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Data Center Building Tracker – 1H22
Covers data center buildings for top cloud and colocation service providers. Includes data center locations, size in square feet, critical load in kilowatts (kW), new data center capacity construction plans, and industry trends.
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Data Center Server Market Analysis – 2H22
Covers the data center and edge server markets, including vendor market share; enterprise, cloud, and communication service provider investment; and semiconductor-level transitions such as the adoption of servers with co-processors for AI.
Report / 11 Oct 2022 / Manoj Sukumaran
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Data Center Racks Tracker – 2022
This is the annual update to the Data Center Rack Tracker, including updated forecasts and vendor shares. It uses 2021 as a base year and forecasts to 2026.
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The Analyst Team

Vladimir Galabov
Research Director, Cloud and Data Center
Vlad is responsible for the cloud and data center research practice’s expanding roadmap, focused on disruptive trends such as the shift to edge computing, the evolution of compute silicon, and the need for efficiency improvements and sustainability in the data center industry.
Vlad joined Omdia, formerly IHS Markit Technology, in 2016. He developed many of our unique, industry-leading analyses including our flagship open computing equipment research and our three-way server market reconciliation process. He’s held senior positions at Intel and Ciena, where his data-led insights served as a central input into the companies’ strategies. Key market models he developed include an in-depth analysis of telecommunications network operations costs, a PC inventory tracker, and an actionable IOT market forecast. Vlad’s work has even facilitated decision-making in the British Parliament. He holds a Bachelor of Science degree in economics and management from Aston University, UK.
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These are Omdia driven recommendations based on content that is both similar, and has been frequently visited by other users in combination with the content you are currently viewing.
Omdia Recommends
These are Omdia driven recommendations based on content that is both similar, and has been frequently visited by other users in combination with the content you are currently viewing.
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