The data revolution is undoubtedly upon us. Big Data is no longer just for mega-corporations like Google or Apple. Even small, up and coming businesses these days have their eye on Big Data. Companies have high hopes for data analysis, such as ensuring smoother scaling or enhancing customer-centric operations. When small to medium-sized enterprises set such Big Data goals, they are forgetting one crucial aspect: Big Data is highly dependent on big hardware.
The Overlooked Hardware
Non-IT focused companies that rely on Big Data don’t often realize that futuristic data centers cannot exist solely on the cloud. The colossal mounds of data even a tiny app can rake in require the necessary hardware to store. This often calls for massive investments in infrastructure, such as hard drives and RAM storage, for which smaller companies might not be prepared.
Companies may underestimate the demands that Big Data pose for IT infrastructure largely as a result of misunderstanding what it is exactly. Big Data may refer to large swaths of files stored at multiple locations, even if most companies strive for single, consolidated data centers. The nature of the Big Data that a company collects also affects how it can be stored.
Simply put, the more data a business collects, the more demanding the storage requirements would be. Traditionally, information was stored on databases located on one server. The single server model is no longer feasible for a business that handles, or at least hopes to handle, Big Data in its operations. Even if a company were to house massive databases on a single server, the costs would be out of this world.
Aside from servers, handling Big Data would require upgrades to regular office computers as well. Big Data operations inevitably result in running chunky data analysis programs. Businesses would definitely need to upgrade from 500GB hard drives with only 4GB of RAM to avoid all too predictable lag issues.
Small businesses, such as those centered around apps, may rush ahead to facilitate data collection and analysis but without thinking equally about the hardware requirements as mentioned above. The vital question is, how big should a company’s hardware be to host Big Data?
Big Data Hardware Requirements
Unlike software, hardware is more expensive to purchase and maintain. This is one of the reasons why companies switch over to cloud—not only is this technology more scalable, it also eliminates the costs of maintaining hardware. When businesses handle Big Data, hardware requirements can change. A company cannot rely solely on cloud to store massive troves of information.
These costs, of course, will change depending on individual business needs. Business consultants warn against believing in a singular type of infrastructure for hosting Big Data. The hardware a company needs will depend on how the collected data would be used. For some businesses, a single data center would make sense. But for others, data may need to be stored in connected but individual nodes.
The costs of Big Data hardware would thus change according to unique business needs. Technically, Big Data analysis is a combination of processing power and storage. However, the latter costs more. The standard Big Data storage model nowadays focuses on optimizing multiple nodes in order to distribute and store data. However, these solutions focus on efficiency, rather than on affordability.
Because businesses need quick access to store data, companies are rushing ahead to purchase SSDs over HDDs. SSDs are known to be faster, but cost more compared to traditional HDDs. Though new technology exists to mitigate hardware needs generated by Big Data, acquiring such tech has become quite costly.
What Businesses Can Do to Prepare
Companies that plan Big Data operations should not underestimate the hardware requirements these operations demand. When planning to execute a data processing program, companies should facilitate the right hardware infrastructure, including both server space as well as office computer networks that would eventually conduct data analysis.
Planning ahead to take on such costs would prevent companies from overspending on infrastructure later into a project. I highly recommend doing research on the topic well in advance as well. Since hardware infrastructure needs vary between businesses, it is only prudent to understand the type of Big Data storage a company needs well in advance. Once this is done, your business can smartly calculate costs and keep the Big Data project within budget.