Critical but less “time sensitive” data can use the cloud or the company’s data centers to be processed in their complexity.
Some examples of this saudi number for whatsapp can be represented by Big Data, historical data analysis, long-term storage or everything related to the activities aimed at implementing the learning of ML (machine learning) algorithms.
It is worth remembering that the term edge was coined by Cisco in 2014 to describe a particular trend that has emerged in the development of IT architecture, regarding its propensity to move data analysis capabilities from traditional “core” network equipment to devices close to the data source.
The implementation brought by edge computing can be considered from the point of view of its processing and communication capabilities. In fact, data coming from remote devices is first processed at the edge and then sent to the central database for further analysis.
Alternatively, communication from the edge to the core can be prioritized, allowing real-time monitoring without pre-processing or storage. Some systems do both, prioritizing local storage before sending data to a central database.
By performing data analysis locally at the source, you can reduce latency and make quick decisions without having to wait for information to travel back and forth over long distances.
Another potential benefit is increased security through decentralization . By moving analytics capabilities away from a single point of vulnerability, edge computing minimizes the impact of security breaches and system outages on business processes. This is especially useful in scenarios where response time is of the essence, such as emergency services or disaster recovery planning.