How Prescient Devices Uses Time Series Data for IoT Automation
Susannah Brodnitz /
Dec 07, 2022
This article was originally published in The New Stack and is reposted here with permission.
Companies need to consider both how fast they can put edge applications into action and update them, and how quickly they can process incoming data.
Industrial processes are becoming increasingly automated as sensors on machines collect a growing amount of data. Much of this data is time-stamped and can help companies improve processes. This large volume of sensor data can become unwieldy if companies don’t manage it properly.
Companies need to be able to handle the life cycle of time series data, from real-time analysis to downsampling of historical data. Many companies use purpose-built data management tools and products to help with this.
Connecting edge and cloud
Prescient Devices created Prescient Designer, a SaaS platform to help businesses manage distributed edge devices. It has an agent that users install on edge devices and an interface where they build and implement applications. InfluxDB is the backbone of Prescient Designer and handles time-stamped data.
Prescient Devices built the platform using Node-RED, an open source project that has a low-code visual interface. Prescient Designer also uses InfluxDB Cloud, TensorFlow, and Grafana and can integrate with other tools that companies require.
Prescient Edge is Prescient Devices’ edge management software. It uses Node-RED, InfluxDB OSS, TensorFlow, and a runtime system. Prescient Edge deployments can include applications built by users in Prescient Designer. Data from edge devices come in any number of formats. Prescient Devices enables users to collect a wide variety of sensor data, such as temperature, humidity, and acceleration, as well as data from cameras, APIs, and industrial equipment.
Data come into Prescient Designer in two separate streams, one for user data and one for application management and deployment data. Prescient Devices includes a broker to connect Prescient Designer and devices running Prescient Edge.
Their system also supports custom brokers if users need support for compliance requirements like HIPAA or GDPR. For data visualization, Prescient Designer leverages Grafana dashboards to provide users insight into their applications in real time.
Prescient Designer is based on Node-RED and lets users create applications using minimal code. This creates a lower barrier to entry and makes it easier for companies to develop applications and solutions quickly.
This approach also enables companies to include subject-matter experts who aren’t developers more directly in building applications. So, key stakeholders like data engineers, system integrators, and other innovators can all use Prescient Devices to build better edge-to-cloud data solutions, without having to be experienced coders. Prescient Designer’s visual workspace also lets users get a view of their whole system in one place.
The two key issues with data that Prescient Devices helps solve are scale and speed. IoT environments have huge volumes of data coming from hundreds of devices that each process a significant amount of data. Companies need to consider both how fast they can put edge applications into action and update them and how quickly they can process incoming data.
Prescient Devices initially used Telegraf, InfluxData’s open source plug-in–driven agent for collecting metrics, to collect and process data from edge devices. They ultimately chose to use InfluxDB Cloud as their time series database because it supports Node-RED and runs well on edge devices with limited resources. Many of Prescient Devices’s customers already used InfluxDB and specifically requested InfluxDB support in the platform.
Prescient Designer lets IoT companies create distributed systems quickly. Users can build applications within weeks and update and deploy changes to those applications within hours to seconds. Because users can build applications within a visual interface, experts can build applications even if they aren’t developers. This lets IoT manufacturers automate their systems quickly and get better insights from their data.