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💡 𝐃𝐚𝐭𝐚 𝐂𝐞𝐧𝐭𝐞𝐫𝐬: 𝐄𝐧𝐚𝐛𝐥𝐢𝐧𝐠 𝟐𝟒/𝟕 𝐆𝐥𝐨𝐛𝐚𝐥 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐯𝐢𝐭𝐲 𝗚𝗲𝘁 (𝗲-𝐏𝐃𝐅) 𝐂𝐨𝐩𝐲: https://lnkd.in/geg6ExaU From AI and cloud computing to IoT and 5G, the world’s digital backbone relies on one thing—data centers. Today, the market is evolving rapidly: ✅ Rising demand for cloud storage & hyperscale centers ✅ Shift toward green, energy-efficient infrastructure ✅ Growth of edge data centers to enable real-time connectivity As organizations accelerate their digital transformation, data centers are no longer just physical spaces—they’re strategic assets driving innovation, resilience, and sustainability. 🔷𝐃𝐫𝐢𝐯𝐞𝐫𝐬  ➼ AI/HPC compute boom to drive high-density infrastructure upgrades  ➼ Hyperscale Capex super-cycle to accelerate infrastructure spend  ➼ Regulatory & ESG mandates to drive power and cooling modernization   🔷𝐎𝐏𝐏𝐎𝐑𝐓𝐔𝐍𝐈𝐓𝐈𝐄𝐒  ➼ Retrofitting legacy data centers to meet AI-driven density demands  ➼ Rise of liquid cooling in AI-driven data center infrastructure to meet next-gen density requirements 🔺𝐊𝐞𝐲 𝐀𝐫𝐞𝐚𝐬 𝐚𝐧𝐝 𝐓𝐡𝐞𝐢𝐫 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞 Direct-to-Chip Cooling | Remote monitoring and data center infrastructure management | AIOps & Digital Twins | High-density GPUs | Lithium-ion & next-gen battery energy storage UPS | Software-defined Networking | Hyper-converged Infrastructure | Modular Power 𝐌𝐨𝐫𝐞 𝐈𝐧𝐟𝐨 𝐇𝐞𝐫𝐞:@ https://lnkd.in/gcJn4PYn #DataCenters #DigitalTransformation #CloudComputing #AI #EdgeComputing #GreenDataCenters #Sustainability #FutureOfTech #5G #Innovation

Proper data collection is crucial for creating accurate digital twins in metal fabrication, enhancing operational efficiency and security with IoT sensors, cloud storage, and robust security measures.

Is Big Data Too Big for Today’s Data Centres? 🧠 Global data creation is expected to reach 181 zettabytes by 2025, putting more pressure on data centres than ever before. Big Data refers to massive, complex datasets that traditional computing systems can’t efficiently store, process, or analyze. These datasets often come in real-time, from sources like IoT sensors, social media platforms, transactions, and enterprise systems - all requiring fast, reliable, and scalable infrastructure. Here’s what this means for data centres: 🔻 Edge and Hybrid Integration To process data closer to its source, edge computing is rising, and data centres need to support hybrid architectures that blend core and edge seamlessly. 🔻 High-Bandwidth Connectivity Processing Big Data requires ultra-fast network speeds and low latency to move huge datasets without bottlenecks. 🔻 Scalable Storage Architecture As data volumes explode, data centres must offer flexible, tiered storage options - from hot to cold to archival. 🔻 Advanced Cooling + Power Management Handling large-scale data analytics means higher compute densities, which demand more power and smarter cooling. Big Data is reshaping industries, but it’s also reshaping infrastructure. Data centres must now meet demands that go far beyond traditional workloads, accommodating unprecedented scale, complexity, and speed. Facilities that can’t handle the volume, variety, and velocity of Big Data risk falling behind - not just technically, but competitively in a data-driven economy. What’s the biggest Big Data challenge you think data centres need to solve next? 🤔 #datacentre #datacenter #bigdata #storagesolutions #zettabyte

