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By Przemek Tomczak
Abraham Maslow’s wry observation that “if all you have is a hammer, everything looks like a nail” could unfortunately be applied to many cloud-migration strategies where there is a tendency to see centralized processing as the solution to every problem. But in some cases, reverting to the cloud is neither necessary nor indeed advisable. Consider a vehicle in motion and its embedded control systems: while some decisions such as best route to take may rely on external information and tolerate a reasonable time delay, others like whether to brake, accelerate or swerve to avoid an obstacle are based solely on information at hand and must be taken within milliseconds.
The same is true of decisions on the factory floor in relation to high performance tooling and systems. They cannot wait, and nor do they need to, for information from external systems or centralised services to determine next steps. It’s about making decisions locally based on information at hand. It’s about the using the edge, and using it intelligently.
What can be achieved at the Edge?
The concept of intelligence at the edge is not new, it was identified by Tim Bittman in Gartner some time ago and is gaining significant traction as its business benefits become increasingly apparent: improving quality by diagnosing problems faster, extending uptime by making corrective actions sooner, and saving costs by utilizing assets better. Specific examples of where edge intelligence has been introduced include:
In each case above the edge engine is KX. This is why.
The challenge of Intelligent Edge processing
In order to determine the most appropriate action, and determine it instantly, an intelligent edge system needs data and lots of it. Its sources may include the multitude of sensors now available in the IoT world: chemical, biosensors, image, audio, speed, vibration, humidity, pressure, voltage, amplitude, location that, along with related context data (including master data and historical data) must be ingested, processed, and analyzed in the right place and right time. The right place is close to the data, the right time is now.
However, most IoT platforms and analytics solutions were not designed for local processing at that level of speed or sophistication. They are mainly storage systems with limited enrichment and analytics capabilities and rely instead on data being delivered to and from data central facilities far removed from where the response is needed, for advanced analytics. So, while cloud and central services are invaluable in general, the microseconds lost in moving data to the cloud and getting back the recommended action may render the result worthless in specific cases where the error has happened, the damage has been done, or the opportunity has gone. Achieving edge intelligence needs something extra.
KX Streaming Analytics platform
KX is an integrated lightweight platform providing data ingestion, processing, computation, analytics, visualization, and machine learning at both the edge and centrally in the cloud. It empowers intelligent systems and IoT platforms with high-speed, low latency data processing and historian capabilities while delivering data to cloud and on-premises data systems as needed. It integrates with IOT platforms and industry applications to move beyond store and forward to combined analytics of OT and IT data.
(Diagram courtesy of the IIC Consortium)
KX uniquely fits into the processing ecosystem across the three tiers above
These capabilities are delivered from a time-series, relational, and columnar database that includes built-in analytics libraries and a programming language for developing customised code and complex queries. It is delivered via a low footprint (<1MB) executable that imposes minimal infrastructure requirements and runs on Intel, ARM, and AMD CPUs, Linux, Windows, and MACOS.
Among the key feature of the platform that enable Intelligent Edge are
A recent partnership with Ori Industries, a global edge computing infrastructure firm, illustrates how these capabilities can be applied in reducing the decision-making window using real-time data analytics in the telco world. The integration of Ori’s Global Edge (OGE) platform and KX enables real-time in-stream analytics at the edge, significantly increasing the speed at which data can be analysed while also reducing the amount of data that needs to be sent to the cloud for processing. As commented by Douglas Mancini, Chief Commercial Officer at Ori Industries : “In pairing one of the industry’s fastest streaming analytics solutions with our low latency delivery stack we’re enabling telcos to leverage their networks like never before, opening up the true promise of 5G.”
That’s what intelligence at the edge can do.