The Future of Data Analysis: Fog Computing
by Jaap van Duijvenbode on May 29, 2018
What is Fog Computing?
To understand fog computing, basic knowledge of edge computing is required. Edge computing, or remote computing, is the current forefront of data analysis and storage for global enterprises. Edge computing allows these enterprises to perform data analysis at remote locations where the data is gathered, eliminating the need for uploading raw data to a central hub for analysis. Not only does edge computing remove the need for a constant data stream, it’s also quite useful for IoT (Internet of Things) devices that suffer from poor connectivity. These devices can’t maintain a consistent connection to the cloud, so any data they transmit could be incomplete. In summary, edge computing reduces the need for a constant data stream and performs local data analysis for better control of the flow of information.
Fog computing functions as a direct upgrade to edge computing. Fog processing, much like edge, uses the remote devices of a networks infrastructure to perform local data analysis. However, fog computing also incorporates the network connections between these devices and the cloud. In this way fog computing creates a web utilizing all available resources to analyze data. That being said, fog computing isn’t too different from edge computing. The resources and benefits of fog processing are largely the same you would acquire from normal edge computing, but on a larger scale.
Impact on Storage Strategies
Fog computing reduces the data required to be stored locally, as well as reduce bandwidth taken up during transfers. Think of fog computing as an extra jump in your network. It still requires the same security and data strategies, but it helps reduce the workload by a little.
Performing local data analysis is crucial for industries with latency sensitive data, such as financial services or manufacturing. Even a few milliseconds of latency could be crucial. Fog computing instead results in smaller uploads with better quality information. Cutting out potential latency could mean the difference between success or disaster.
By leveraging unconventional resources, fog computing provides a better option for data storage and analysis. It doesn’t remove the need for consolidating or mining data, but it does create a better environment for managing data streams. However, many of the same principles of data management still apply:
- Consolidation Edge and Fog computing not only allow you to reduce bandwidth usage, but also cut down on required data storage. With data sets being analyzed and reduced on site, the need to locally store the excess is no longer required.
- Control Unlike the normal process of acquiring data and uploading to a central hub, fog computing places control back in the hands of the user. Data sets aren’t uploaded en masse, and the flow of information allows the global enterprise to make decisions faster and with more accuracy.
- Mining Fog computing will handle data mining both remotely and autonomously. This means less human intervention is required to pull the necessary information from large data sets, freeing up resources and manpower for other tasks.
- Security Even with the data analysis offered by fog computing, data security is still a top priority. Data processed through fog or edge computing should be treated like normal infrastructure security, with all the necessary precautions.
Fog, and by extension Edge computing, are the next step in remote analysis of data sets. For any enterprise, processing data locally before uploading to a central hub provides global benefits. It’s unlikely fog computing will replace edge computing, but will instead work in tandem to assist in real-time analysis of field data. Paired with Talon FAST™, your company’s data will be available anywhere, at any time, faster than you can imagine.