Streaming analytics
            

Streaming analytics is essential for enterprises that want to extract immediate insights from fast and ever-growing volumes of data. As the number of data streams expand, streaming analytics enables enterprises to analyze and integrate information in real time from Internet of Things (IoT) sensors, markets, mobile devices, internal transactional systems, clickstream analysis and many other sources.

What is streaming analytics?

Streaming real-time analytics enables you to access, analyze and act on both historical and real-time, fast-moving live data from IoT devices to determine if there is an issue relating to equipment and to prevent future problems.

Why use streaming analytics?
In any environment using equipment, you want to be able to spot problems before they become an issue. You need to know if there are signs of possible equipment failure—such as temperature too high or pressure too low. You need to analyze this information, learn from it and act. That’s where streaming analytics comes in. With streaming real-time analytics, you can prevent certain events from happening in the first place. You can predict and detect significant business events the moment they occur, when it matters most, making it possible to minimize risk and maximize your gain.

Sample use cases
Companies are relying on real-time analytics as big and fast data proliferates and more and more data streams are generated in real time from the IoT as well as markets, mobile devices, clickstreams and internal transactional systems.

With streaming real-time analytics, you can:

  • Design, develop and deploy sophisticated analytics that monitor any number of event streams and event data of any kind
  • Detect and analyze patterns from many sources at the same time
  • Respond to events the moment they happen—or even before, when using predictive models
  • Automate responses to take intelligent actions instantly without human intervention
  • Spot significant patterns of events, like a change in pressure or temperature, which could indicate pending equipment failure

One company uses streaming analytics based on an aggregate view of the data to apply diagnostics along a conveyor belt in a factory. If an item fails, then it is pulled from the belt. If there is no problem, it is not pulled from the conveyor belt.

Key considerations
When choosing a solution for streaming analytics, make sure it’s really “real time” so you can act on insights when they matter. Ask:

  • Will analytics be accessible to a wide range of people in your organization?
  • Will you need to manually push a software configuration or a firmware update to the edge?
  • Will you be able to plug into a data capture pipeline to reduce latency of actionable insights?
  • Will you be able to connect third-party products that benefit the rest of the business?
  • Will you have full control of the device from a support and management perspective?
  • Will you need an army of software engineers to architect and build your analytics solution?

Benefits of streaming analytics

Software AG’s Cumulocity IoT Streaming Analytics is an end-to-end, modular and integrated set of world-class capabilities optimized for high-speed analytics and machine learning on real-time data. You can access, analyze and act on both historical and real-time, fast-moving live data from IoT devices, to determine if there is an issue relating to equipment and to prevent future issues.

Streaming analytics in Cumulocity IoT is powered by Apama, the industry's leading streaming analytics engine. Apama is proven in many different environments, from the Internet of Things to high-frequency trading in capital markets.

Self-service analytics
With Cumulocity IoT, anyone can define streaming analytics using easy-to-connect building blocks. No coding is required. Operational technicians, factory-floor engineers and analysts can build analytics on their own to improve operational efficiencies faster.

Using an intuitive interface, you can design models that look for matching patterns in live data coming from your machines and take appropriate action. Simply “drag and drop” to define how to act in real time on what’s happening in your production lines or on your factory floor.

Using a library of analytic blocks, you can:

  • Identify threshold breaches
  • Calculate averages and standard deviations
  • Calculate weighted linear regression gradients
  • Discover missing data

You can even create your own custom analytics blocks using the Analytics Block SDK.

Preset smart rules
Wizard-driven pre-designed smart rules help you quickly and easily create rules. These are designed with the operational user in mind, so that you can set alarms and events without coding.

Using machine learning models
Leverage machine learning models to supplement or replace manual processes with automated systems using statistically derived actions in critical processes. No matter where your applications are running—in the IoT, a distributed environment, the cloud or on a mainframe—you can execute, optimize and scale models without allocating dedicated IT resources. This includes deep neural network models built using Keras, Caffe or TensorFlow®.

Available at the edge and/or in the cloud
Use streaming analytics capabilities where you see fit—on edge devices, in the cloud or on on-premises servers—to analyze and filter data at a local level before passing it to the back-end for more processing. Because you’ll be using the same streaming analytics engine on the cloud all the way to the edge, you can develop a streaming analytics app once and deploy everywhere.

Coding for advanced use cases
Cumulocity IoT Streaming Analytics gives you a coding environment where you can create custom apps and behavior to suit even the most complex and in-depth streaming analytics use cases. Developers have a full set of tools to create even the most advanced streaming analytics projects.