What Is a Data Center? An Inside Look

A data center is a physical location (room, building, or facility) that an organization uses to house its IT infrastructure and fulfill its data needs. This includes building and running critical applications and services and gathering, storing, and processing vast amounts of data.

Virtually every modern organization relies on data to function. This puts data centers front and center in terms of importance as they support all kinds of business processes and house vital assets, playing a crucial role in maintaining day-to-day operations.

In the 1960s, bright minds figured out they could store all of the required computer hardware in a ‘computer room’ (as it was called) that could have dedicated cooling and maintenance.

Over time, these massive supercomputers of yesterday, called mainframes, became the first iterations of in-house data centers.

As the technology became cheaper and more advanced, mainframes became smaller throughout the 1970s and 80s, primarily due to the advent of microprocessors.

These once-behemoths held their ground until the 1990s, when personal computing and networked systems entered the spotlight.

Soon, the old mainframe rooms were filled with servers powered by high-performance and high-speed processors, standardizing how in-house data centers were designed and giving birth to the modular racks we know today.

This transition to networks of smaller and cheaper servers led to the emergence of colocation, allowing companies to rent space in a data center instead of managing their own. This offered a new method of managing IT infrastructure amidst the rapidly expanding digital world.

Virtualization took off in the 2000s, significantly reducing data center power, space, and cooling requirements.

Thanks to cloud computing, we have today’s modern, sleek data center, which links on-premise systems with networks, applications, and workloads virtualized in multiple private, public, and hybrid cloud environments.

There are five different types of data centers, each of which is tailored to specific business needs and workload scopes.

Enterprise Data Centers

The largest type, an enterprise data center, is custom-built, owned, and operated by a single organization for its internal needs.

It’s typically located on-premises, though it can be situated off-site at a place that supplies prime power, connectivity, and security for continuous uptime.

Use cases include:

• Hosting specific IT equipment or services and related systems to support demanding workloads such as high-performance computing.
• Higher data security and easier compliance with regulations such as GDPR and HIPAA since deployment, monitoring, and management are handled in-house.
• Consolidating data systems (storage, processing, and analysis) to manage data across various departments better.

Because it requires a considerable upfront investment, this proprietary data center is usually used only by large companies.

Colocation Data Centers

Here, businesses rent out space within a physical location owned by a data center provider. Since a third party owns the space, they supply support infrastructure that includes bandwidth, power, cooling, and security.

The business renting the colocation data center provides and manages its components, such as servers, racks, and computing hardware.

Benefits of colocation include:

• Lower costs, as there is no required capital expenditure associated with building and running a data center, and lower operational expenses.
• Increased reliability due to 24/7 monitoring and a wide array of redundancies in power and cooling systems.
• Greater scalability: You can freely scale your business up or down while only paying for the resources you need.

Colocation data centers represent an appealing solution to companies focusing primarily on core business objectives, without having to deal with the complexities of building, maintaining, and securing a data center.

Managed Services Data Centers

Managed data centers are deployed and managed by third-party service providers based on a leasing model. Instead of purchasing all the necessary equipment and infrastructure, a company can rent it out and access data via a managed service platform.

Use cases include:

• Projects requiring resource-intensive workloads (e.g., big data analytics) with on-demand scalability.
• Housing remote data backup and disaster recovery.
• Ensuring PCI DSS and other types of compliance when regulations stipulate specific data storage locations.

For businesses that lack the room or expertise to deploy and manage IT infrastructure but don’t want to share resources in a public cloud data center, managed services data centers will likely meet their needs.

Cloud and Hybrid Cloud Data Centers

We’re now in the off-premises territory, as cloud-based data centers host data and applications in a virtual data center. However, the hybrid cloud version mixes computing, storage, and services with on-premises infrastructure.

In both cases, the leased IT infrastructure is managed by a third-party cloud provider, which allows access via an Internet connection and greater flexibility regarding usage and resource sharing.

Major providers include:

• Amazon Web Services
• Microsoft Azure
• Google Cloud Platform

Cloud and hybrid cloud data centers are particularly suitable for startups and small businesses that prioritize agility and cost-efficiency over everything else.

Edge Data Centers

Distributed at the network’s ‘edge’ (hence the name), edge data centers are essentially micro data centers — smaller facilities located geographically closer to data generation sources.

As such, they solve the ever-present latency problem by processing data locally, elevating application performance and customer experience.

Benefits include:

• Minimal latency for real-time, data-intensive workloads.
• Faster delivery of services and cached content.
• Reduced cloud storage costs.

Due to their self-contained, modular design, edge data centers are often implemented in retail shops, warehouses, and other locations with limited space. Thus, they are connected to a central cloud data center for further analysis or storage.

