Edge Computing Applications

Why do Instagram Reels load faster than your own phone gallery videos?” You shoot a 4K video on your phone. It lags when you open it in your gallery. But open the same clip on Instagram… it plays instantly. Ever wondered how? 🧠 The Secret: Instagram doesn’t show you the video. It shows you an illusion of one. Here’s how: 1️⃣ Multi-pass Encoding When you upload a reel, Instagram doesn’t compress it once. It compresses it 3–5 times — at different bitrates and resolutions. Each version is optimized for a specific network condition. So if you’re on weak 4G, Instagram auto-switches to a lighter stream without buffering. That’s Dynamic Bitrate Streaming, powered by DASH (Dynamic Adaptive Streaming over HTTP) 2️⃣ Chunked Playback Instead of loading the full video, Instagram splits it into small 2–3 second chunks. Only the first few chunks load instantly — that’s why playback feels “instant”. Meanwhile, the rest buffers quietly in the background. 3️⃣ Predictive Prefetching Instagram knows your scrolling patterns. If you pause for 2 seconds, it starts preloading the next 2 Reels. So when you scroll, boom — they’re already in cache. 4️⃣ Hardware-Aware Encoding On iPhones and Androids, Instagram uses device-specific encoding. Your video is reprocessed based on your phone’s chipset and decoder capabilities. Result: better quality at smaller file sizes. 5️⃣ Edge Delivery via CDN All this magic is useless without speed. Instagram stores these compressed video chunks across Meta’s global CDN. Your video isn’t being fetched from California — it’s coming from a node maybe 5 km away. That’s how “instant” really works 💡 Lesson: People think Instagram is a “video app”. In reality, it’s a real-time distributed compression and caching system— wrapped in a dopamine loop.

There are mainly three communication patterns that form the backbone of modern distributed systems. Understanding these patterns is crucial for any software architect or engineer working on scalable applications. Let's dive into these patterns and explore how they shape our systems: 1. Synchronous Communication • Direct, real-time interaction between services • Client initiates request through API Gateway • Services communicate sequentially (A → B → C) • Uses HTTP Sync at each step • Pros: Simple, immediate responses • Cons: Can create bottlenecks, potential for cascading failures Ideal for: Operations requiring immediate, consistent responses 2. Asynchronous One-to-one • Utilizes message queues for communication • Client sends request to API Gateway • Services listen to and receive from queues • Allows for decoupled, non-blocking operations • Pros: Better load handling, fault tolerance • Cons: More complex, eventual consistency Ideal for: High-load scenarios, long-running processes 3. Pub/Sub (Publish/Subscribe) • Employs a central topic for message distribution • Client interacts with API Gateway • Multiple services can subscribe to a single topic • Enables one-to-many communication • Pros: Highly scalable, great for event-driven architectures • Cons: Can be complex to manage, potential message ordering issues Ideal for: Event broadcasting, loosely coupled systems Key Considerations When Choosing a Pattern: • Scalability requirements • Response time needs • System coupling preferences • Fault tolerance and reliability • Complexity of implementation and maintenance The art of system design often involves skillfully combining these patterns to create robust, efficient, and scalable distributed systems. Each pattern has its strengths, and the best architects know how to leverage them for optimal performance. Which pattern do you find most useful ? How do you decide which to use in different scenarios?

