When Rolls Royce Power Systems recently needed to improve its operational efficiency within its manufacturing plants, it didn’t expand its use of a legacy process historian or purchase historian connectors to export data to their business intelligence systems. Instead, it decided to go with a modern time series database, InfluxDB. Graphite Energy, another customer we featured in our recent IIoT announcement, also chose InfluxDB over the legacy process historian vendors. Why?

A multicloud strategy is a necessity for modern businesses, as the recent AWS outages made clear, but managing this infrastructure remains a huge challenge. Infrastructure management teams have long struggled to juggle diverse technology solutions, policies and services to get access to a point-in-time view of their resources. The result is either waste through overprovisioning or huge overheads for nitpicking manual management and repetitive tasks.

The question of how to get data into a database is one of the most fundamental aspects of data processing that developers face. Data collection can be challenging enough when you’re dealing with local devices. Adding data from edge devices presents a whole new set of challenges. Yet the exponential increase in IoT edge devices means that companies need proven and reliable ways to collect data from them.

InfluxDB is increasingly being used in IoT solutions to store data from connected devices. Now it can also be used on IoT edge gateways as a data historian to analyze, visualize and eventually transmit aggregated IoT data up to a centralized server. In this article we’re going to look at three simple ways you can connect an instance of InfluxDB on your IoT Edge device to another instance of InfluxDB in the cloud.

Although time series data can be stored in a MySQL or PostgreSQL database, that’s not particularly efficient. If you want to store data that changes every minute (that’s more than half a million data points a year!) from potentially thousands of different sensors, servers, containers, or devices, you’re inevitably going to run into scalability issues. Querying or performing aggregation on this data also leads to performance issues when using relational databases.

While relational database management systems (RDBMS) are efficient with storing tables, columns, and primary keys in a spreadsheet architecture, they become inefficient when there’s a lot of data input received over a long period of time. Databases designed specifically to store time series data are known as time series databases (TSDB). For example, an RDBMS might look like this.

Live and on-demand trainings simplify application building for faster Time to Awesome™ SAN FRANCISCO – March 9, 2022 – InfluxData, creator of the leading time series platform InfluxDB, today announced the launch of InfluxDB University (InfluxDB U), an online education platform for customers and developers working with time series data.

At InfluxData, it’s no surprise that we are passionate about time series data. Our team is committed to helping our community understand its capabilities and sharing easier and more efficient ways of working with InfluxDB, Telegraf and Flux. Our end goal is always to deliver faster Time to Awesome™ for our users. To this end, we’re excited to announce the launch of InfluxDB University.

As a strictly typed language, Flux protects you from a lot of potential runtime failures. However, if you don’t know the column types on the data you’re querying, you might encounter some annoying errors. Suppose you have a bucket that receives regular writes from multiple different streams, and you want to write a task to downsample a measurement from that bucket into another bucket.