As the complexity of modern applications continues to increase, so too does the demand for comprehensive observability solutions. Organizations looking to enhance their applications’ performance, reliability, and scalability need powerful tools that allow them to monitor, analyze, and visualize their infrastructure. One such tool is InfluxDB 3.0, a time series database designed to handle large-scale monitoring and analytics workloads.

Just like the classic Scott Bakula tv series, the new InfluxDB 3.0 is a quantum leap forward. Of course, for us it’s the evolution of the InfluxDB product suite. InfluxDB 3.0 is the designation for all products powered by the InfluxDB IOx engine. The latest product release in this new suite is InfluxDB Cloud Dedicated. Let’s jump into the basics for InfluxDB Cloud Dedicated. WHO: There are several different groups of users that should consider using InfluxDB Cloud Dedicated.

Open source project InfluxDB IOx becomes InfluxDB 3.0, the next-generation time series database powering all InfluxDB products. InfluxDB Cloud Dedicated added to suite of enterprise-grade cloud services.

It’s been literally years now that I have been first tangentially, and then intimately involved with the project that has become InfluxDB 3.0. I started using it so early that one of the DataFusion upstream developers literally calls me “User0” … a moniker of which I am not-so-secretly proud.

Today we have exciting news for Grafana customers with Flight SQL data sources: Now there is a new community plugin available for Grafana that allows it to communicate with Flight-SQL-compatible databases. Flight SQL is a client-server protocol developed by the Apache Arrow community for interacting with SQL databases. It utilizes the Flight RPC framework and the Arrow in-memory columnar format.

Databases power all modern applications. They’re behind your Angry Birds mobile game as much as they’re behind the space shuttle. In the beginning, databases were hosted on a single physical machine. Basically, it was a computer running only one program: the database. Then we moved to running databases on virtual machines, where resources are shared among multiple operating systems and applications.