𝗔𝗻𝗸𝗶𝘁𝗮: You know 𝗣𝗼𝗼𝗷𝗮, last Monday our new data pipeline was live in cloud and it failed terribly. Literally had an exhaustive week fixing the critical issues. 𝗣𝗼𝗼𝗷𝗮: Ohh, so don’t you use Cloud monitoring for data pipelines? From my experience always start by tracking these four key metrics: latency, traffic, errors, and saturation. It helps you to check your pipeline health, if it's running smoothly or if there’s a bottleneck somewhere.. 𝗔𝗻𝗸𝗶𝘁𝗮: Makes sense. What tools do you use for this? 𝗣𝗼𝗼𝗷𝗮: Depends on the cloud platform. For AWS, I use CloudWatch—it lets you set up dashboards, track metrics, and create alarms for failures or slowdowns. On Google Cloud, Cloud Monitoring (formerly Stackdriver) is awesome for custom dashboards and log-based metrics. For more advanced needs, tools like Datadog and Splunk offer real-time analytics, anomaly detection, and distributed tracing across service. 𝗔𝗻𝗸𝗶𝘁𝗮: And what about data lineage tracking? How do you track when something goes wrong, it's always a nightmare trying to figure out which downstream systems are affected. 𝗣𝗼𝗼𝗷𝗮: That's where things get interesting. You could simply implement custom logging to track data lineage and create dependency maps. If the customer data pipeline fails, you’ll immediately know that the segmentation, recommendation, and reporting pipelines might be affected. 𝗔𝗻𝗸𝗶𝘁𝗮: And what about logging and troubleshooting? 𝗣𝗼𝗼𝗷𝗮: Comprehensive logging is key. I make sure every step in the pipeline logs events with timestamps and error details. Centralized logging tools like ELK stack or cloud-native solutions help with quick debugging. Plus, maintaining data lineage helps trace issues back to their source. 𝗔𝗻𝗸𝗶𝘁𝗮: Any best practices you swear by? 𝗣𝗼𝗼𝗷𝗮: Yes, here’s what’s my mantra to ensure my weekends are free from pipeline struggles - Set clear monitoring objectives—know what you want to track. Use real-time alerts for critical failures. Regularly review and update your monitoring setup as the pipeline evolves. Automate as much as possible to catch issues early. 𝗔𝗻𝗸𝗶𝘁𝗮: Thanks, 𝗣𝗼𝗼𝗷𝗮! I’ll set up dashboards and alerts right away. Finally, we'll be proactive instead of reactive when it comes to pipeline issues! 𝗣𝗼𝗼𝗷𝗮: Exactly. No more finding out about problems from angry business users. Monitoring will catch issues before they impact anyone downstream. In data engineering, a well-monitored pipeline isn’t just about catching errors—it’s about building trust in every insight you deliver. #data #engineering #reeltorealdata #cloud #bigdata

Processing billions of #events in real-time at #Twitter 😯 😯 😯 Approximately 400 billion events in real-time and generates petabyte (PB) scale data every day. To process those types of data in those sources and platforms, the Twitter Data Platform team has built internal tools like Scalding for batch processing. ⚫ Old Architecture: The old architecture had a lambda architecture with both batch and real-time processing pipelines, built within the Summingbird Platform and integrated with TSAR. The real-time data is stored in the Twitter Nighthawk distributed cache, and batch data is stored in Manhattan distributed storage systems. They have a query service to access the real-time data from both stores, used by customer services. ⚫ Challenge: Because of the high scale and high throughput of data they process in real-time, there can be data loss and inaccuracy for real-time pipelines. To overcome this data loss issue, reduce system latency, and optimize the architecture, they propose to build pipelines in kappa architecture to process the events in streaming-only mode. ⚫ New Architecture: The new architecture is built on both Twitter Data Center services and Google Cloud Platform. On-premise, they built preprocessing and relay event processing which converts Kafka topic events to pubsub topic events with at-least-once semantics. On Google Cloud, they used streaming Dataflow jobs to apply deduping and then perform real-time aggregation and sink data into BigTable. Know more: https://lnkd.in/dCwXw9Vc