The majority of data centers consist of the same categories of components:

Physical Infrastructure

A data center’s first and foremost component is the square footage of where the infrastructure and equipment are stored. Larger and more complex data centers require more dedicated space.

These physical locations are designed for maximum efficiency, with proper airflow, structured cabling, and racks and cabinets aligned in rows for optimal space usage and ready access.

Hardware Components

This encompasses all the essentials that come to mind upon mentioning a data center.

These include different kinds of servers (rack-mount, blade, mainframes), storage systems such as network-attached storage (NAS) and storage area network (SAN), and all the fiber optics, cables, switches, and routers that comprise the network equipment responsible for connecting data center components.

All of these feature a redundancy element, which is configured in a failover or load-balancing mode. Redundant servers, network devices, and storage arrays ensure a seamless transition into operation in case a hardware failure occurs.

Power and Cooling Systems

To keep everything running smoothly and by the book, data centers employ dual power supplies that include uninterruptible power supplies (UPS), backup batteries, and generators if the primary power system fails.

Redundant arrays of independent disks (RAIDs) and backup cooling infrastructure also safeguard against downtime.

Numerous cooling systems and equipment are placed to keep servers running at optimal temperatures.

Air-cooled thermal options include passive heat sinks, heat pipes, vapor chambers, active cooling fans, and blowers, although precise air conditioning systems such as Computer Room Air Conditioners (CRAC) units are most commonly used.

Liquid cooling and chillers are a step up, and they are usually reserved for high-performance computing tasks. Water is circulated throughout the data center as a cooling medium.

It’s worth mentioning that data center staff uses various cooling technologies and methodologies from section to section (e.g., hot and cold aisle configuration), with airflow management being one of the core techniques.

You can’t build a data center in a day — there are many factors to consider to lower the risk of downtime and data exploits.

Tier Classification

Tier classification refers to levels of data center design and implementation. Internationally recognized standards for data center availability and overall performance inform the design, construction, and maintenance of a data center.

In other words, these are unbiased sets of infrastructure and operating criteria that outline minimum requirements and cover all aspects of physical infrastructure.

As one of those standards, the Uptime Institute uses a progressive four-tier system to rate the redundancy and resiliency of data centers:

• Tier I: These are the rudimental types of data centers geared toward small businesses and startups with undemanding requirements. They incorporate basic redundancy components such as UPS and non-stop cooling, but they lack redundant systems. A Tier I data center guarantees a minimum of 99.671% uptime per year or a maximum of 28.8 hours of downtime.

• Tier II: Includes power and cooling redundancies, including engine generators, energy storage, and cooling units. The improved reliability makes cost-conscious SMBs the likeliest users, with 99.741% uptime yearly or 22 hours of downtime.

• Tier III: Adds additional reliability through partial fault tolerance (the capability to deliver uninterrupted service despite one or more components failing). Data centers in this tier also use several distribution paths, thus requiring no shutdowns for maintenance or replacement. Ideal for large companies and growing startups, tier III promises 99.982% uptime and no more than 1.6 hours of downtime annually.

• Tier IV: Provides full fault tolerance via multiple independent and physically isolated systems by duplicating every critical component of the primary architecture. The Tier IV data center is the most reliable, with 99.995% uptime per year or less than 26.3 minutes of downtime, typically reserved for global enterprises and government agencies.

By investing in the tier classification process, you’ll comply with industry guidelines and reassure your customers that your data center will fully meet their expectations.

Scalability and Modularity

One of the most critical design criteria, scalability, is sorely needed to expand data processing and storage when necessary.

With the advent of hyper-scale architectures, characterized by their vertical and horizontal scaling ability, there is a greater need for robust infrastructure capable of supporting increasing workloads.

Using modular systems and standardized topologies that can be duplicated, data centers can be expanded with smaller blocks as many times as necessary to meet data growth.

Modular data centers ranging from ready-built with no customization to 100% customizable are vital in improving scalability and utilization.

All-in-one IT, power, and cooling modules function on the plug-and-play principle and can help organizations quickly develop and amplify their data processing capacity.

Modular units also radically shorten CapEx and provide more flexibility in adapting to changing business strategies. Moreover, modular data centers can be deployed faster than their traditional counterparts and often run more efficiently energy-wise.

Energy Efficiency

Besides being designed with a reliable power supply, a data center needs an energy-efficient design to keep down the bottom line and environmental impact.

The amount of electricity used at a large data center can power a small town, so optimizing cooling processes and using energy-efficient hardware is a must. To measure and understand the energy efficiency of data centers, a PUE (Power Usage Effectiveness) index is used.

The ideal PUE ratio is 1.0 — every unit of power consumed is used solely by the IT equipment.

Sadly, that’s not the case in most data centers, which fall within the range of 1.2 to 1.4 due to subpar equipment efficiency, inefficient cooling systems, and power losses in non-IT equipment.

