽𝘆𝗮𝗿𝗿𝗼𝘄 or 𝗳𝗮𝘀𝘁𝗽𝗮𝗿𝗾𝘂𝗲𝘁. 🔹𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻-𝗗𝗿𝗶𝘃𝗲𝗻 𝗦𝗮𝗳𝗲𝘁𝘆: Moved to a centralized 𝗬𝗔𝗠𝗟-𝗯𝗮𝘀𝗲𝗱 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝘆. Using a custom 𝗨𝗻𝗶𝗾𝘂𝗲𝗞𝗲𝘆𝗟𝗼𝗮𝗱𝗲𝗿, the library now enforces a "ground truth" for valid engine arguments. 🔹𝗛𝗶𝗴𝗵-𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗖𝗮𝗰𝗵𝗶𝗻𝗴: Implemented 𝗹𝗿𝘂_𝗰𝗮𝗰𝗵𝗲 for parameter retrieval, ensuring that even with strict configuration-driven filtering, I/O overhead remains sub-millisecond. By decoupling logical state from physical environment checks, I’ve made my pipelines more portable and significantly more resilient. Check it out on PyPI: 𝗽𝗶𝗽 𝗶𝗻𝘀𝘁𝗮𝗹𝗹 𝗱𝘀𝗿-𝗳𝗶𝗹𝗲𝘀 #Python #MachineLearning #SoftwareEngineering #MLOps #OpenSource #CloudComputing

Stop the Race: Solving Data Inconsistency in Concurrent Systems Building a "working" application is easy. Building a reliable one is hard. I recently spent time diving into the world of Concurrency and Data Integrity using Python and SQL. One of the most common (and dangerous) bugs in software is the "Race Condition"—where two processes try to update the same data at the same time, leading to "lost updates" and corrupted balances. I simulated a high-traffic banking system to see how data inconsistency happens and, more importantly, how to stop it. The Solution: A Two-Pronged Defense  Application-Level Locking: Using Python’s threading.Lock to create "Mutual Exclusion" (Mutex). This ensures that only one thread can access the critical "Read-Modify-Write" logic at a time.  Database-Level Integrity (ACID): Moving the logic into a relational database (PostgreSQL/SQLite) to leverage Atomicity and Isolation. By using BEGIN, FOR UPDATE, and COMMIT statements, the database acts as the ultimate gatekeeper for data truth. Key Takeaways: Transactions are Non-Negotiable: If it’s not Atomic (all-or-nothing), it’s not safe. The "with" Statement is a Lifesaver: Using context managers in Python ensures locks are released even if the code crashes, preventing deadlocks. Scalability Matters: While local locks work for one server, ACID-compliant databases are essential for distributed systems. Check out the snippet of my GitHub Codespaces setup below! https://lnkd.in/eguenR7g #Python #SoftwareEngineering #SQL #Database #Coding #DataIntegrity #BackendDevelopment #GitHub

Building a Better "Human-in-the-Loop" Audit Trail 🛠️ I’m excited to share the release of 𝚍𝚜𝚛-𝚍𝚊𝚝𝚊-𝚝𝚘𝚘𝚕𝚜 𝘃𝟭.𝟯.𝟬! This version is a major step forward in creating systematic, reproducible model auditing workflows. We’ve moved beyond simple automation to focus on precision and persistence. Key technical milestones include: 🔹5-6× Performance Boost: Refactored data type detection with vectorized NumPy operations, cutting down processing time for large-scale datasets. 🔹Persistent Recommendation Pipelines: Integrated full YAML serialization for the 𝚁𝚎𝚌𝚘𝚖𝚖𝚎𝚗𝚍𝚊𝚝𝚒𝚘𝚗𝙼𝚊𝚗𝚊𝚐𝚎𝚛, allowing you to save, version, and audit every suggested data transformation. 🔹Standardized Serialization: Added 𝚝𝚘_𝚍𝚒𝚌𝚝 logic across the 𝚁𝚎𝚌𝚘𝚖𝚖𝚎𝚗𝚍𝚊𝚝𝚒𝚘𝚗 base class to safely handle Enum-to-string conversions for reliable external exports. 🔹Comprehensive Documentation: Full NumPy-style docstring coverage across the manager and subclasses to support cleaner MLE integration workflows. Check out the documentation and release notes in the first comment! 👇 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚍𝚜𝚛-𝚍𝚊𝚝𝚊-𝚝𝚘𝚘𝚕𝚜 #MachineLearning #Python #MLOps #DataEngineering #OpenSource

🚀 rst-queue v0.1.6: Scaling Terabytes with Megabytes In a world of bloated data systems, we often find ourselves throwing more hardware at software problems. But what if our tools were engineered to be small, grounded, and incredibly powerful? Introducing rst-queue v0.1.6, a high-performance async queue system built for the modern developer who values efficiency above all else. Inspired by the psychology of the Leafcutter Ant, this project is the first major release from the Datarn initiative. Why rst-queue? Most Python-based queues are limited by the Global Interpreter Lock (GIL) and high memory overhead. rst-queue is different. By using Rust and the Crossbeam framework, we’ve built a system that: ⚡ Bypasses the GIL: Achieve true parallelism with native Rust worker pools. 🐜 Microscopic Footprint: 30-50x less memory usage than traditional message brokers. 🛡️ Dual Modes: Choose between AsyncQueue (In-memory for 1M+ items/sec) or the new AsyncPersistenceQueue (Durable storage with Sled KV). Grounded in the Kernel The secret to our speed is "Simple OS Layering." We’ve designed rst-queue to sit as close to the OS kernel as possible, utilizing direct system calls and memory-mapped I/O. This isn't just a library; it's a high-velocity data crossing (Taran) for your most critical applications. Get Started in Seconds We believe in zero-setup excellence. You can add high-performance queuing to your Python project with a single command: Bash pip install rst-queue==0.1.6 Join the Datarn Movement At Datarn, we are building a suite of "Small but Mighty" tools for data-intensive domains like B2B e-commerce and real-time analytics. rst-queue is just the beginning. Explore the project on PyPI: https://lnkd.in/d54yqdea Contribute on GitHub: https://lnkd.in/d_x3E-zj #Python #RustLang #DataEngineering #OpenSource #Efficiency #Datarn #PerformanceOptimization #SoftwareArchitecture

One thing that really helped me understand backend development was building a simple API from scratch. Here’s what clicked for me: 👉 When you call an endpoint like /weather/london, your backend: Receives the request Calls an external API Processes the JSON response Returns only the relevant data Then I took it a step further: ✔ Simulated telemetry data (like rainfall, humidity, solar radiation) ✔ Created dynamic rules to generate alerts ✔ Stored the results in a database (SQLite) ✔ Built filtering endpoints to query the data What I realized is that backend development is not just about code — it’s about: structuring data designing logic connecting systems Still learning, but building things like this makes everything much clearer. #Python #FastAPI #Backend #APIs #Data #Learning