With edge analytics, organizations can process the data, gain insights based on analytics at the edge, and take appropriate actions. Processing the data here would mean cleaning, aggregating, and modeling appropriately for analytics purposes. Analyzing at the edge is faster, and the latency is very minimal. Therefore, for organizations that need to derive insights from connected devices and take appropriate actions in real time, edge analytics can come in very handy.
Edge Analytics vs. Cloud Analytics
The primary intent of both edge cambodia whatsapp number data analytics and cloud analytics is to analyze all the data, derive insights, and facilitate appropriate decision-making processes. Here are some key differences between both.
A centralized analytics solution hosted in the cloud considers data from all the sources that typically is massive. On the other hand, an edge analytics solution can consider only the data from the edge deployment or deployments that it has visibility into.
Since a cloud analytics solution is deployed in a cloud, all the raw data needs to be transported to the cloud, cleaned, and preprocessed before feeding into the analytics solution. Moving data from various sources to the cloud can be time-consuming and further cleaning and modeling the data could also result in delays. Edge analytics, on the other hand, processes the data generated from the edge deployments it has visibility into. As edge analytics solutions are closer to the sources where data gets generated, there is minimal latency.