IoT on the Edge —Data Orchestration in a Hyperconnected World
We’ve seen many oscillations between centralized and decentralized computing over a period of decades. Mainframes, PCs, Mobile, Cloud and other more granular innovations along the way mark these swings along a circuitous trendline. For a moment it seemed that Cloud computing would have an extended rule in the kingdom of compute — perhaps it will. But even if Cloud computing is here to stay, the pendulum is about to drop hard and fast in the other direction as we move from millions to trillions of connected devices.
Peter Levine described it best — even if his headline is a tad hyperbolic — The End of Cloud Computing.
The internet-of-things imagines a world where people, services and machines “talk” with seamless data chatter —optimizing innumerable human interactions and business processes. In IoT terms, decentralized computing is often referred to as Edge or Fog computing. As devices become more capable, the Edge gets smarter — and like Moore’s Law, that tendency will not slow anytime soon.
A smarter Edge means near instantaneous local actions — actually reactions to real-time data — regardless of available connectivity to the Cloud. Sensors, analytics, database functions and local communication protocols are all part of an ecosystem of services that must work together at the Edge (i.e. interoperability) to create industrial efficiencies, minimize physical danger, cut costs, save resources, lock-down security, or delight consumers. Edge intelligence will also determine which events should be pushed to the Cloud for aggregate or historical analysis — turning fire hoses of data into more usable garden hoses of intelligent data flow.
What we do at flowthings is called “data orchestration” — the logic that defines how data flows between devices, data services, applications and people to produce desired outcomes. Orchestration is all about simplifying complex data flow processes. It’s the “glue” that connects data silos across the IoT ecosystem — freeing real-time data to do its job.
What separates flowthings from the field is our ability to produce orchestration instructions that execute at the Edge or in the Cloud as java bytecode — using the same developer APIs and graphical workflow tools no matter where those instructions are executed.
flowthings data orchestration at the Edge is defined by sets of rules that tell devices and various data services how and when to communicate with each other. Orchestration includes data-driven instructions that define when to shut down devices, when to push data to the Cloud, how to create interoperability between data services, software and hardware from different vendors, when and how to authenticate identities for data access and sharing, and how to share compute resources between Edge devices.
flowthings orchestration can be created and visualized (optionally) with drag-and-drop workflow graphs that describe the concurrent paths data must take to produce desired results. The flowthings editor is usable by business analysts and others with little or no coding experience to create or modify live deployments, explore new use cases, or test assumptions with live or simulated data.
While flowthings enables transforms, time series analysis and other analytic functions, we focus entirely on explicit, rules-based functions — not implicit functions like natural language processing or complex pattern recognition. flowthings data orchestration can be understood as the “IoT nervous system” that connects the “brain” (e.g. deep machine learning) and “senses” of the physical world (e.g. sensors) with any number of data services, applications, machines and people.
Controlling and securing the end-to-end workflow of data-in-motion is at the core of building repeatable solutions. Our belief is that data orchestration, especially at the Edge, is the next great enabler of the next generation of industrial, enterprise and consumer IoT applications. flowthings is the clear technology leader in this space.