In a world where data is now considered to be the new oil, we need a secure, reliable, and scalable platform to store, analyze, and distribute data. This is where cloud computing comes into play, particularly with data centers.
Every human being approximately generates around 1.7 megabytes (MB) of information each second. That will accumulate to nearly 150 gigabytes (GB) of data daily. This enormous growth in data generation drives the increased demand for data storage.
Data centers that have a history going back to the mid-1940s are known to be dedicated spaces within buildings that house computing resources and other components associated with data management. By 2023, market research firm Technavio forecasts the data center market to grow into more than $280 billion.
From large traditional centers, modern data centers have transformed digitally. On-premises physical servers are now combined with virtual networks that support applications and workloads beyond tangible infrastructure into a multi-cloud environment.
In the Industry 4.0 era, data exists and is connected across multiple data centers, the edge, and hybrid (public and private) clouds. Now more than before, data centers must be able to communicate across both on-premises and in the cloud to cater to huge amounts of data.
Generally, the public cloud is a massive collection of data centers. As cloud providers extend their capabilities in providing more efficient and cost-effective data center resources, the digital transformation for data use cases can be deployed in a seamless manner.
Role of cloud computing in data
Before we tackle further on data centers, let’s look at the bigger picture and assess the importance between data and cloud computing. In particular, the huge volume and collection of data led to the concept of big data. As the data becomes massive and more complex, traditional data management tools are not capable enough to store and process it efficiently.
Therefore, big data and cloud computing play a huge role in digital society. Combining these two technologies results in various benefits, such as:
- CAPEX to OPEX
Big data projects typically require immense infrastructure resources, which traditionally translate to high on-premise capital expenditure (CAPEX) investments. But thanks to the cloud infrastructure’s virtual approach and new business models, businesses are now convinced to migrate to the cloud.
The Infrastructure-as-a-Service (IaaS) and Software-as-a-Service (SaaS) models have allowed companies to practically eliminate their biggest CAPEX expenses. What does this mean? By shifting to the operating expenditure (OPEX) approach, businesses have more agility and flexibility to stay relevant in ever-changing markets.
Large volumes of structured and unstructured data require an enhanced capacity, processing power, reliable storage, and more. In response, a scalable cloud architecture comes into play through network virtualization.
Whether data traffic or application workload spike in demand or grow gradually over time, a scalable cloud solution enables organizations to respond accordingly and cost-effectively by increasing the storage and performance as necessary.
- Cheaper cost
Mining big data in the cloud has made the analytics process more affordable as it entails saving costs related to system maintenance and upgrades, energy consumption, and facility management, among others.
Moreover, a cloud platform does not require the need to create and allocate new IT infrastructure or virtual servers for analytics. As a result, the technical aspects of big data processing are nothing to worry about, and creating insights becomes the main focus.
- Better contingency plan
In cases of cyberattacks, power outages, or equipment failure, establishing a data center with duplicate storage, servers, networking equipment, and other infrastructure as an alternative is time-consuming, difficult, and expensive.
In addition, systems will take ages to back up and restore fully. Having the data stored in a distributed cloud infrastructure will allow an organization to recover from adversities much faster, ensuring continued access to information and insights.
How data centers are innovating
A cloud data center is purely remote and functioning online. When your data is stored on cloud-based servers, it automatically gets fragmented and reproduced across various locations. This results to a better and more secure storage. Those data centers that exceed 5,000 servers and 10,000 square feet in size are defined by IDC as “hyperscale”.
Commenting about these hyperfacilities, John Dinsdale, a chief analyst and research director at Synergy Research Group, said, “There were more new hyperscale data centers opened … with activity being driven in particular by continued strong growth in cloud services and social networking.”
On the other hand, the sources of big data include the cloud, the Internet of Things (IoT), and the web itself. As the processing of these data calls for accuracy, low latency, speed, and security, data centers have now come closer to the edge, or the source of the data.
Edge data centers are smaller facilities located in near proximity to end-users to allow cloud computing resources and cached content to be delivered in real-time. Typically connected to a centralized data center or multiple data centers, edge computing allows organizations to reduce latency and improve the customer experience by processing data and services at close range.
Additionally, end-users and interconnected devices have increased the demand of accessing applications, services, and data within data centers anytime and anywhere. By establishing edge data centers, high-performance, cost-effectiveness, and reduced latency are carried out.
Today’s distributed cloud is comprised of more than 10,000 data centers scattered across the globe. In the next five years, tens of thousands of additional scaled-down data centers are expected to emerge at the edge of the network.
Now, a new generation of cloud-native applications are already on the table. It covers different categories such as entertainment, retail, manufacturing, and automotive. In many cases, these applications will rely more on full-scale cloud operations, expecting instantaneous services.
Traditional centralized cloud architectures will not meet the Quality of Service (QoS) expectations for cloud-native applications that require a more dynamic and distributed cloud model. As a result, cloud resources involving compute and storage must move closer to the edge of the network.
This shift to a distributed edge cloud model will result in more or less three times as many data centers at the network edge. Hence, it will require the entire cloud ecosystem to embrace the role of network convergence and connectivity.
From a local perspective, an Analysys Mason research highlights that MENA operators could do more to capitalize on established relationships with third-party public cloud service providers to deliver end-to-end solutions. They could combine the public cloud, their internal proprietary cloud, and their network management capabilities to offer integrated and differentiated propositions.
What is more, operators can also improve their attractiveness to global cloud players that are looking to establish a local point-of-presence (PoP). In compliance with regulations, those that have invested in building data centers such as Zain, Orange Egypt, Ooredoo, and Huawei, and expanding connectivity infrastructure can even become national or regional cloud brokers or aggregators.