- Community Home
- >
- Solutions
- >
- Tech Insights
- >
- When Big Data analytics needs to happen at the edg...
Categories
Company
Local Language
Forums
Discussions
Forums
- Data Protection and Retention
- Entry Storage Systems
- Legacy
- Midrange and Enterprise Storage
- Storage Networking
- HPE Nimble Storage
Discussions
Discussions
Discussions
Forums
Forums
Discussions
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
- BladeSystem Infrastructure and Application Solutions
- Appliance Servers
- Alpha Servers
- BackOffice Products
- Internet Products
- HPE 9000 and HPE e3000 Servers
- Networking
- Netservers
- Secure OS Software for Linux
- Server Management (Insight Manager 7)
- Windows Server 2003
- Operating System - Tru64 Unix
- ProLiant Deployment and Provisioning
- Linux-Based Community / Regional
- Microsoft System Center Integration
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Community
Resources
Forums
Blogs
- Subscribe to RSS Feed
- Mark as New
- Mark as Read
- Bookmark
- Receive email notifications
- Printer Friendly Page
- Report Inappropriate Content
When Big Data analytics needs to happen at the edge
by Eddy Biesemans
Global Account Manager
A decade ago, when the concept of big data analytics began to gain steam, attention turned to banking, equities, insurance, advertising, biopharma, and other business and scientific fields that generated lots of digital data. Analysis could take place in the data center or in the cloud, giving companies valuable insights into customer behavior, product performance, and research.
Few anticipated the exponential growth of data from industrial IoT systems. Now, just like their counterparts in business and scientific fields, managers in manufacturing, oil & gas, energy grids, smart cities, transportation, and agriculture want to derive actionable insights using big data analytics. But unlike white-collar fields, industrial big data canโt be compiled and analyzed in data centers or in the cloud. It has to take place on the edge of the network, right where the action is.
Big data analytics use case: self-driving cars
HPE works closely with engineers, data architects, and customers on projects ranging from cyber security to smart grids. We also have an ongoing project for a client in the automotive industry that uses big data analytics on the edge.
This company is developing a new autonomous self-driving car. The testing regimen requires some 22 million hours of driving time to evaluate and analyze multiple automotive systems in a wide variety of conditions. If you calculate that forward, it comes to approximately 2,500 years! No one can wait that long, of course.
The technology we have jointly deployed with Schneider Electric is a big data analytics appliance. There are 40 of them collocated in locations around the world where testing is taking place. Instead of one car driving 22 million hours, there are thousands of vehicles driving around for thousands of hours each. The cars have IoT sensors monitoring speed, road conditions, and nearby objects such as pedestrians, curbs, and other vehicles. When the test units reach one of the 40 collection points or branch offices, IoT data collected during the trial is transmitted to the appliance. The data is automatically processed and analyzed at the edge, with the results sent back to headquarters.
Why do it this way? Consider the alternative: If the big data packages from the trials had to be transmitted to the data center or cloud, it would be nearly impossible.
Instead of bringing data to the analytics applications at the data center or cloud, the edge appliances bring analytics to the data as itโs being gathered. The analytical models run on every one of the 40 appliances, enabling real-time processing at the edge. Only the results are uploaded to HQ, avoiding all of the problems with latency, timing, and availability. Critically, this approach contributes to a much faster time to market.
Running big data analytics on the edge is applicable to other industries as well. Basically, any use case that requires big data on the edge is a candidate. We are looking at oil and gas, energy grids, and other industrial scenarios in which lots of IoT big data needs to be processed on the plant floor or out in the field.
Join HPE at Industry of Things World in Berlin on September 23-25 to learn more about using data for the manufacturing process - particularly the concept of closed-loop manufacturing - and applying AI in the industrial setting to make sense of this data. Get a preview of the agenda here.
HPE Edgeline Converged Edge Systems is powered by Intelยฎ Xeonยฎ.
- Back to Blog
- Newer Article
- Older Article
- Amy Saunders on: Smart buildings and the future of automation
- Sandeep Pendharkar on: From rainbows and unicorns to real recognition of ...
- Anni1 on: Modern use cases for video analytics
- Terry Hughes on: CuBE Packaging improves manufacturing productivity...
- Sarah Leslie on: IoT in The Post-Digital Era is Upon Us โ Are You R...
- Marty Poniatowski on: Seamlessly scaling HPC and AI initiatives with HPE...
- Sabine Sauter on: 2018 AI review: A year of innovation
- Innovation Champ on: How the Internet of Things Is Cultivating a New Vi...
- Bestvela on: Unleash the power of the cloud, right at your edge...
- Balconycrops on: HPE at Mobile World Congress: Creating a better fu...
-
5G
2 -
Artificial Intelligence
101 -
business continuity
1 -
climate change
1 -
cyber resilience
1 -
cyberresilience
1 -
cybersecurity
1 -
Edge and IoT
97 -
HPE GreenLake
1 -
resilience
1 -
security
1 -
Telco
108