IT System Monitoring Tools

Understanding why metrics move is one of the most important and challenging problems in data-driven organizations. When a key metric suddenly changes, teams need to know why before they can take meaningful action. In a recent tech blog post, Pinterest's engineering team shared how they built a root-cause analysis platform to tackle this exact challenge. The system combines three complementary approaches — Slice and Dice, General Similarity, and Experiment Effects — each uncovering a different layer of insight into metric changes. - The Slice and Dice approach focuses on breaking down metrics across different dimensions — like geography, device type, or user segment — to pinpoint where the change is happening. It’s an intuitive yet powerful way to quickly surface meaningful patterns. - The General Similarity approach looks across historical data to identify whether similar movements have happened before. By comparing current shifts to past patterns, the system can suggest potential causes that previously explained similar trends. - Finally, Experiment Effects integrates information from ongoing A/B tests and feature rollouts, helping teams understand whether an experiment might be responsible for a particular metric movement. Together, these methods form a comprehensive analytical workflow that blends data science, engineering, and product understanding. The result is a system that helps teams move beyond what changed — to why it changed — enabling faster and more confident decision-making. #DataScience #Analytics #RootCauseAnalysis #Experimentation #MachineLearning #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gncpBNMm

Integrating PLCs from different vendors sounds completely straightforward on paper. Well… it’s not. In reality, it’s one of the most consistently painful parts of industrial automation. Every manufacturer speaks its own dialect, and somehow the translation issues always end up on your desk. Before you write a single line of logic, map out the communication protocols. Know exactly who speaks what — and what translators (gateways, OPC UA, etc.) you’ll need — before the chaos begins. Here’s a quick overview of the most common platforms and the languages they primarily “speak”: Common PLC Platforms & Their Native/Preferred Protocols • Rockwell Automation (Allen-Bradley): EtherNet/IP (primary), ControlNet, DeviceNet, DF1 (serial) • Siemens: PROFINET (primary), PROFIBUS, S7 communication • Schneider Electric (Modicon): Modbus (RTU/TCP – very common), EtherNet/IP support, PROFINET in some models • Mitsubishi: MELSEC (various, including CC-Link IE), Modbus TCP/RTU • Omron: EtherNet/IP, EtherCAT, FINS, Modbus • Beckhoff: EtherCAT (strong native), EtherNet/IP, PROFINET support Universal bridges that often save the day: • Modbus TCP/RTU — the “English” of the automation world (widely supported) • OPC UA — modern, secure, and increasingly the go-to for cross-vendor integration • EtherNet/IP — very common in North America Pro tip: Even when two systems claim to support the same protocol (like EtherNet/IP or PROFINET), implementation differences can still cause headaches. Always test early and have a solid gateway or protocol converter ready as backup. Save yourself the pain — protocol mapping first, logic later.

Kotlin Multiplatform — Shared Logic Across Platforms Kotlin Multiplatform (KMP) is an approach that allows sharing business logic across different platforms while still keeping native UI and platform-specific layers. It enables developers to write common code once and reuse it on: Android iOS Web Desktop The main idea is to reduce duplication in areas like: Networking Data handling Business/domain logic UI remains native for each platform (Jetpack Compose for Android, SwiftUI/UIKit for iOS, etc.), which keeps the platform experience consistent. KMP can be integrated gradually into existing projects, allowing teams to adopt it module by module based on need. It fits use cases where: Apps target multiple platforms Core logic should be aligned across platforms Teams want to maintain one source of truth for domain and data layers Compose Multiplatform is an optional addition that allows sharing some UI when appropriate, mainly for desktop and web.

