Herrenknecht AG is a technology leader in mechanized tunneling systems. Engineers at Herrenknecht set out to build an industrial internet of things (IIoT) platform that provided insight into live and historic data for all their tunnel boring machines (TBMs). These machines have thousands of sensors generating high velocity data, sometimes in remote areas with limited connectivity.
We love to write and ship code to help developers bring their ideas and projects to life. That’s why we’re constantly working on improving our product in sync with developer needs to ensure their happiness and accelerate time to awesome. This is the first in a blog series covering our product’s latest features — features that we think will save you time and effort when building with time series and InfluxDB.
This article was originally published in The New Stack. Given how volatile Bitcoin price is, an automated alerting system can be valuable for preserving our attention and sanity. We can pay attention to Bitcoin only when the price action is interesting. Momentum, that is buying an asset that has done well in the past, has been one of the most persistently effective trading strategies — see Clif Asness: “Value and Momentum Everywhere” and Tzouvanas (2019).
The Particle platform enables companies to manage and program their IoT devices, bringing them to market quicker. To accomplish this, Particle developers needed to be able to collect telemetry data from a large number of edge IoT devices to measure performance. A key benefit of the Particle platform is its ability to scale from prototype to enterprise. Developers often prototype IoT devices using microcontrollers like Arduino or Raspberry Pi.
Prescient Devices focuses on changing the way businesses think about and use edge data and industrial IoT. The company built Prescient Designer, an edge data solutions platform that gives organizations the boost they need to transform their IT/OT processes. Prescient’s products empower data engineers, system integrators, and innovators to easily design and orchestrate edge-to-cloud data solutions.
There are multiple ways to use Flux to bring in data from a variety of different sources including SQL databases, other InfluxDB Cloud Accounts, Annotated CSV from a URL, and JSON. However, previously you could only manually construct tables from a JSON object with Flux as described in this first example. We’ll describe how to work with three examples with increasingly complex JSON types. First we will describe how to work with these JSON types with metasyntactic examples.
