How Apache Kafka enables real-time processing at the edge

New AI-optimized cooling system etches liquid channels directly into silicon, potentially transforming data center efficiency and chip performance. #NETP #DataCenter #DataCenterKnowledge https://bit.ly/3IzvCf6

#Who Makes #DataCenter 2024 - AI adoption to accelerate growth in the $215 billion Data Center market - Data Centers are a $215bn global market that grew 18% annually between 2018-2023 - AI adoption is expected to accelerate data center growth as AI chips require 3-4x more electrical power versus traditional central processing units - BofA estimates a $20bn market size for Data Center networking equipment. Cisco is the market share leader, with an estimated 28% market share https://lnkd.in/giueQ5AK

With compute intensifying, data centers must tackle an unseen phenomenon behind some of the biggest challenges: poor power quality. The consequences pile up, affecting not only the data center but all who share the grid: • Hardware and equipment failure • Unreliable, unrepeatable AI training results • Overheated power supply units in high-density racks • Brownouts and transformer failure • Increased energy loss, thus higher energy costs Power quality is a system-level problem requiring system-level solutions. Get ahead of it with innovative solutions from Flex. https://lnkd.in/g5cfbNji #DataCenter #DataCenterPower

⚙️Message Queues – The Engine Behind Real-Time Data Flow Lately, I’ve been diving deeper into real-time data systems, and one thing that stands out is how critical message queues are in keeping everything running smoothly. Think about all the apps, sensors, and systems generating data every second — it’s impossible to process that data instantly without some sort of buffer or coordinator in between. That’s exactly what a message queue does — it sits between data producers and consumers, making sure every message is delivered reliably and in order, no matter how fast things move. Here’s why I find them so powerful 👇 ⚡ They make real-time streaming possible — perfect for instant dashboards or live analytics. 🔄 They decouple systems, so producers and consumers can work independently without crashing under pressure. 📦 They act as a safety net, handling traffic spikes and preventing data loss. 🔐 They ensure reliability through acknowledgment and retry mechanisms. Some popular tools in this space are Apache Kafka, RabbitMQ, AWS SQS, and Azure Service Bus — each with its own strengths depending on the use case. You’ll find message queues everywhere — from IoT telemetry to log aggregation, fraud detection, and microservices communication. In short, if you want real-time insights, your data pipeline probably starts with a message queue. I’m curious — what’s your go-to tool for handling streaming or real-time data? I’ve mostly used Kafka and rabbitMQ in action, but I’d love to hear how others are using these technologies in different contexts. #DataEngineering #MessageQueues #StreamingData #Kafka #RabbitMQ #RealTimeAnalytics #EventDrivenArchitecture #CloudComputing

RabbitMQ vs Kafka vs NATS — 2025 Breakdown I’ve been diving into message brokers lately — exploring how RabbitMQ, Kafka, and NATS approach communication, reliability, and scalability differently. Here’s what stood out 👇 🚜 RabbitMQ — the old craftsman Classic AMQP broker: routing keys, exchanges, acknowledgements. Great when you need guaranteed delivery, complex routing, or transactional workflows. Downside: not designed for extreme throughput or massive horizontal scaling. 🏭 Kafka — the industrial backbone Not a “message queue” in the traditional sense — it’s a distributed commit log. Kafka shines when data is a stream, not a message: analytics, telemetry, event sourcing. It’s heavy, though. Powerful, but comes with ops overhead — infra discipline required. ⚡ NATS — the minimalist reactor Lightweight, blazingly fast pub/sub with microsecond latency. Perfect for real-time communication, control planes, IoT, or edge systems. JetStream adds durability, but the essence of NATS is simplicity and raw performance. 💡 Verdict: • Workflow reliability → RabbitMQ • Data streaming & replay → Kafka • Sub-ms communication → NATS Every tool is great — until you use it for the wrong war. #backend #architecture #systemdesign #eventdriven #highload #kafka #nats #rabbitmq #microservices

As AI workloads surge, traditional scale-out architectures are hitting limits and facing latency, data bottlenecks, and constrained I/O that slow performance. Now, meeting the demands of AI means rethinking how data moves through the system, not just adding more servers. Next-generation data centers are being built around optimized network topologies, faster intra-rack communication, high-speed interconnects, and network acceleration devices that reduce congestion and enhance throughput. Read the full article from Christopher Tozzi in Data Center Knowledge to see how scalability depends as much on data movement as it does on compute power: https://lnkd.in/g8anTT6f

Data centers are hitting gigawatt-scale power consumption, and the effect on the $7 trillion AI infrastructure race is clear. Efficiency is now non-negotiable. I recently spoke with Arm's Mohamed Awad, who explained the power of efficiency at scale: a 20-30% reduction in CPU power consumption multiplied across your data center means significantly more AI capacity without expanding your power footprint. For our full interview, check out the link in comments. 👇 #Arm #AIInfrastructure #DataCenter #HyperscaleComputing

Celestica introduces new DS6000 & DS6001 1.6T data center switches, doubling today’s capacity for high-bandwidth AI/ML applications. Powered by Broadcom's Tomahawk 6 chipset, these open designs offer advanced AI routing features and hybrid cooling options. Learn more at the 2025 OCP Global Summit, Oct 13-16. Link to press release is below. #AI #Datacenter #Networking

Each operation in Artificial Intelligence runs through a colossal ecosystem of servers, chillers, generators and cooling towers, an invisible infrastructure pulsing with energy every second. A single data centre can consume as much electricity as an entire city. It is remarkable to realise that as AI expands, we are not merely increasing our computational capacity, but redefining the very energy logic of the digital world. #ArtificialIntelligence #EnergyTransition #DigitalInfrastructure #FutureOfEnergy #Sustainability #DataCenters #TechLeadership #InnovationStrategy

With the AI explosion, data center managers are rethinking strategies for power, cooling, and fiber planning. Panduit shares insight from Thought Leader Robert Reid on recent advancements in optical networking technology for faster and more reliable data processing. >>> https://shorturl.at/qvDgb #PoweredbyPanduit #DataDrivenInfrastructure #Panduit #datacenter #NetworkInfrastructure