Interoperability is not a Platform, It’s an Evolving Capability: Step-by-Step Roadmap for Data Interoperability
 Fresh, practical, and aligned with modern tech trends   1. Diagnose the Data Disconnect Why it matters: Understand where integration fails and what it costs the business. Actions: -Use data lineage tools (e.g., Collibra, Alation) to auto-map data silos, legacy connectors, and flow bottlenecks. -Run a maturity diagnostic focused on governance, quality, and system interoperability. -Pinpoint root causes like format mismatches (XML vs. JSON), brittle ETL, or API fragmentation.   Outcome: Heatmap of friction points tied to real-world impact (e.g., delayed closings, NPS drop).   2. Anchor Interoperability to Business Objectives Why it matters: No point fixing pipes unless it fuels outcomes that matter.   Actions: -Align with business imperatives: e.g., real-time 360, ESG reporting, IoT-led efficiency. -Use OKRs for precision targeting. Objective: Cut reconciliation time by 70%. Key Result: Adopt FHIR for patient data or AGL for vehicle telemetry.   3. Architect for Flexibility and Scale Why it matters: Interoperability is not a platform, it’s an evolving capability.   Options: -Data Mesh: Empower domains with ownership and APIs (e.g., supply chain owning SKU data products). o  Tools: Starburst Galaxy, Confluent. -Data Fabric: Auto-discover and govern with ML-driven metadata (e.g., CLAIRE). -Infrastructure: o  Cloud-native + serverless (AWS Lambda, Azure Synapse). o  Edge-first for latency-sensitive IoT workloads.   4. Standardize with Open APIs Why it matters: Without shared protocols, integration becomes brittle and expensive.   Actions: -Enforce open standards: o  Healthcare: FHIR + SMART. o  Manufacturing: MTConnect. o  Global: JSON-LD. -Build API-first ecosystems: o  Use GraphQL for dynamic querying, AsyncAPI for event-driven models. -Use smart gateways (Apigee, Kong, Azure API Management with AI security).   5. Leverage AI for Intelligent Interoperability Why it matters: Manual mapping can’t keep pace, automation is non-negotiable.   Actions: -Use Gen AI to auto-map schemas (e.g., CSV → FHIR-compliant JSON). -Deploy ML-driven data quality tools (Monte Carlo, Great Expectations). -Accelerate integration using low-code platforms like Power Automate.   6. Embed Federated Data Governance Why it matters: Centralized governance slows agility. Federated = control with speed.   Actions: -Assign Data Product Owners for accountability. -Automate policy enforcement (Policy-as-Code). -Apply zero-trust sharing (e.g., Immuta, Okta).   7. Pilot Fast, Prove Value, Scale Hard Why it matters: Show early ROI to unlock buy-in and budget.   Actions: -Pick high-ROI pilots (e.g., CRM-Marketing integration). -Track KPIs: Latency <100ms, error rate <1%, adoption >80%. -Scale using Agile sprints and replicate via IaC (Terraform).     Continue in first comment.   Transform Partner – Your Strategic Champion for Digital Transformation   Image Source: MDPI

𝗢𝗻𝗲 𝗰𝗼𝗱𝗲𝗯𝗮𝘀𝗲. 𝗙𝗼𝘂𝗿 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗹𝗲𝗮𝗿𝗻𝗲𝗱 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗿𝗲𝗮𝗹 𝗞𝗠𝗣 𝗽𝗿𝗼𝗷𝗲𝗰𝘁. 🧑💻 Just launched a Multi-Category POS system running on Windows, Android, iOS, and Web. The catch? One codebase for everything. Tech stack:  • Kotlin Multiplatform (KMP)  • Compose Multiplatform  • MVVM + Clean Architecture  • Shared logic, native performance 𝗪𝗵𝘆 𝗞𝗠𝗣 𝘄𝗼𝗻 𝗳𝗼𝗿 𝘁𝗵𝗶𝘀 𝗽𝗿𝗼𝗷𝗲𝗰𝘁: Most cross-platform tools force compromises. Flutter gives you custom UI but struggles with native integrations. React Native is JavaScript on mobile (not my preference). Xamarin is... well, dying. KMP? Write Kotlin once, deploy everywhere, keep native when you need it. 𝗪𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗴𝗼𝘁 𝘀𝗵𝗮𝗿𝗲𝗱: → Business logic (100%) → Data models (100%) → API calls & networking (100%) → Database layer (100%) → UI (80% with Compose Multiplatform) 𝗪𝗵𝗮𝘁 𝘀𝘁𝗮𝘆𝗲𝗱 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰: → Some native system integrations → Platform-specific optimizations 𝗧𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀: Traditional approach: 4 separate codebases = 4x development time KMP approach: 1 codebase = 60% less code, 50% faster delivery Real challenges I faced:  • Kotlin Multiplatform is different when it comes to DI, Networking.  • Some libraries aren't KMP-ready yet  • Platform-specific debugging can be tricky Worth it? Absolutely. One team. One codebase. Four platforms. Native performance everywhere. This is the future of cross-platform development. 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝗶𝗻𝗴 𝗞𝗠𝗣 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗻𝗲𝘅𝘁 𝗽𝗿𝗼𝗷𝗲𝗰𝘁? 𝗔𝘀𝗸 𝗺𝗲 𝗮𝗻𝘆𝘁𝗵𝗶𝗻𝗴 𝗯𝗲𝗹𝗼𝘄. 💬 #KotlinMultiplatform #KMP #MobileDevelopment #CrossPlatform #ComposeMultiplatform #Android #iOS #SoftwareEngineering

