EDGE ANALYTICS AND FAST DATA: THE CORE OF IOT

IoT has evolved from a conceptual framework into a core component of modern data infrastructure. This shift has introduced a new challenge. How do you process and extract value from continuous, high-volume data streams? Two concepts have emerged as central to addressing this challenge: fast data and edge analytics.

Fast Data Streams and Big Data

We’ve all heard the term “big data,” referring to the analysis of extraordinarily large datasets stored on servers. It’s the cornerstone of the cloud. This analysis enables valuable insight across a wide range of use cases, from identifying new revenue opportunities to understanding why a failure occurred and how to prevent it in the future.

Insights are extracted from fast data streams, in which continuous flows of data move into servers, applications, and other computing environments from thousands or millions of endpoints. This is the essence of IoT. More devices and equipment have built-in sensors, and they generate and transmit an unprecedented volume of data.

To put it into perspective, a single Airbus A350-1000 commercial aircraft has nearly 6000 sensors throughout the plane and generates an average of 2.5 TB of data per day. And the volume continues to grow. Research suggests the number of connected IoT devices will reach 39 billion by 2030, reflecting continued growth in connected systems across industries.

Analyzing this much data presents an incredible challenge. The cost and complexity of transferring, storing, and processing it can be prohibitive. Even when it’s feasible, the value of the data can be limited if it can only be analyzed after the fact. Fully leveraging IoT means analyzing fast data streams in real time so teams can optimize operations and introduce predictive capabilities.

This is where edge analytics comes into play.

Edge Analytics and Processing Fast Data 

If the server side is considered “the cloud” and associated with big data, then “the edge” refers to the devices and sensors that generate data and send it to the cloud for processing. Historically, these endpoints have been relatively unintelligent, serving primarily as data transmitters.

Edge analytics shifted a significant portion of processing away from centralized servers and closer to where the data is generated. This can be achieved using smart devices, IoT gateways, onsite servers, or a combination of these approaches. With the introduction of edge AI, edge analytics has become even more effective in identifying patterns and making more informed decisions directly at the source.

Further, rising memory and storage costs for RAM and SSD are putting increased pressure on centralized architectures. When edge devices can analyze, filter, and act on data in real time, only the most relevant information needs to be stored and transmitted. This improves responsiveness and reduces the growing cost burden associated with storing and managing massive volumes of raw data.

Potential Applications

There are many possible use cases for edge analytics. To illustrate the range of possibilities, here are a few examples that highlight how this technology can be applied:

• Workplace Safety: In sectors such as mining, gas, and raw materials processing, heavy machinery is in constant use. Failures in pumps, fans, and motors can lead to significant financial loss, and more importantly, risk to human life. By analyzing sensor data like peak acceleration, vibration, temperature and humidity in real time, operators can prevent accidents before they happen.
• Healthcare Monitoring: Through smart devices, sensors, and patient reporting, edge analytics can be used for remote patient monitoring. Alerts can trigger action for caregivers and healthcare providers when certain parameters fall outside of acceptable ranges.
• Kiosk and Vending Systems: Edge analytics enables real-time tracking of customer buying patterns to better forecast inventory. It also supports predictive maintenance by identifying component issues before failure and helps maintain consistent product quality across machines.
• Security Analytics: Edge-based systems can perform tasks like facial recognition and on-site inferencing directly within surveillance devices. By analyzing video streams locally, these systems can identify individuals, detect anomalies, and trigger immediate responses for faster, more reliable security decisions.

Are you optimizing where and how your data is processed? Explore New Era Electronics’ edge computing solutions.