Your Raspberry Pi excels at WiFi and cloud connectivity— but your factory floor still runs on proven CAN bus networks. The integration gap is real: over 1 billion CAN nodes are in operation worldwide, yet mainstream processors don’t include CAN controllers for economic reasons. We just published a deep dive into three proven approaches: MCP2515 → The reliable veteran (~3K msg/sec) TCAN4550 → Modern CAN-FD solution (~25K msg/sec) Custom MCU bridges → Maximum flexibility (~150K msg/sec) Each option comes with its own trade-offs in performance, development time, and cost. Whether you’re building automotive test equipment or industrial IoT gateways, knowing these differences can save weeks of development. 👉 The guide includes real performance benchmarks, PCB design notes, and common pitfalls to avoid. Read the full analysis here: 🔗 https://lnkd.in/dxYi6VmH #Embedded #CAN #Industrial #IoT #Engineering

Your Raspberry Pi speaks WiFi. Your factory speaks CAN. Bridging the two isn’t as simple as it looks — and the wrong approach can cost you weeks. We broke down 3 proven solutions.

How Kafka and Edge Processing Enable Real-Time Decisions - RTInsights: Consider a predictive maintenance system for industrial equipment. Kafka can aggregate data from thousands of IoT sensors, analyze patterns, and push ... #bigdata #cdo #cto

Recent months show accelerating 5G-IIoT adoption with German precision engineering and Chinese scale manufacturing demonstrating complementary innovation pathways for industrial transformation.Emerging patterns in global manufacturing reveal distinct yet complementary 5G Industrial IoT deployment strategies, with German Mittelstand SMEs focusing on precision latency requirements while Chinese #5GIIoT #crossborderinnovation #digitaltransformation #industrialautomation #Industry40 #latencyoptimization #PredictiveMaintenance #SmartManufacturing

Top 10 Benefits of an Edge Data Center: Edge data, processed and analyzed near its source through edge computing, offers significant advantages by reducing latency, enhancing security, and improving operational efficiency across various industries. These benefits are particularly valuable for real-time applications in IoT, healthcare, autonomous vehicles, and smart cities. 1. Reduced Latency: Processes data close to the source, enabling near-instantaneous response times critical for applications like autonomous driving and industrial automation. 2. Improved Data Security: Sensitive data is processed locally rather than transmitted over networks, reducing exposure to interception and cyber threats. 3. Real-Time Analytics: Enables immediate data analysis without relying on cloud connectivity, allowing faster decision-making in time-sensitive scenarios. 4. Cost-Effectiveness: Reduces bandwidth usage and cloud storage costs by filtering and processing data locally before sending only relevant information to the cloud. 5. Enhanced Scalability: Supports large numbers of connected devices by distributing processing loads, preventing network congestion in IoT environments. 6. Increased Data Privacy: Keeps personal and sensitive information on local devices or private networks, supporting compliance with regulations like GDPR and CCPA. 7. Improved Reliability: Maintains functionality during cloud outages or poor connectivity, ensuring continuous operation of critical systems. 8. Network Efficiency: Minimizes data transmission across networks by processing it locally, leading to optimized bandwidth use and lower operational costs. 9. Better Application Performance: Delivers faster and more responsive services by reducing the distance data must travel, enhancing user experience. 10. Energy Efficiency: Reduces the need for constant data transmission to distant data centers, lowering power consumption for edge devices and networks.

Storage at the edge: improving data analysis from the Industrial Internet of Things The Industrial Internet of Things (IIoT) is transforming how industries gather, interpret, and act on data. Machines and sensors are becoming increasingly smart and interconnected. This continuous transformation leads to the production of enormous volumes of real-time data, covering everything from performance metrics and operating temperatures to environmental conditions, vibration levels and even alerts for upcoming maintenance. Read More: https://lnkd.in/dAkthb8F Sandisk Christophe Vaissade #Sandisk #Transformation #Data #Maintenance #SupplyNetworkAfrica #SNA

How Kafka and Edge Processing Enable Real-Time Decisions - RTInsights: Kafka can aggregate data from thousands of IoT sensors, analyze patterns, and push alerts to engineers. #iot #data #internetofthings