Operations | Monitoring | ITSM | DevOps | Cloud

When multiple teams use Grafana, it can start to feel a bit messy. Dashboards pile up, permissions become unclear, and teams accidentally overwrite each other’s work. To help you and your organization stay clear, collaborative, and secure, we recommend putting all users in a single Grafana Cloud stack and managing access with teams, roles, and folders. To illustrate this, I’ll share a hypothetical example of how you can put this into practice across three teams. Let’s dive in!

A year ago, we hit a wall. Our Grafana OSS releases were excruciating to execute. The process was confusing and hard to follow, security patches were non-trivial, and many engineering hours were lost to an overly manual process. We needed to move fast, cut through ambiguity, and pull in just the right people without waiting on roadmaps or org charts.

Since its launch in 2020, the Infinity data source for Grafana has become the go-to solution to seamlessly query and visualize data from JSON, CSV, XML, and GraphQL endpoints within Grafana. Allowing users to integrate diverse data formats via HTTP-based APIs, the Infinity data source has enabled a wide range of use cases within our community over the years — from visualizing cloud computing costs to popular Pokémon games.

There’s a new way to ingest AWS metrics into Grafana Cloud that makes observing your AWS resources more cost-effective, easier to operate, and more accurate. You can now stream metrics into the AWS Observability app in Grafana Cloud in near real-time thanks to our new integration with Amazon CloudWatch and Amazon Data Firehose. We’re already using it internally, and we’re finding that it’s not only easier to operate—it’s at least five times more cost-effective.

One of the biggest challenges in frontend development is identifying when and why users encounter performance issues, whether it’s slow page loads, JavaScript errors, or failed HTTP requests. With Grafana Cloud Frontend Observability — a hosted service for real user monitoring (RUM) — you get immediate, clear, and actionable insights into the end-user experience of your web applications.

Note: A version of this post originally appeared on the OpenTelemetry blog. Victoria Nduka is user experience designer and open source contributor making her way into the cloud native space. She writes about design, accessibility, and open source with the same curiosity she brings to her work. On May 29, I wrapped up my mentorship with Prometheus through the Linux Foundation mentorship program.

Imagine you’re a frontend engineer monitoring the user experience for an e-commerce app. You notice your checkout flow has a 15% abandonment rate. Your API responses are inconsistent. Your users are frustrated, and you’re drowning in data and complex queries trying to figure out why. Sound familiar? You can use real user monitoring (RUM) to determine what has happened, looking at page load times, error counts, user sessions, etc.

Wilfried Roset is an engineering manager who leads an SRE team and he is a Grafana Champion. Wilfried currently works at OVHcloud where he focuses on prioritizing sustainability, resilience, and industrialization to guarantee customers satisfaction. Whether it’s for efficient resource allocation, flexibility, high availability, or scalability, it makes a lot of sense to run Grafana Mimir on Kubernetes—but it’s not the only way to deploy Mimir.

As part of our big tent philosophy at Grafana Labs, we think you should be able to dig into your data and find meaningful insights — wherever that data happens to live. For many of our users, that data lives in Salesforce, the cloud-based customer relationship management (CRM) platform. In this post, we’ll take a closer look at how you can use the Salesforce Enterprise data source for Grafana to quickly and easily visualize your Salesforce data using Grafana dashboards.

The more your infrastructure grows, the more likely you are to face a familiar challenge: where to safely store the API keys, passwords, and tokens that power your observability stack. Unfortunately, a common response to this dilemma is to scatter credentials across configurations, making security and management of secrets increasingly complex.