The Need for Speed: 5G Data and Edge Computing
We are living in a digital world. We share more data online than we ever have, meaning large enterprise businesses like Facebook, Amazon, and Google are generating and aggregating more data than ever before. This is creating a new challenge: to handle all of that data, we need faster networks and more powerful processing. That is where 5G and edge computing come in. Utilizing these forms of technology will become vital as we begin to move into the era of connected cars and smart cities.
The Hyperscale Growth of 5G and Big Data
Since its launch in 2019, 5G and big data networks have grown in both size and number, with arguably the largest 5G expansion occurring in 2021. This growth is largely driven by the proliferation of smart devices in the marketplace. In 2021 alone, the number of connected Internet of Things (IoT) devices increased 8%, with a projected 18% increase expected by the end of 2022.
Now, consider the makers of these IoT devices, the hyperscalers like Facebook, Amazon, and Google. How can they possibly process the sheer magnitude of data that is being pushed through their devices on a minute-by-minute basis? Simply put, they can’t. This is part of the continual push for expanded 5G networks, coupled with edge computing to adequately process 5G and big data that comes with it.
The Potential of 5G Analytics
Analytics are a beautiful thing. They tell us what our customers like or don’t like, what their interests are, how they enjoy spending their spare time, if they prefer video content over images and text, if they are drawn to information, data, or emotional stories, and so much more. Given the expanded abilities 5G analytics provides, that means more information to gather.
Now, before we get an ugly big brother vibe from this, understand – this information can be used to enhance the user experience. Meaning that the better these hyperscale businesses understand the consumer’s personal preference, the better they can deliver services and content that the end user wants and appreciates. Consider this: if you said “Hey Alexa, play Sweet Caroline by Neil Diamond” and she did, but then the next song was “Juice” by Lizzo, you might wonder what happened. Algorithmically, the device should know to play another song within the same genre as Sweet Caroline, because it’s basing the next piece of content on the existing choice. Similarly, if you were watching video clips and after three or four it turns into full articles, your exit rate would be quite high because your interest was in video content, not print. Without gathering the data on end-user habits, the algorithms cannot provide quality content that the end-user deems valuable.
From a business perspective, hyperscale businesses can aggregate these 5G analytics to make data-informed business decisions. This could contribute to their research and development phase for enhancements to existing products. Or the aggregated data could help them make more informed decisions when developing new versions of their existing products or deciding which IoT devices they should expand into. Businesses could also use this data to develop compelling marketing campaigns that are tailored to each user demographic. For example, if they know 55% of their user base utilizes their product for video content, 50% for household organization, and 40% for streaming music, they can create marketing campaigns highlighting these three functions. They also know from an R&D perspective that integration with basic calendars is important to users, as well as sound and viewing quality. Alternatively, if a new feature is created to enhance the key usage elements of the device, they can create an entire campaign on its functionality and how it benefits the end user.
The Need for Hyperscale Data Analysis
This is all well and good. However, for hyperscalers to use this data to do anything, they first must process it. Unfortunately, there are two pain points with existing 5G and big data processing solutions. First, they can be quite costly. Second, they simply cannot scale to meet the processing demands of loading data at terabits per second.
Hyperscale Analytics Delivered for 5G Data
The Ocient Hyperscale Data Warehouse empowers next-generation data analytics solutions that competitors cannot. By truly understanding the need for capturing and utilizing 5G analytics, Ocient offers a combination of high performance, massive scalability, and low cost.
Ocient was developed with speed and scale in mind. For data analysts, there is nothing worse than sitting idle because you are waiting for a query to load. Ocient transforms and loads data at terabits per second and runs queries up to 50 times faster than the best alternatives on the market. As we know, with the number of connected IoT devices projected to grow year over year, scalability will be vital for any data processing solution. Today, Ocient can not only transform and load data at terabits per second, but also possesses the scalability to handle petabytes of data without compromising performance. Additionally, Ocient incorporates extract, transform, load (ETL) out of the box, maximizes the input-output from the most performant industry-standard hardware, and decreases the storage footprint by up to 80%, all of which result in a lower cost of ownership.
Considering all of the above, Ocient not only addresses the two biggest pain points in the data processing industry, it’s delivering an all-around better solution that is ideal for handling data captured from 5G networks.
Conclusion
Our digital connectedness is far from plateauing. As 5G and big data continues to grow at hyperscale, Ocient is well-positioned to keep up with the demand, not only today but in the future. The Ocient Hyperscale Data Warehouse can process large data sets quickly and efficiently, making it the perfect solution for enterprises that not only need to make sense of their hyperscale data but want to capitalize on 5G analytics.