Ever Wonder How Cities Predict and Prevent Traffic Jams Before They Happen? 🚦 The answer lies in Digital Twin Cities – dynamic, data-rich virtual replicas of our city environments. A live, interactive command center. Here's your streamlined workflow for smart transportation: 1️⃣ Data Foundations Gather Data from Real-time traffic sensors (JSON/XML streams), vehicle GPS, public transport feeds, and weather APIs. 2️⃣ Standardise your City DT for Interoperability Use GeoJSON for features like road networks, zones and CityGML for rich, semantic 3D city models with buildings, vegetation, and transport infrastructure. Use IFC for BIM-specific assets like bridges, train stations. 3️⃣ Create the Core Digital Twin Platform A central meta hub (often cloud-based) manages this standardized data using spatial capabilities. This happens only (!) in the gaming engine's DT capability tables. 4️⃣ Model Assets & Relationships Create digital representations of roads, signals, vehicles, etc., and define their interactions. 5️⃣Gaming Engine Meta-Layer Import your data (CityGML, IFC translated to FBX/glTF; GeoJSON mapped) into the gaming engine. Ideally have a plug-in mode for your data through FME - your feature manipulation engine. I call it the "Swiss Knife" 😇 6️⃣Real-Time Dynamics Connect your live data streams to animate the 3D scenes you created (e.g., vehicle movement, traffic signal status). Have your Interactive UIs. This is your visualized data dashboard - for querying data, controlling simulations, and visualizing the insights you want to have. 7️⃣The Gold Standard - AI-Powered Insights "What-If" Scenarios: Model impacts of road closures, signal timing changes, within the gaming engine. Apply AI to forecast congestion and optimize traffic flow dynamically. Twins are never truly finished. As a city, establish this solid foundation from the beginning to avoid finding yourself in a dead end down the road. #SmartCities #CityGML #GeoJSON If you find this helpful... ----------- Follow Me for #digitaltwins Links in My Profile Florian Huemer

Virtual reality is not just a tool for entertainment but a game-changer in product design, allowing teams to experiment, refine, and collaborate remotely in ways that were once impossible, leading to faster innovation, cost reductions, and more precise manufacturing outcomes. Immersive 3D environments are transforming product design by eliminating physical constraints and allowing real-time iteration. Virtual prototyping enables companies to test designs without manufacturing costly models, reducing waste and accelerating development. Interactive visualization helps engineers refine products before production, leading to better ergonomics and functionality. Remote collaboration means teams across continents can work seamlessly, breaking traditional logistical barriers. Realistic product previews enhance customer trust and decision-making, particularly in industries like architecture, automotive, and consumer electronics, where accurate representations are crucial for investments and sales. #VirtualReality #3DDesign #ProductDevelopment #RemoteCollaboration #DigitalTransformation

Edge is not a trend; it’s an architecture shift. From $10B in 2023 to $50B+ by 2033... ...the growth isn’t driven by hype. It’s driven by physics. Because once you move from 100 ms to 20 ms, apps feel usable. But to cross 5 ms? You need to compute at the baseband, not the core. Here’s how to engineer edge sites that deliver deterministic low latency.. ...the kind autonomous vehicles, high-frame-rate AR, and critical IoT actually depend on: 1️⃣ Deploy true micro-edge, not retrofitted closets. Use prefabricated, hardened SmartMod™ units from Schneider Electric. Each is factory-integrated for power, cooling, fire, and control. Drop next to STC, Du, or Airtel 5G towers. Size them in 50 kW increments, enough for MEC, AI inference, or on-prem cloud functions. 2️⃣ Terminate fibre and power before you lift a panel. Edge buildouts fail when backhaul and power provisioning lag site readiness. Lock dual feeds (utility + genset), reserve dark fibre with SLA-bound loop latency. Tie telemetry into a regional NOC using EcoStruxure™ IT Expert. 3️⃣ Architect for adversarial environments. At edge, risk profiles flip. You’re no longer behind seven enterprise firewalls. Implement zero-trust gateways at entry points. Segment IoT ingress from control networks. Deploy biometric access control per rack, not just facility. 4️⃣ Design for thermal density and burst load. Run average loads at 65–70% to preserve thermal headroom. Plan cooling for non-linear spikes from MEC caching or edge GPU workloads. Active airflow control, rear-door heat exchangers, or liquid-ready chassis, depending on density. 5️⃣ Treat orchestration as a control system, not a dashboard. With EcoStruxure™, power, cooling, access, and IT converge into a decisioning plane. Don’t just monitor, let the system act. Use real-time data to preempt failure, not just alarm on it. This isn’t edge as a PoC. This is production-grade, SLA-bound, carrier-integrated infrastructure. 5G gives you bandwidth. Edge gives you responsiveness. Without both, your low-latency promise doesn’t land. Ready to design for 5 ms? Let’s draw your first edge map.

