Where Connectivity Meets Storage
- Daniel Ezekiel
- Jul 24
- 5 min read
Updated: Jul 30
Why the Convergence of Dataflow, Protocols, and AI Infrastructure Demands a New Architectural Paradigm ...
The Inflection Point
Across verticals—from telcos and AI factories to mobility and manufacturing—we are at a turning point: storage and connectivity are no longer distinct layers. They are now tightly coupled infrastructure requirements, driving the performance, scalability, and intelligence of next-generation systems.
As workloads shift toward multimodal AI, real-time inference, and sovereign edge computing, the boundaries between “where data is stored” and “how data moves” are dissolving. What’s emerging is a unified view of data pipelines, where terabyte-per-second throughput is the requirement.
The 5 Pillars of This Emerging Infrastructure Stack
Ubiquitous, Deterministic Connectivity
Fiber, Wi-Fi 7, Ethernet 800G, and 5G Advanced
Deterministic latency, large bandwidths, and guaranteed throughput are the new QoS
High-Throughput Intelligent Storage
NVMe-over-Fabrics, PCIe Gen5/6, and high-bandwidth memory buffers
Local + distributed storage optimized for real-time AI dataflows
Heterogeneous Compute
CPUs, NPUs, GPUs, xPUs and dedicated accelerators at edge and core
Compute is everywhere—but useless without aligned I/O
AI Inference + LLM Automation
Data pipeline readiness determines inference speed, accuracy, and cost
Inference is no longer compute-bound—it’s I/O-bound (It's more about Ingestion and export of data)
Multimodal Sensing and Actuation
Across all verticals, sensor fusion is critical, and thereby
Requires low-latency loop across sensing → storage → inference → actuation
Market Signals
Jensen Huang (GTC 2024): Called out “Terabyte-per-second throughput” as a new standard for datacenter clusters
Nokia’s acquisition of Infinera: Signaling deep investment in optics and deterministic connectivity
AI Sovereignty and Edge Deployments in EMEA/Asia: Accelerating need for local AI data infrastructure
Industry Verticals Shift from Telcos, eMobility, Smart Cities, Industrial: From building AI edge ecosystems to sovereign compute and storage pipelines—this is the core design shift I’ve seen globally
Global Telco-Fibre Intiatives: Some of the Major telecom players like Deutsche Telekom, RIL, T-Mobile, and Telefonica Spain have driven massive fiber optic expansions over the last decade. This significant push towards fiber infrastructure is set to continue, especially within data centers.
Hyperscalers Fiber Initiative in Data Centers: Hyperscale cloud providers (e.g., Google, Amazon Web Services, Microsoft Azure, Meta) are at the forefront of fiber adoption within and between their data centers. Their entire business model relies on moving vast amounts of data quickly and efficiently. The AI focus and the large models, and the vast volumes of data shuffling associated therein, is accelerating this adoption.
Why It Matters
What matters now is not isolated performance—it’s data fluidity. The bottlenecks in AI are not just model size or GPU count. It is about the complete movement and storage of data across the pipeline.
The real pain points are:
Storage access speeds
Memory-to-memory latency
Interconnect protocol overheads
Deterministic, secure transport of large volumes of sensor and model data
Processing time of data at the processors (including code and data)
Software architecture and implementation
It’s about leading a stack-level future-proofand scalable architecture that meets the performance and sovereignty needs of AI factories, telcos, EVs, and industrial OEMs.
Ecosystem Involvement
In this new architecture, some of the companies like ATTO, MaxLinear, Broadcom, Qualcomm, are in a unique position. With strong IP across Fibre Channel, WiFI, Cellular 5G, NVMe, PCIe, Thunderbolt, Ethernet, and software tuning—they are the data pipeline enablers - where Storage meets Connectivity - enabler for Edge AI to plug-ins on Data Centers
Challenges and Opportunities
The convergence of Storage and Connectivity is both a challenge, risk and an opportunity for infrastructure and storage/connectivity companies.
Wired Connectivity: Fibre’s Rise Ethernet remains crucial—especially with 400G/800G scaling—but fibre is gaining ground, not just in long-haul and metro links but inside data centers and edge clusters. Its performance advantages in speed, latency,future-proofing, and energy efficiency are becoming critical at AI-era scales.
