How edge analytics will drive smarter computing

Many analytics and machine learning use cases connect to data stored in data warehouses or data lakes, run algorithms on complete data sets or a subset of the data, and compute results on cloud architectures. This approach works well when the data doesn’t change frequently. But what if the data does change frequently?

Today, more businesses need to process data and compute analytics in real-time. IoT drives much of this paradigm shift as data streaming from sensors requires immediate processing and analytics to control downstream systems. Real-time analytics is also important in many industries including healthcare, financial services, manufacturing, and advertising, where small changes in the data can have significant financial, health, safety, and other business impacts.

If you’re interested in enabling real-time analytics—and in emerging technologies that leverage a mix of edge computing, AR/VR, IoT sensors at scale, and machine learning at scale—then understanding the design considerations for edge analytics is important. Edge computing use cases such as autonomous drones, smart cities, retail chain management, and augmented reality gaming networks all target deploying large scale, highly reliable edge analytics.

Edge analytics, streaming analytics, and edge computing

Several different analytics, machine learning, and edge computing paradigms are related to edge analytics:

  • Edge analytics refers to analytics and machine learning algorithms deployed to infrastructure outside of cloud infrastructure and “on the edge” in geographically localized infrastructure.
  • Streaming analytics refers to computing analytics in real time as data is processed. Streaming analytics can be done in the cloud or on the edge depending on the use case.
  • Event processing is a way to process data and drive decisions in real time. This processing is a subset of streaming analytics, and developers use event-driven architectures to identify events and trigger downstream actions.
  • Edge computing refers to deploying computation to edge devices and network infrastructure.
  • Fog computing is a more generalized architecture that splits computation among edge, near edge, and cloud computing environments.

When designing solutions requiring edge analytics, architects must consider physical and power constraints, network costs and reliability, security considerations, and processing requirements.  

Reasons to deploy analytics on the edge

You might ask why you would deploy infrastructure to the edge for analytics? There are technical, cost, and compliance considerations that factor into these decisions.

Copyright © 2020 IDG Communications, Inc.

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