Nine Essential Integration Patterns for Software Architecture Platform scalability means increasing computational resources and optimizing inter-service communication. This guide outlines integration patterns that enhance system reliability and specifies appropriate use cases for each. Streaming Processing Continuous event streams enable near real-time processing. This pattern is particularly effective for telemetry, dynamic pricing, fraud detection, and clickstream analytics. Batching Batch processing groups tasks and executes them at scheduled intervals to optimize resources. This approach is suitable for nightly settlements, large-scale data exports, and complex data transformations. Publish and Subscribe In the publish-subscribe pattern, a producer transmits a message once, allowing multiple consumers to process it independently. This approach decouples systems and supports multi-destination notifications without direct dependencies. ETL The extract, transform, and load (ETL) process consolidates data from applications and databases into centralized repositories such as data warehouses or lakes. ETL is essential for business intelligence, regulatory compliance, and long-term analytics. Event Sourcing Event sourcing persists a chronological sequence of events, enabling system state reconstruction as needed. This pattern supports auditability, historical data analysis, and recovery after system defects. Request and Response The request-response pattern uses direct, synchronous communication between services. It is effective for simple data retrieval, idempotent write operations, and user-facing application programming interfaces (APIs). Peer to Peer The peer-to-peer pattern enables direct communication between services. This approach is best when minimizing latency is critical and service ownership and contracts are clearly managed. Orchestration Orchestration uses a central workflow to coordinate multiple services, manage retries, and address failures. This pattern is suitable for extended business processes that require comprehensive oversight. API Gateway An application programming interface (API) gateway provides a unified entry point for system access, managing authentication, rate limiting, routing, and protocol translation. This pattern standardizes access and enforces policies at the system boundary. Select the integration pattern that best aligns with system requirements for performance, reliability, and cost efficiency. Most architectures use a combination of two or three patterns, with effective teams monitoring their effectiveness. Follow Umair Ahmad for more insights #SystemDesign #Architecture #Microservices #APIs #EventDriven #DataEngineering #Streaming #CloudComputing 

