Operations | Monitoring | ITSM | DevOps | Cloud

The promise of software as a service (SaaS) has always been value-based pricing. Rather than owning a suite of products they might not need, customers can choose only the services they do need to drive specific outcomes for their businesses. From day one, SaaS was about value. More often than not, products in the enterprise software space fail to live up to this ideal. In light of that, we came up with a crazy idea to help customers accelerate time to value. The idea clearly struck a chord.

Grafana Tempo 2.0 is finally here, and it’s being released with two new important features. It took us longer than we would have liked to get this release going, but it turns out that rewriting your backend AND building a new query language is quite difficult. Thanks to a massive team effort, we are proud to release Tempo with support for TraceQL and with Apache Parquet as the default backend storage format. Read on to get a quick overview of this huge release.

Here at Grafana Labs, we aim to create products which integrate well with open standards and are easy to install everywhere. Today, we’re excited to announce Grafana Agent v0.31, which allows you to connect to even more types of observability signals for both scraping and remote writes. And to help you install the Agent more easily, there is now an official Windows Docker image and an official Helm Chart. Here’s a breakdown of the latest features and upgrades in Grafana Agent v0.31.

Imagine starting your car in the morning and having your attention captured by a little red check engine light. After expressing frustration in your own unique way, your next objective is to determine why this little light has brought darkness to your morning. Your owner's manual clearly outlines how to operate and routinely maintain your vehicle, but all you know about this little light is that you’ll soon be meeting your local mechanic.

This release has some particularly interesting features that we’ve been wanting to ship for a while now. We’re excited to share them with you! For those who aren’t familiar with Multipass, it’s software that streamlines every aspect of managing and working with virtual machines. We’ve found that development, particularly for cloud applications, can often involve a huge amount of tedious work setting up development and testing environments.

Network observability is critical. You need the ability to answer any question about your network—across clouds, on-prem, edge locations, and user devices—quickly and easily. But network observability is not always easy. To be successful, you need to collect network telemetry, and that telemetry needs to be extensive and diverse. And once you have that raw telemetry data, you need to interpret it.

Back in October 2022, our Founder and CTO Paul Dix announced the limited release of InfluxDB IOx, our new database engine. After several months of beta testing, we’re excited to announce the next phase of our database engine: general availability. As of today, InfluxDB IOx releases to the rest of the world as the new and improved InfluxDB Cloud.

Some things are inevitable, like Thanos, paying taxes, and change. While it would be nice to simply snap our fingers and deliver new products, things aren’t so simple in the real world. InfluxDB has been the leading time series database since January 2016. But we’re not content to rest on our laurels. The quest to improve InfluxDB is constant and ongoing. As of today, we’re beginning the rollout of an all-new and improved InfluxDB Cloud powered by IOx.

Unit testing is a software testing method in which individual units or components of a software application are tested in isolation from the rest of the system. The goal of unit testing is to validate that each unit of the software application is working as intended. In this article, we’ll cover the benefits of unit testing and demonstrate how to run a unit test in Python.

Debugging complex code can be an intimidating and time-consuming task, but it doesn’t have to be. With the right strategies, you can quickly identify and remedy issues in your code. In this article, we’ll cover some of the most effective techniques for debugging complex code including using print statements, breakpoints, test-driven development, refactoring for readability, utilizing debuggers and automated tools – plus more!