Operations | Monitoring | ITSM | DevOps | Cloud

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.

We consistently roll out helpful updates and fun features in Grafana Cloud, our fully managed observability platform powered by the open source Grafana LGTM Stack (Loki for logs, Grafana for visualization, Tempo for traces, and Mimir for metrics). In case you missed them, here’s our monthly round-up of the latest and greatest Grafana Cloud updates. You can also read about all the features we add to Grafana Cloud in our What’s New in Grafana Cloud documentation.

Generative AI is becoming a core part of modern applications, making it essential to monitor and manage how these services are used. That’s why, today, we’re excited to introduce the Anthropic integration for Grafana Cloud, a new solution that lets you connect directly to the Anthropic Usage and Cost API from within Grafana Cloud.

Andy Jiang is a JavaScript engineer with nearly 10 years of experience. He’s interested in making JavaScript and TypeScript simpler to use. He currently works at Deno as a product marketing manager. Outside of work, Andy likes cooking, writing, and playing tennis. Observability is essential for modern applications. Metrics, logs, and traces allow you to troubleshoot production issues, monitor performance, and understand usage patterns.

Did you know that observability has been around for more than six decades? It all goes back to a Hungarian-American inventor named Rudolf Kálmán who thought about how external outputs could measure the internal state of a machine. Kálmán wrote about monitoring single-input single-output systems, but our demands are very different today. We need to observe monoliths, microservices, clusters, pods, regions, and many more.

Grafana puts a powerful set of observability capabilities right at your fingertips, but onboarding entire teams to the sophisticated platform is often a nontrivial exercise—one that can slow adoption and prevent organizations from getting immediate value. We want to make the process as frictionless as possible, which is why we’re excited to tell you that Grafana Assistant is now available in public preview to all Grafana Cloud users.

Distributed tracing data is a unique and powerful observability signal, allowing you to understand how your services interact and the relationships between them. Sometimes it can be difficult, however, to turn raw tracing data into actionable insights. This is exactly why we introduced Grafana Traces Drilldown, an application that lets you quickly investigate and visualize your tracing data through a simplified, queryless experience.

Organizations in Brazil are eager to adopt some of the latest observability trends and technologies as they look to keep their software running as smoothly as possible, according to analysis of a micro survey recently conducted by Grafana Labs. Observability is an evolving space, and this is the first time Grafana Labs has run a Brazilian version of our annual Observability Survey.

A trace always remembers! Okay, okay. I will try to keep my Game of Thrones references to a minimum throughout this post, but there is a lot of truth to that statement. In observability, a trace is the “when” and “where” of telemetry signals, allowing us to track the state of interactions between services within a microservice architecture. This makes traces the ideal observability signal for discovering bottlenecks and interconnection issues.