A single update—and suddenly, all my AI integrations broke. Two weeks of patching, testing, and frustration later, I thought: there has to be a better way. That’s when I discovered 𝗠𝗖𝗣. Imagine connecting any AI model to any tool with just one simple setup. Let me save you time for long research: MCP is an open protocol using JSON-RPC that standardizes how AI models interact with external tools and data sources. When Anthropic said, "Let's unify how AI connects to tools via a shared protocol," the industry listened. And for good reason. 𝗧𝗵𝗲 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 (𝗕𝗲𝗳𝗼𝗿𝗲 𝗠𝗖𝗣): • LLMs used conflicting integration methods • Custom adapters needed for every app-tool pair (M apps × N tools = M×N integrations) • Engineering teams wasted time by reinventing the same connections • Maintenance nightmare as platforms evolved 𝗧𝗵𝗲 𝗠𝗖𝗣 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: ✅ Build one MCP server for your tool ✅ Works with any MCP-compatible AI app (Claude, GPT, etc.) ✅ Only M + N integrations needed total ✅ AI models dynamically discover available tools via the protocol Here's the beautiful part: If you understand JSON and basic APIs, you're already 80% ready for MCP. What used to take development teams weeks now takes hours. Connect databases, APIs, or internal tools through real-time bidirectional communication using a single standardized protocol. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻: → Build once, integrate everywhere (zero platform-specific code) → Tools work instantly with all current AND future MCP-compatible AI systems → Teams focus on core functionality instead of maintaining fragile adapters → Reduced technical debt and faster time-to-market Instead of reading 50+ different integration docs, you just learn one simple standard. Instead of managing fragmented connections, you maintain one standards-compliant server. MCP isn't just solving today's integration headaches – it's future-proofing AI tool connectivity. Have you explored MCP's dynamic tool discovery capabilities yet? What's your biggest AI integration challenge? #MCP #AIIntegration #DeveloperTools #AgenticAI #AI

Can integration hubs save the day? In the near to mid-term, any full-service 3-4-5-star hotel will need over 100 plus APIs (application programming interface) with third-party tech applications and solutions to be able to function and meet the basic needs and wants of today's tech-savvy travelers. These include AI-enabled and powered applications like Agentic AI and chatbots, contactless guest experience, mobile locks, issue resolution apps, guest messaging, virtual concierge, IoT devices and utility management, smart room technology, entertainment hubs, CRM programs, CRs, RMS, Channel Managers, etc. LLMs like ChatGPT and Google's Gemini need to enable Personal AI Agents to communicate and do a handshake with hoteliers' own AI Agents or with AI connectivity middleware platforms like the Model Context Protocol (MCP) and Agent-to-Agent (A2A). There are over 5,000 established hotel tech vendors around the world working around the clock to develop new and innovative solutions to common problems or applications to elevate service delivery in hotel operations, guest communications, revenue management, marketing, etc. Historically, to access these much-needed third-party solutions and applications required lengthy and expensive integrations that "had the effect of dissuading hoteliers from actively seeking out tools that would enhance their business and the guest experience, because they knew that even if they found a great solution, integrating it would feel like more trouble than it's worth," as per Mews PMS CEO Matt Welle. So, what is the solution? Can integration hubs, PMS-related or third-party Integration hubs, step in and solve this urgent industry need? Luckily for our industry, the solution is already here in the form of two types of third-party technology integration platforms: Cloud PMS with Open API like Opera Cloud PMS, StayNTouch, Protel, CloudBeds, Mews, etc. and their integration platforms, and Independent integration hubs, like APS, NoniusHub, SiteMinder, Impala, IreconU, Hapi, and the new type of AI connectivity middleware hubs like the Model Context Protocol (MCP) and Agent-to-Agent (A2A). I believe the PMS-centric tech stack will continue to dominate hotel technology in the future, but what kind of a PMS? A cloud PMS with its Open API and integration hub that solves the problem of connecting to the myriad of third-party applications, in addition to lower upfront costs, efficiencies, higher productivity and data security. Good examples: The Oracle Hospitality Integration Platform (OHIP) with 3,000 API capabilities, StayNTouch Integration Hub with 1,100 APIs; Protel Air PMS Marketplace - 1,000 APIs, Cloudbeds PMS - 300 APIs, apaleo PMS Store, etc. Accor adopted Opera Cloud citing its OHIP platform as one of the main benefits of their decision. What should the 650,000 hotels with legacy PMS or no PMS at all do? I see two options: * Switching to a cloud PMS * Partnering with a third-party integration hub.