Wireless: Wi-Fi and Cellular Trade-offs Wireless choices hinge on use case. While cost pressures do not favor dual-mode chipsets, the market has evolved to support flexible, application-specific solutions—Wi-Fi, cellular (5G/6G), or hybrid—backed by full-stack software. This adaptability is key for OEMs addressing diverse industrial and AI edge demands.
Challenges
Legacy Factor: Existing Ethernet-based infrastructure in enterprises and hyperscalers slows the adoption of more performant fiber alternatives.
Fiber Cost & Complexity: While superior, fiber-based solutions demand higher investment in transceivers, specialized management, and skilled deployment, especially in existing (brownfield) environments.
Fragmented Standards & Lock-in: Diverse and competing standards (e.g., Fibre Channel, NVMeoF, iSCSI) create vendor uncertainty, hindering interoperability and raising concerns about vendor lock-in.
Wired vs Wireless: Some parts of the solution topology are best suited Wired (as in the Access Layer of Telecom, within Data centers and HPC Clusers, while the Edge Topology is best enabled mostly by Wireless (Cellular or NCS), and in cases by Wired (where regulations demand it due to security concerns).
Latency & Data Gravity: Distributed AI training/inference struggles with non-trivial challenges in moving petabytes of data across networks with minimal latency and jitter.
Opportunities
AI Factories & Sovereign Edge: The burgeoning need for AI training and inference clusters drives demand for low-latency, high-throughput interconnects, a sweet spot for fiber, NVMe, and specialized storage fabrics.
Deterministic Networks for AI: AI pipelines require consistent throughput, a capability uniquely delivered by fiber and next-generation deterministic Ethernet (TSN).
Compute-Adaptive Storage: Storage evolves from passive to active, with NVMe-over-Fabrics, intelligent tiering, and edge caches directly contributing to real-time AI processing.
Hyperscaler & Telco Disaggregation: Trends like O-RAN and composable infrastructure necessitate flexible, modular fabric-based storage and interconnects, favoring providers who can bridge these domains.
EMEA & Sovereignty: Europe's focus on data privacy, edge autonomy, and AI sovereignty creates a significant market for localized, deterministic, and differentiated connectivity and storage solutions.
EMEA & Germany: Storage‑Connectivity Landscape
Germany as European Infrastructure Hub
Germany hosts over 500 operational data centers, the highest in Europe, with revenue forecast to rise from ~ USD 8.17 B in 2023 to over USD 18 B by 2030—CAGR between 6–12% depending on various studies
Frankfurt currently dominates (78% of major gateway facilities), but other cities like Munich and Berlin are scaling
Networking & Fibre Acceleration
The Germany data center networking market is estimated at USD 1.05 B in 2025, growing at ~5.6% CAGR to USD 1.38 B by 2030
AI and hyperscaler deployment are driving demand from 25/50 GbE fabrics to 400G–800G networks and optical core infrastructures
Fibre optics Europe-wide was ~USD 2 B (2023), with Germany accounting for 36% share, and projected to reach ~USD 2.7 B by 2030 (~4.3% CAGR)
Storage Market Trends
In Germany, Storage Area Networks (SANs) dominate enterprise adoption, with hybrid and all-flash growth accelerating due to cloud, AI, and regulatory drivers
Western Germany leads storage capacity (>45% of national share), growing ~7–10% annually; Eastern Germany is growing even faster (~9–14%) driven by industrial modernization, lower labour costs and better tax benefits.
Drivers Behind Growth
AI & Industrial Enablement: Germany’s Industry 4.0, smart factories, and sovereign AI initiatives require ultra-low-latency storage-connectivity fabrics
Hyperscale Investment: Major operators and colocation providers are expanding Frankfurt and Munich with 800G-ready infrastructure for AI workloads
Regulatory Influence: Germany's Gigabit rollouts (Fiber & 5G), sustainability regulations, and data sovereignty laws drive demand for deterministic connectivity and localized
Conclusion
We are not just connecting devices—we are connecting intelligence. The convergence of storage and connectivity is the backbone of this shift. And the companies that master this convergence will define the infrastructure backbone of the AI decade.
I’m committed to enabling that vision—especially across EMEA, where the opportunity is vast and the demand is urgent.
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👉 For deeper insights into how this impacts your Edge AI, Datacenter, Private Cloud, Telco or AI Factory strategy—whether in semiconductor, storage, connectivity decisions, AI, or adopting the appropriate solution topology or vendors— schedule a slot here: https://lnkd.in/eTk5pQxx
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