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CoreSite Launches Data Center Knowledge Base

CoreSite, an American Tower company, recently launched the Data Center Knowledge Base

Through informative videos, infographics, articles and more, this digital content hub highlights the pivotal role data centers play in transmitting, processing and storing vast amounts of data across both wireless and wireline networks – acting as the invisible engine that helps keep the modern world running smoothly.

“Everyone is a consumer of data centers; data is generated every time you use a smart phone, stream a show or send an email – and that data has to be stored and processed,” said Juan Font, President and CEO of CoreSite and SVP of American Tower. “Data centers serve as the backbone and critical infrastructure for all this data, and we saw an opportunity to help people understand this relationship and the many benefits data centers have long contributed to our modern way of life.”

The Data Center Knowledge Base offers:

  • General Data Center Operations: Information on what data centers are, the different types of data centers, how they work and how they enable daily life.
  • Tech Innovation: Overview of the advanced technology deployed at data centers—home of the cloud and AI applications—and how data centers are fueling advancements and driving innovation.
  • Economic Impact: Details on how the data center industry stimulates economic growth, creates jobs and helps provide stability.
  • Energy Efficiency: Information on how data centers can help companies optimize energy usage and the energy demand of different types of data centers.
  • Community Support: Highlights how data centers, and the people who run the facilities, help connect their local communities while making education, mentorship and activism contributions.

Hot Topic

The Latest

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

CoreSite Launches Data Center Knowledge Base

CoreSite, an American Tower company, recently launched the Data Center Knowledge Base

Through informative videos, infographics, articles and more, this digital content hub highlights the pivotal role data centers play in transmitting, processing and storing vast amounts of data across both wireless and wireline networks – acting as the invisible engine that helps keep the modern world running smoothly.

“Everyone is a consumer of data centers; data is generated every time you use a smart phone, stream a show or send an email – and that data has to be stored and processed,” said Juan Font, President and CEO of CoreSite and SVP of American Tower. “Data centers serve as the backbone and critical infrastructure for all this data, and we saw an opportunity to help people understand this relationship and the many benefits data centers have long contributed to our modern way of life.”

The Data Center Knowledge Base offers:

  • General Data Center Operations: Information on what data centers are, the different types of data centers, how they work and how they enable daily life.
  • Tech Innovation: Overview of the advanced technology deployed at data centers—home of the cloud and AI applications—and how data centers are fueling advancements and driving innovation.
  • Economic Impact: Details on how the data center industry stimulates economic growth, creates jobs and helps provide stability.
  • Energy Efficiency: Information on how data centers can help companies optimize energy usage and the energy demand of different types of data centers.
  • Community Support: Highlights how data centers, and the people who run the facilities, help connect their local communities while making education, mentorship and activism contributions.

Hot Topic

The Latest

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...