So far in this book, we have discussed the current state of real-time analytics systems, and we’ve covered a lot of ground, learning about a lot of technologies. The adoption of real-time analytics is expected to grow exponentially over the next few years as more businesses become more data driven, and the technologies used to make real-time analytics become more accessible and easier to use.
While I don’t have a crystal ball, in this chapter we’ll look at where this field might go in the next few years.
We’ll also briefly describe technologies that will make real-time analytics more affordable and accessible to businesses and end users.
Edge Analytics
The IoT has already permeated many aspects of our daily lives, but it is expected to become even more ubiquitous in the coming years.
With the proliferation of IoT devices, the amount of data generated is staggering, and managing and making sense of this data will be a critical challenge.
This is where real-time analytics will play a crucial role.
Edge analytics is the process of analyzing data at or near the edge of the network, where the data is generated.
This is in contrast to traditional analytics, which typically involves sending data to a centralized location for processing. Figure 13-1 shows a typical architecture for edge analytics.
Figure 13-1. Edge analytics architecture ...
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