DATA CENTERS ON FIRE
A new podcast series exploring what’s really happening behind modern data centers, from evolving technologies to the risks that come with them.
In each episode, we bring together industry experts to openly discuss the challenges shaping today’s data center landscape: increasing power demand, battery innovation, fire protection, and the complexity of modern infrastructure.
DATA CENTERS ON FIRE – Episode 1
Classifying the Real Risk (Part 1 of 3)
Data centers are no longer simple infrastructures, they are complex ecosystems where new technologies are constantly reshaping both performance and risk.
In this first episode, hosted by Miguel Martinez, we are joined by Darragh Williamson and Riccardo Cerati to explore how the industry is evolving and what it really means for risk classification.
From the introduction of batteries and liquid cooling directly into data halls, to the growing gap between real-world deployments and existing standards, this conversation highlights a key question:
Are we truly understanding the risks we are designing for?
This is the first part of a three-episode recording, setting the foundation for a deeper exploration of how risk is evolving in modern data centers.
DATA CENTERS ON FIRE – Episode 2
Classifying the Real Risk (Part 2 of 3)
Lithium-ion batteries are reshaping data centers — and redefining risk.
In this episode, hosted by Miguel Martinez, with Darragh Williamson and Riccardo Cerati, we explore how energy storage is moving closer to the core of data center operations and what this means for fire safety and sustainability.
How do we balance performance, safety, and sustainability?
DATA CENTERS ON FIRE – Episode 3
Classifying the Real Risk (Part 3 of 3)
As data centers become more complex, risk doesn’t just increase — it multiplies.
In this final episode, hosted by Miguel Martinez, with Darragh Williamson and Riccardo Cerati, we explore how rising density, new technologies, and human factors are shaping today’s risk landscape.
Because in the end, risk should never be underestimated just to fit a preferred solution.