Advanced cooling techniques such as hot and cold aisle containment, precision air conditioning, and airflow management can lower PUE. The same goes for upgrading outdated and ineffective servers, storage devices, and network equipment.

The good news is that sustainability has taken root in the industry, giving birth to green data centers. These are designed for minimal environmental impact via low-emission building materials, catalytic converters, and renewable and alternative energy tech.

Data center management is no easy task, as it includes many day-to-day tasks and activities related to operational systems and workflows. Depending on the chosen service model, a data center manager is mainly responsible for these.

Monitoring and Maintenance

Whether working directly in the facility or remotely, a data center manager ensures smooth operations by overseeing essential maintenance tasks.

This includes upgrading software and hardware, keeping the environment clean and organized, and strategically positioning servers for optimal performance.

They also act as guardians of the data center, proactively identifying and quickly addressing potential threats that could compromise security or compliance. An adequate and swift response is vital to server uptime and no reductions in quality of service.

Data center infrastructure management (DCIM) software helps them in this endeavor. It acts as a central command hub for the entire data center, allowing managers to monitor and control all segments in real time.

This includes everything from the IT equipment to the physical environment, encompassing heating and cooling systems. This holistic view grants a deeper understanding of the data center’s IT infrastructure and helps optimize its performance.

Disaster Recovery and Business Continuity

Downtime is costly for data center providers, so disaster recovery planning begins at the very placement on the ground.

From the start, you want to place your data center in an area that is minimally susceptible to natural or artificial disasters and close to a stable power source.

Next, you’ll want to assess your risks and consider the level of impact of specific workloads on your business. Not everything is critical for proper operations, which means categorizing your applications based on importance.

Of course, backing up your data is essential, but a comprehensive disaster recovery plan goes beyond that.

It should also ensure you have the necessary resources, such as additional storage and computing power, to quietly switch operations to a backup system (failover) and then transition back to the original system (failback) once the issue is resolved.

Furthermore, having a second data center in a different location provides additional business continuity assurance if the primary location is affected by an emergency.

Security Management

Modern data centers are a potent combo of traditional and cloud computing technologies, which means the risks and potential vulnerabilities are greater. This especially goes for multicloud environments where the attack surface is sizable.

Hence, robust cybersecurity measures, such as intrusion prevention and detection systems, in conjunction with next-generation firewalls and multi-factor authentication, are necessary to allow access to specific sections of the network.

Best practices such as using secure protocols and strong passwords are also part of the effort, as is maintaining up-to-date systems with the latest security patches.

Adequate security must also incorporate perimeter defense and trust zones within the facility. These include physical security systems such as fences and gates, entry control and monitoring via biometric scanners and security doors, and video surveillance.

On top of that, you’ll find various safety measures, such as emergency equipment or fire suppression systems, to protect the data center against multiple hazards.

As data demands continue to increase, so does the demand for data center innovation to keep ahead (or afloat, if you like) of the ever-growing mountains of incoming information.

Virtualization and Cloud Computing

To say that virtualization has been a game changer would be an understatement. Enabling hardware resources to be abstracted from their physical limits brought new life to traditional data centers and prolonged their lifecycle.

The creation of software-defined overlay networks enabled significant gains in efficiency and performance as workloads are now distributed across several servers and resources are used to their fullest.

Still, virtualizing traditional IT architecture can’t stop the broader transition to cloud-based services, where the inherent scalability and the elasticity of a cloud computing environment deliver almost unprecedented flexibility.

Many companies embrace a “best of both worlds” approach with hybrid cloud deployments.

This strategy leverages the strengths of both public and private cloud environments: control and security for essential elements on private servers while seamlessly offloading processing-intensive workloads to the public cloud when required.

Going serverless is a godsend for companies seeking to save money. It offers a much more approachable cost model, and the pay-as-you-go structure eliminates the burden of unused capacity and long-term contracts.

AI and Automation

Artificial intelligence is a part of any tech-based conversation, and data centers are no different. One could say it’s leading a quiet revolution in the industry, offering new possibilities in data center performance and management.

For example, Google’s DeepMind AI consistently reduced the amount of energy used for cooling by 40%, equating to a 15% reduction in overall PUE overhead.

Enhanced energy efficiency is just one example of AI’s impact: reduced carbon emissions, improved security measures, and lower maintenance expenses are also part of the equation.

The effect is felt on smaller tasks, where predictive analytics automates mundane everyday operations such as monitoring. The machine learning algorithms provide real-time information into potentially problematic areas and predict requirements for a given time frame.

Troubleshooting infrastructure and inefficiencies is similarly automated, allowing for higher productivity while freeing human computing for other critical tasks.

Edge Computing and IoT

Relying solely on centralized data processing isn’t quite productive. Edge computing offers a more efficient solution by leveraging the processing power of devices at the network’s edge, closer to where data is generated.

