The Internet of Things (IoT) is increasingly driven by sensor data, with devices taking measured actions based on everything from wind speed and direction, vital body functions, illumination intensity, and temperature.
In this session we will showcase how to build a fully functional sample IoT monitoring application built with javascript and utilizing InfluxDB as its backend. With integrations to visualization libraries such as Plotly, creating automated alerts with InfluxDB as well as data downsampling.

When monitoring Node.js, you can track your applications’ performance and availability by finding bottlenecks and fixing errors. You can identify issues by specifically looking at metrics like Process memory usage, Average response time, CPU usage, and more. If you add monitoring of the other components of your entire stack, you will gain a comprehensive view of what could be impacting application performance.  At that point, it can point out the problem at the code level — allowing you to track down and fix those issues before they negatively impact end user experience.  This talk will focus on the tools available from the Open Source time series database InfluxDB. It will be using the open source node.js telegraf plugin, so you can easily collect key metrics to help you get that view into your application. We will be using the Node.js Monitoring Template which is prebuilt and equipped to monitor an applications' performance and availability. All code examples will be in Javascript, and we will also go over the javascript library for those who are working in other javascript server environments, or who want to export data to their preferred visualization tools. 

In this talk, Zoe Steinkamp will talk about Giraffe, an open source visualization library that powers data visualizations in the InfluxDB 2.0 UI. Giraffe can be used to display your data within your own app and is Fluxlang-supported! It uses algorithms to handle visualizing high volumes of time series data that InfluxDB can ingest and query.