The hardest question in data modeling isn't how to store data. It's how to store what data 𝘂𝘀𝗲𝗱 𝘁𝗼 𝗯𝗲. As a data engineer, you've likely faced this moment: a stakeholder asks "what was this customer's tier last quarter?" — and you realize your pipeline only kept the latest state. 😬 History is easy to need and painful to retrofit. I've watched teams burn months rebuilding pipelines they thought were "good enough." Don't be that team. Before you pick a pattern, consider these questions: - Is history for 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝘂𝘀𝗲 or 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝗮𝗹 𝘂𝘀𝗲? - Do you need 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗰𝗵𝗮𝗻𝗴𝗲 𝗽𝗿𝗼𝗽𝗮𝗴𝗮𝘁𝗶𝗼𝗻? - Can you accept 𝗲𝘃𝗲𝗻𝘁𝘂𝗮𝗹 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆? - Does this domain truly need a 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲, 𝗱𝘂𝗿𝗮𝗯𝗹𝗲 𝗵𝗶𝘀𝘁𝗼𝗿𝘆 as its system of record? Your answers will guide you to one of these 👇 𝗘𝘃𝗲𝗻𝘁 𝗦𝗼𝘂𝗿𝗰𝗶𝗻𝗴 Your system of record IS the sequence of events. State is derived, not stored. → 𝗨𝘀𝗲 𝘁𝗵𝗶𝘀 𝘄𝗵𝗲𝗻: You need bulletproof auditability — financial ledgers, compliance-heavy workflows. Overkill for most everything else. 𝗦𝘁𝗮𝘁𝗲-𝗳𝗶𝗿𝘀𝘁 + 𝗔𝘂𝗱𝗶𝘁 𝗟𝗼𝗴𝗴𝗶𝗻𝗴 / 𝗖𝗗𝗖 𝗟𝗲𝗱𝗴𝗲𝗿 Keep your traditional state model, but persist a change log alongside it. → 𝗨𝘀𝗲 𝘁𝗵𝗶𝘀 𝘄𝗵𝗲𝗻: You want recoverability and traceability without the operational weight of full event sourcing. The sweet spot for most teams. 𝗦𝗖𝗗 𝗧𝘆𝗽𝗲 𝟮 When an attribute changes, close out the old row and open a new one with effective dates. → 𝗨𝘀𝗲 𝘁𝗵𝗶𝘀 𝘄𝗵𝗲𝗻: You're building for analytics historization in a warehouse. Classic for a reason — but keep it in the warehouse. 𝗖𝗗𝗖 + 𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗲𝗱 𝗟𝗲𝗱𝗴𝗲𝗿 + 𝗗𝗲𝗿𝗶𝘃𝗲𝗱 𝗦𝘁𝗮𝘁𝗲 Capture changes as they happen, persist them as an immutable ledger, materialize current state as a derived view. → 𝗨𝘀𝗲 𝘁𝗵𝗶𝘀 𝘄𝗵𝗲𝗻: You need near-real-time replication with the flexibility to replay. Powerful, but you better have the team to maintain it. 𝗖𝘂𝗿𝗿𝗲𝗻𝘁-𝗦𝘁𝗮𝘁𝗲 𝗧𝗮𝗯𝗹𝗲𝘀 / 𝗣𝗲𝗿𝗶𝗼𝗱𝗶𝗰 𝗦𝗻𝗮𝗽𝘀𝗵𝗼𝘁𝘀 A nightly snapshot or a few audit columns (created_at, updated_at). → 𝗨𝘀𝗲 𝘁𝗵𝗶𝘀 𝘄𝗵𝗲𝗻: Simplicity wins. Not every table needs history — and this covers more requirements than you'd think. Seriously, start here. I mapped these decision points into the flowchart below 👇 The goal isn't to pick the most sophisticated pattern — it's to pick the simplest one that won't leave you rebuilding later. Complexity you don't need is debt you'll pay forever. What's the most expensive "we should've captured history" lesson you've learned? 👇 Bonus: Fivetran has "History" mode for Data Lakes which enables SCD2 with just a click! It's pretty sweet. Check out Managed Data Lakes to see it in action. #dataengineering #dataarchitecture #data