Aggregations are a powerful tool when processing large amounts of time series data. In fact, most of the time you’re going to care more about the min, max, mean, count or last values of your dataset than you will about the raw values you’re collecting. Knowing this, InfluxDB and the Flux language make it as easy as possible to run these aggregations, whenever and wherever you need to, and sometimes that leads people to running them in ways that aren’t as efficient as they could be.

If you’re building an IoT application on top of InfluxDB, you’ll probably use a graphing library to handle your visualization needs. Today we’re going to take a look at the charting library, Highcharts, to visualize our time series data with InfluxDB Cloud. However, I also encourage you to take a look at Giraffe, a React-based visualization library that powers the data visualizations in the InfluxDB 2.0 UI.

One of the best things about getting started with InfluxDB over traditional relational databases is the fact that you don’t need to pre-define your schema in order to write data. This means you can create a bucket and write data in seconds, which can be pretty powerful to developers who care way more about the application they’re building than the mechanics of storing the data.

This post was written by James Hickey. Scroll below for full bio and picture following this article. Time series databases (TSDBs) can transform the way you handle streams of data in real time or IoT applications. In this tutorial, you’ll learn how to set one up in a C# application. Relational databases have their place. They’re great at things like data normalization, avoiding duplication, indexing over specific data points (like columns), and handling atomic changes to the schema.

Directly embedding passwords and API keys into the code you write is a bad practice. Of course, everyone knows this, but I’ll be the first to admit that it still happens now and then. In the world of source control and shared codebases, leaking a password can be a huge problem that costs your team time and money. Of course, today many companies leverage a secrets management system to lower the probability of something like this happening.

So you’re interested in time series databases, and you decided to explore InfluxDB Cloud or InfluxDB v2. Perhaps you just created a free account or downloaded the binary, and now you’re playing around with the InfluxDB User Interface (UI) and learning Flux. The hardest thing for beginners to understand are the return results from a Flux query or Annotated CSV.

Adding annotations to your data is a great way to share context with other members of your team. In May, we added the ability to annotate individual points in your data. Today, we have added the ability to add ranged annotations to your dashboard graphs. We’ve also reworked some of the interactions with annotations based on user feedback so that they can be added quickly and easily. To learn more about working with annotations, check out our documentation.

GitHub Actions are a powerful way to add automation to any source code repository. When you take that power and connect it with InfluxDB, you get an amazing combination that allows you to automate data generation, manage GitOps workflows, and a whole lot more. This post will highlight some of the interesting ways to use InfluxDB and GitHub Actions.

We recently introduced a new Map graph type into InfluxDB Cloud to help users visualize time series data that includes position. Above is a graph showing the most recent earthquakes in California, where the color of the marker indicates their magnitude. In this post, I’m going to walk through the ways to ingest geotemporal data into InfluxDB Cloud, and how to use the new Maps visualization type.

When learning a new technology stack or language, access to good documentation, tutorials, and support is critical to lower the barrier to adoption and enable users to take advantage of the tools themselves. At InfluxData, we support our users by providing the following resources. Searching through all of these resources and more, like GitHub issues, can be time-consuming and difficult. In response, the support team at InfluxData has recently created InfluxData Support.