𝗗𝗼𝗻’𝘁 𝗝𝘂𝘀𝘁 𝗥𝗲𝗮𝗱 𝗔𝗯𝗼𝘂𝘁 𝗔𝗜 𝗶𝗻 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴. 𝗔𝗽𝗽𝗹𝘆 𝗜𝘁. The AI headlines are exciting. But if you're a founder, engineer, or educator in manufacturing, here's the question that actually matters: 𝗪𝗵𝗮𝘁 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗱𝗼 𝘵𝘰𝘥𝘢𝘺 𝘁𝗼 𝘁𝘂𝗿𝗻 𝘁𝗵𝗲𝘀𝗲 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻𝘁𝗼 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻? Let’s get tactical. 𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗱𝗲𝗺𝗮𝗻𝗱 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 Tool to try: Lenovo’s LeForecast A foundation model for time-series forecasting. Trained on manufacturing-specific datasets. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re battling supply chain volatility and need better inventory planning. 👉 Tip: Start by connecting your ERP data. Don’t wait for perfect integration: small wins snowball. 𝟮. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝘄𝗶𝗻 𝗯𝗲𝗳𝗼𝗿𝗲 𝗯𝘂𝘆𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗻𝗲𝘅𝘁 𝗿𝗼𝗯𝗼𝘁 Tools behind the scenes: NVIDIA Omniverse, Microsoft Azure Digital Twins Schaeffler + Accenture used these to simulate humanoid robots (like Agility’s Digit) inside full-scale virtual factories. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re considering automation but can’t afford to mess up your live floor. 👉 Tip: Simulate your current workflows first. Even without a robot, you’ll find inefficiencies you didn’t know existed. 𝟯. 𝗕𝗿𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗤𝗔 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝟮𝟬𝟮𝟬𝘀 Example: GM uses AI to scan weld quality, detect microcracks, and spot battery defects: before they become recalls. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re relying on spot checks or human-only inspections. 👉 Tip: Start with one defect type. Use computer vision (CV) models trained with edge devices like NVIDIA Jetson or AWS Panorama. 𝟰. 𝗘𝗱𝗴𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗻𝘆𝗺𝗼𝗿𝗲 Why it matters: If your AI system reacts in seconds instead of milliseconds, it's too late for safety-critical tasks. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You're in high-speed assembly lines, robotics, or anything safety-regulated. 👉 Tip: Evaluate edge-ready AI platforms like Lenovo ThinkEdge or Honeywell’s new containerized UOC systems. 𝟱. 𝗕𝗲 𝗲𝗮𝗿𝗹𝘆 𝗼𝗻 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 The EU AI Act is live. China is doubling down on "self-reliant AI." The U.S.? Deregulating. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You're deploying GenAI, predictive models, or automation tools across borders. 👉 Tip: Start tagging your AI systems by risk level. This will save you time (and fines) later. Here are 5 actionable moves manufacturers can make today to level up with AI: pulled straight from the trenches of Hannover Messe, GM's plant floor, and what we’re building at DigiFab.ai. ✅ Forecast with tools like LeForecast ✅ Simulate before automating with digital twins ✅ Bring AI into your QA pipeline ✅ Push intelligence to the edge ✅ Get ahead of compliance rules (especially if you operate globally) 🧠 Each of these is something you can pilot now: not next quarter. Happy to share what’s worked (and what hasn’t). 👇 Save and repost. #AI #Manufacturing #DigitalTwins #EdgeAI #IndustrialAI #DigiFabAI

