In recent years, the cloud has captured much of the market attention when it comes to building and using big data solutions. However, edge computing may be an important part of your solution—and it’s something you should consider now.
What is edge computing?
Edge computing is a way of processing and storing data close to the source. Data is processed in the cloud, but it’s also processed at the edge–at the place where it’s generated.
Edge computing allows for more real-time analysis of data so you can get faster insights into what’s happening with your business. This is important because many businesses rely on having access to up-to-date information so they can make decisions quickly based on what they know about their customers or products.
Why is it important for big data?
Edge computing is a new technology that’s on the rise in the big data industry. It has many benefits, including:
- Better user experience
- Improved quality of big data
- Reduced costs
How does edge computing fit into traditional data center architectures?
Edge computing is not a replacement for traditional data centers. Instead, it’s an extension that can be used to offload computation from the cloud and storage from the cloud.
For example, consider an IoT application that requires real-time processing of sensor data and analytics before sending it back up to your cloud platform. In this scenario, edge computing provides a way for you to perform local processing on that data before sending it over the airwaves or across long distances via broadband connection (such as 5G). On top of that, if you don’t have reliable connectivity at all times (for example when there are pockets of poor reception), then using edge computing allows you to continue operating without interruption by saving up some information until there’s better connectivity available again so they can be sent later on when everything goes back online again
Edge computing makes sense for many applications, but how you build it depends on your needs and resources.
Edge computing makes sense for many applications, but how you build it depends on your needs and resources. You should consider the following factors:
- How much data do you have? How does this define the scope of your edge computing platform?
- What kind of machine learning models do you want to run on the edge? Does your software need a GPU or FPGA accelerator?
- Do users expect fast responses from their devices (e.g., VR/AR headsets), or can they tolerate longer response times while they wait for updates from other sources (e.g., web browsers)?
These questions can help determine whether an architecture based on custom silicon, a pre-built appliance like an Intel NUC PC with integrated graphics card(s) plus SSD storage plus networking components is right for your application–or if another solution might be better suited given its requirements for performance and power consumption
As you can see, edge computing is a powerful tool for big data. It gives us the ability to store and process data closer to where it is collected, which means faster response times and less latency for users. However, this technology isn’t right for every application or company size; there are many factors that will influence how much value edge computing provides for your organization.