By processing information locally, edge computing significantly reduces latency, alleviating the network’s stress and reducing data transport costs by preserving network bandwidth.

This leads to better performance for all connected devices and a responsive user experience with fewer service disruptions.

The market for edge data centers reached $11.2 billion in 2023 and is expected to hit $33.9 billion by 2030. Now, edge computing complements the cloud by processing real-time information from Internet of Things (IoT) devices.

Besides reducing latency and optimizing bandwidth usage, edge computing scores big in the security department. Because it processes sensitive IoT data locally, privacy and security are improved, decreasing the risk of data breaches—especially if edge servers are located on the premises.

Here are a few examples of how data centers are applied across the business landscape.

Major Data Centers

China has some of the world’s largest data centers. The largest is China Telecom’s Inner Mongolia Information Park, located in Hohhot, with a total area of 10,763,910 square feet.

The Citadel, run by data center company Switch, is one of the more notable facilities in the United States. It takes up more than 130,000 square meters of floor space and a power capacity of 130 MW.

It’s a prime example of the company’s exascale data center ecosystem, characterized by ultra-powerful supercomputing.

The ongoing expansion project will take 675,000 square meters, with a total power capacity of 650 MW, 100% green power, and 100% power uptime guarantee.

Microsoft operates a highly scalable data center in West Des Moines, Iowa, where the Azure supercomputer is located. Microsoft built it for OpenAI to train breakthrough AI models, integrating quantum computing.

Interestingly, this data center uses less energy and water for the roughly same amount of computing than the company’s Arizona data center — a benefit courtesy of Iowa’s colder climate.

In terms of sustainability, Meta’s data centers are envisioned to promote biodiversity, native plants, and adaptive landscapes that mimic the region’s natural hydrology. Such design helps reduce the urban heat island effect, scoring a 1.09 PUE ratio.

Industry-Specific Data Centers

Outside of service providers, plenty of companies have opted to build their own data centers.

Banks such as JPMorgan Chase & Co and Bank of America invested heavily in facilities to support their banking operations.

Mayo Clinic, a renowned medical institution, established a data center to securely manage vast amounts of patient data while adhering to strict HIPAA compliance regulations.

Retail giants Amazon and Walmart also operate their own data centers to manage inventory, supply chains, and customer data, among other things. These centers support eCommerce, logistics, and in-store operations.

In the automotive world, BMW constructed a data center near its Munich headquarters to analyze sensor data from its global arsenal of production facilities.

To stay successful, data centers must traverse a growingly complex landscape of operational and legal challenges.

Current Challenges

“Mo’ data, mo’ problems” would be the gist of the industry’s attempts to meet the growing demands of our increasingly interconnected world.

Increased scaling via the cloud comes with increased vulnerability. Many companies don’t have a stellar track record in cybersecurity resilience.

Cloud services often involve multiple third-party providers in the chain, which makes maintaining consistent cybersecurity frameworks more challenging.

Data center power demands have never been greater, so investing in newer, more efficient cooling equipment is a given. Not every power grid is built to handle the required output, resulting in occasional but highly costly difficulties in supplying consistent power to high-demand facilities.

There is also the legal side of things. The data center landscape is shifting toward a greener future, with new regulations being implemented to reduce the environmental impact of data centers.

Carbon emission caps, power consumption limitations, and data compliance legislation add more complexity, particularly since they are at the federal, state, and local levels.

Adhering to industry best practices while staying on the right side of the law is becoming a worthy challenge. The silver lining is that these changes will ultimately pave the way for a more sustainable industry.

Future Trends

Thanks to advancements in server design and efficiency, the data center of tomorrow will likely have fewer racks and a higher density.

Higher-density racks allow providers to meet growing demand while maintaining their footprint.

Implementing more innovative cooling systems and optimizing power infrastructure has led to lower PUE ratios, with more room for improvement via high-density server configurations and advanced airflow management techniques, more so as liquid cooling systems become more advanced and cheaper.

New data centers will optimize their physical infrastructure for AI, where energy management systems such as real-time power and cooling optimization will be entirely in the hands of finely tuned algorithms.

Further expansion of 5G will facilitate faster data connections and access, boosting the usage of edge computing.

Adopting cloud computing is a paradox for companies with existing data centers.

While significant investments in on-premises infrastructure can create a pushback toward a cloud migration, the ongoing costs of maintaining a data center can also be a solid motivator to embrace the cloud’s potential for efficiency and scalability.

One thing is sure: the future of data centers, while unpredictable, is brimming with exciting possibilities.

Innovation will be at the forefront, focusing on creating sustainable, efficient, and highly secure facilities. While keeping digital demands in check will be challenging, it’s also an opportunity to make a tangible impact on the technological and environmental ecosystem.