What connects Industrial IoT, Application and Data Integration, and Process Intelligence? During my time at Software AG, my attention has shifted in line with the company's strategic priorities and the changing needs of the market. My focus on Industrial IoT, moved into Application and Data Integration, and now I specialise on Business Process Management and Process Intelligence through ARIS. While these areas may appear to address different challenges, a common thread runs through them. Take a typical production process as an example. From raw material intake to finished goods delivery, there are countless interdependencies, processes and workflows, and just as many data sources. Industrial IoT plays a key role by capturing real-time data from machines and sensors on the shop floor. This data provides visibility into equipment performance, production rates, energy usage, and more. It enables predictive maintenance, reduces downtime, and supports continuous improvement through real-time monitoring and analytics. Application and Data Integration brings together data from across the value chain, including sensor data, manufacturing execution systems, ERP platforms, quality management systems, logistics, and supply chain management. Synchronising these systems with integration creates a unified, reliable view of production operations. This cohesion is essential for automation, traceability, quality management and responsive decision-making across departments and geographies. Process Management, including modelling, and governance, risk, and controls, takes a different yet equally critical perspective. Modelling helps design optimal process flows, while governance frameworks ensure controls are in place to manage quality, risk, and enforce conformance for standardisation. Process mining uncovers bottlenecks, rework loops, and compliance deviations. It focuses on how the production process actually runs, rather than how it was designed to operate. Despite their different vantage points, each of these domains works toward the same goal: aggregating, normalising, and structuring data to transform it into information that can be easily consumed to create meaningful, actionable insights. If your organisation is capturing process-related data through isolated tools, such as diagramming or collaboration platforms, quality management systems, risk registers, or role-based work instructions, it is likely you are only seeing part of the picture. Without a unified approach to integrating and analysing this data, the deeper insights remain fragmented or out of reach. By aligning physical operations, applications & systems, and business processes, organisations can move beyond surface-level visibility to uncover the root causes of inefficiency, unlock hidden potential, and govern change with clarity and confidence. #Process #Intelligence #OperationalExcellence #QualityManagement #Risk #Compliance

AI at the Edge: Smaller Deployments Delivering Big Results The shift to edge AI is no longer theoretical—it’s happening now, and I’ve seen its power firsthand in industries like retail, manufacturing, and healthcare. Take Lenovo's recent ThinkEdge SE100 announcement at MWC 2025. This 85% smaller, GPU-ready device is a hands-on example of how edge AI is driving significant business value for companies of all sizes, thanks to deployments that are tactical, cost-effective, and scalable. I recently worked with a retail client who needed to solve two major pain points: keeping track of inventory in real time and improving loss prevention at self-checkouts. Rather than relying on heavy, cloud-based solutions, they rolled out an edge AI deployment using a small, rugged inferencing server. Within weeks, they saw massive improvements in inventory accuracy and fewer incidents of loss. By processing data directly on-site, latency was eliminated, and they were making actionable decisions in seconds. This aligns perfectly with what the ThinkEdge SE100 is designed to do: handle AI workloads like object detection, video analytics, and real-time inferencing locally, saving costs and enabling faster, smarter decision-making. The real value of AI at the edge is how it empowers businesses to respond to problems immediately, without relying on expensive or bandwidth-heavy data center models. The rugged, scalable nature of edge solutions like the SE100 also makes them adaptable across industries: Retailers** can power smarter inventory management and loss prevention. Manufacturers** can ensure quality control and monitor production in real time. Healthcare** providers can automate processes and improve efficiency in remote offices. The sustainability of these edge systems also stands out. With lower energy use (<140W even with GPUs equipped) and innovations like recycled materials and smaller packaging, they’re showing how AI can deliver results responsibly while supporting sustainability goals. Edge AI deployments like this aren’t just small innovations—they’re the key to unlocking big value across industries. By keeping data local, reducing latency, and lowering costs, businesses can bring the power of AI directly to where the work actually happens. How do you see edge AI transforming your business? If you’ve stepped into tactical, edge-focused deployments, I’d love to hear about the results you’re seeing. #AI #EdgeComputing #LenovoThinkEdgeSE100 #DigitalTransformation #Innovation