When we announced that Pyroscope was joining Grafana Labs back in March, we expressed our excitement that uniting the Pyroscope and Phlare open source projects and teams would accelerate our plan to add continuous profiling to Grafana Cloud. Just two and a half months later, that day has come! We are proud to announce that Grafana Cloud Profiles is now available in public preview for all Grafana Cloud users, both paid and free.
Rising container usage has fueled a growing reliance on container orchestration systems such as Kubernetes, EKS, and ECS. As organizations increasingly opt to run these systems in the cloud, their cloud spend tends not only to grow but also to become more opaque due to the dynamic complexity of these environments. Typically, various services, teams, and products share cluster resources, and as nodes are added and removed, those resources continuously shift.
In my last blogpost I explained how our ipl-html lib works and how to use it. With the help of ipl-html it is possible to add forms. Usually we want to validate the data of the form before submitting it and display messages if the validation fails. For this purpose, we have introduced the ipl-validator. The ipl-validator includes many useful validators, and today I want to explain how you can easily use them.
Every data center has a rhythm. When you think about a single application—SAP, for example—it has certain “beats.” First thing Monday morning, you see a steady and fast cadence, while over the weekend, it slows to a light tap. And there may be seasonality that rises to a crescendo.
The dynamic nature of cloud costs can make it difficult to fully understand your cloud spend and embrace cost ownership at all levels of your organization. To establish cost governance, FinOps teams need a complete view of cloud costs, including allocation by team, service, and product. And DevOps teams need to detect, investigate, and quickly mitigate unexpected costs to minimize overruns, even as they continue to build features and operate their services.
In today's fast-paced tech environment, swiftly and efficiently resolving software errors is essential to maintain the seamless operation of your application. A prominent problem for engineering leaders is they often need help tracking and effectively understanding their error resolution performance over time. With a comprehensive, real-time visualization of this data, making informed decisions, setting performance benchmarks, and optimizing resources become easier.
InfluxDB Cloud 3.0 is a versatile time series database built on top of the Apache ecosystem. You can query InfluxDB Cloud with the Apache Arrow Flight SQL interface, which provides SQL support for working with time series data. In this tutorial, we will walk through the process of querying InfluxDB Cloud with Flight SQL, using Go. The Go Flight SQL Client is part of Apache Arrow Flight, a framework for building high-performance data services.
