Edge Streaming Analytics

What is Edge Streaming Analytics?

Edge streaming analytics is ingesting a continuous data stream as it’s being created on a device to quickly filter and analyze it in real time. Organizations often use this kind of distributed computation system to get immediate decisions on data that is too substantial to transfer quickly to the cloud.

What does edge streaming analytics do?

Edge streaming analytics is essentially an automated compute process done relatively close to the sensor or at a gateway nearby. The stream processing engine takes in the data continuously and runs it through an algorithm to make determinations, monitoring for events that might trigger automatic actions or even just deciding how much of the data is valuable enough to send to a central processor for further action or to the cloud or a storage device. Because it analyses data as it’s generated, it decreases latency in decision-making for connected devices.

What is edge streaming analytics in IoT?

Most organizations run a complex and interconnected system of devices, all creating data that can build into a massive glut of information if it’s not continuously processed. By running data through an analytics algorithm as it’s created at the edge of a corporate network, you can gain faster insights to find new ways to improve efficiency, engage customers, and develop new business.

Why are edge streaming analytics needed?

As the on-demand economy continues to urge companies to deliver more quickly than ever, businesses need to deliver better services at the point of consumption and avoid any lags caused by using remote data centers or clouds. And as the number of connected devices deployed by organizations increases, the volume of data that needs to be processed is growing too, which can quickly overwhelm central data management systems. Edge analytics help enterprises improve real-time business analytics and facilitate faster decisions when time is critical.

What are the benefits of edge streaming analytics?

As organizations create smarter buildings, cities, workspaces, retail experiences, factory floors, and more, there is a huge opportunity to use the information gleaned from all the connected objects—and in real-time. The benefits of edge streaming analytics are many, including:

Improved uptime

Because the data is processed on-site rather than being transmitted to a far-off central location, and because enterprise IT is able to look at hardware performance data constantly, it can help organizations develop the foresight to predict and head off failures and avoid unplanned downtime.


Sensors can automatically shut down a machine or take corrective action when a repair is needed. Edge streaming analytics can also speed information to the team to fix it, rather than sending the alert to a central processing location first. And in scientific or engineering enterprises, the rapid-fire generation of real information can accelerate innovation and human progress.


Because the computational workload is handled at each device, the overall burden is shared across the ecosystem so it can be processed much more efficiently.


By distributing the data processing across edge computing infrastructure, an organization can reduce data transmission and storage costs. In addition, by learning about the health and performance of devices in real-time, repairs and maintenance costs can be tailored to need rather than a broader schedule, which leads to lower operational expenses.


Because data is processed at the device, it doesn’t need to be transmitted across the network, which exposes it to risk. Raw data never leaves the device that created it.


When even the tiniest error or delay could spell catastrophe, such as in autonomous driving, local oversight, turn by turn, is critical.

HPE and edge streaming analytics

As edge workloads and data volumes continue to increase dramatically, many organizations lack the technical depth or expertise to get their edge programs to the next level. HPE experts have been leading innovation strategies for decades and can help you manage the decentralized world of edge with broad and deep experience in analytics and edge/IoT services. We can help you manage network connectivity for all your edge sites and implement solutions to integrate and handle the continuous streams of value hidden in your data.

HPE GreenLake edge-to-cloud platform can help enterprises improve real-time business analytics by providing, managing, and securing edge deployments as a service. With HPE GreenLake, rather than trying to control your continually expanding digital ecosystems yourself, you’ll be able to turn over that work to HPE experts who will set you up correctly, manage it all cost-effectively, and ensure everything is done as securely as possible.