When Mulling IoT Storage Options, Location Matters
“There’s a real wide continuum of device type so they had to develop different types of storage that can operate with very low power to use in IoT devices,” he said. That tends to be newer solid-state storage technology.”
The data an autonomous vehicle uses to decide whether to stop, for example, is processed at the edge for an instantaneous decision, but once the decision is made, that data is thrown away, Burgener said. Decisions about whether a vehicle needs an oil change based on miles driven, however, is collected at the edge then sent back to a remote location for processing and storage.
IoT has no uniform scenario for storage, said Natalya Yezhkova, research vice president, Infrastructure Systems, Platforms and Technologies, at IDC. “Everything is highly driven by a particular use case,” she said. Some large workloads might be shipped to the cloud for analysis and then returned to an on-premises data center for fine-tuning.
There are also considerations for an organization’s comfort level with cloud storage, the amount of data transfer and how much control over bandwidth they have if large amounts of data need to be moved.
Structured data collected by machinery provides information about characteristics of the machine which have been transferred to data center for analysis. Surveillance videos present huge amounts of unstructured data that may need to be analyzed in real time or often, is never used again.
Also, the amount of storage at the edge is determined by several factors, including how far removed the edge is from the core. Low-power sensors on an oil rig in the ocean, for example, might need additional storage and batter power depending on how often data is uploaded from the device.
“Data can’t just stay idle,” she said, if it is to meet the major goals of IoT: analytics to drive improvements in operations and customer experience.
Most IoT data is unstructured and easy to store in public clouds. And all major cloud providers offer scalable storage systems with little to no charge for data ingress. Cloud also offers big data analysis tools for jobs too big for on-premises datacenters.
When considering storage options, users should make sure data management aligns with workloads and the scale of the application.
“It’s not a settled practice of when to use cloud vs. the datacenter,” Yezhkova said. “It’s almost trial and error for some organizations.”