HPE Focuses on Edge Servers for IoT Data Collection and Analytics
Hewlett Packard Enterprise’s Internet of Things (IoT) solution gained attention at the HPE Discover 2016 conference in June by leveraging HPE’s converged technology in systems designed for IoT data collection and analytics.
Details about the announcement can be seen here.
HPE sees IoT – and the new IoT edge servers – as an important avenue for expanding its server business. Use-case examples for this category of edge servers include analyzing the sensor data generated in retail, manufacturing, oil/gas, health care, transportation and engineering. Scenarios include failure prediction, machine learning, preventative maintenance, and real-time monitoring of industrial processes and manufacturing devices.
Providing context for collected data, where it originates in the field, is key to this announcement, which was made at HPE Discover, the company’s largest annual gathering of customers in North America. These IoT systems will also be seen at HPE Discover in London in November.
The company harmonized several products for its latest IoT solutions:
- HPE’s new Edgeline EL1000 and EL4000 Converged IoT Systems combine deep edge compute, storage, networking and software for data collection, control and data analytics.
- The HPE Vertica software, supporting scalable data analysis, runs analytics directly on the EL1000 and EL4000 converged systems – and it can link to larger Vertica deployments housed in enterprise data centers.
- HPE Aruba ClearPass 6.6 security software, which classifies all connected devices sending sensor data, supports automated threat protection and recovery for devices.
- Enterprise services and consulting, and a partner ecosystem focused on IoT deployments also surround the Edgeline systems.
Context for the Data
Much of the industry conversation surrounding IoT is about gathering up as much data as possible, as quickly as possible. The creation of large datasets is inevitable, given the dozens, hundreds or thousands of sensors feeding data into edge servers for initial processing. This is a type of processing that occurs before the data travels into the enterprise data center, making data quality, analytics performance and security top priorities.
Speed is important, but so is data quality. By highlighting the importance of contextual data and contextual information, HP is focusing on the importance of determining data priority from the time it is collected in the field. This early processing enhances end-to-end data security, and aids in data governance and regulatory compliance. HP explains it as “shifting” more of the early processing to the edge of the network, before data enters the central-site data center.
HPE designed the Edgeline systems to shorten the time to solution for analytics of recently acquired data. That, in turn, brings data analytics closer to the places where the data is collected, supporting data aggregation, pre-processing, closed loop analysis, while improving data quality and protecting data security.
Combining several of its point-products into this combined IoT solution plays to HPE’s strengths in high-volume x86 servers and gateway devices. By doing this, HPE is leveraging a wide installed base in data centers – and its wide presence in SMB sites that have been using HP/Compaq ProLiant systems for many years. Further, it is leveraging converged technology, which was HP has emphasized for years as a key technology for dense servers and blades in the data center.
Competition for the new HPE IoT edge servers will come from IBM/Cisco, Dell and IoT specialty-device providers. All of these providers are targeting the emerging IoT business, given its rapid growth characteristics, and the opportunity to develop new edge and gateway systems supporting end-to-end IoT analytics.
HPE is wise to leverage its experience in converged systems into a form-factor that’s on the edge of enterprise networks. We expect to see this approach become more widespread throughout the industry, as edge servers’ role in data analytics becomes more pronounced — as a vital part of the race to improve business outcomes via IoT.