Older paradigms place corporate data directly in the center of the action. After all, at the center was where the data bases and the computational power was housed. Now, data comes from many sources that make centralized computing difficult to manage since moving data from where it's used to process and return is slow. Data can be processed remotely for many reasons like cloud storage, IoT operations, and distributed IT operations of large organizations.

Edge Computing is a "thing" now. 5G operations can utilize successful computational power that are close to the data source and the results reported back to a central agency. The action and the associated data is co-located for speed, accuracy, and security reasons.

Examples to clarify the concept might include medical monitoring, self-driving cars, supply chain control, and data-analytics along with predictive maintenance.

A lot of data comes in the form of video captures and editing before delivery. This is done at the site that sourced the captures. Many operations need fast processing that is not the primary concern of the central corporate needs. Data analysis is becoming more important as the deliverable. The source data stays out on the network. Massive data delivery has proven to be limited by network bandwidth. Moving massive data loads are done physically by shipping the storage device itself from one place to another where it otherwise might take years to deliver over a network. The data is that big.

So where does that leave you, the career engineer? Behind the power-curve most likely. There are many new technologies and techniques that need to be learned to be effective in this new world. Now that you know what's coming, warm up your Googling fingers and get stuck in it.