Grafana dashboards are powerful and flexible tools for observing applications and infrastructure, so it’s no surprise we get a lot of questions from the community about how to embed them into their web applications. Over the past few releases, we’ve developed a lot of options for how to do this in Grafana, but there can be confusion about how they work, and when to use each approach.
Grafana Agent v0.37 is here! This new release brings a lot of exciting new features and marks the pinnacle of a year-long effort to achieve feature parity between Grafana Agent Flow mode and Grafana Agent Static mode. We also extended our config converter to ease the migration from Static to Flow mode and we added the possibility to split your Flow configuration into multiple files. Please make note of some breaking changes in this release.
You may be ready to make the move to Grafana Cloud, but securely querying private data has been a blocker. If you wanted to query a network-secured data source like a MySQL database or an Elasticsearch cluster that is hosted in an on-premises private network or a Virtual Private Cloud (VPC), you needed to open your network to inbound queries from a range of IP addresses.
Using Grafana Cloud to manage and monitor even your most sensitive data from your AWS services just got easier. If your organization’s workloads are hosted in AWS and you are using a Grafana Cloud instance that’s also hosted in AWS, you can now use AWS PrivateLink to establish a secure connection between your virtual private cloud (VPC) network and Grafana Cloud for all your data.
Throughout the software development process, engineers can use a number of methods and tools to ensure their code is efficient. When using Go, for example, there are built-in tools, including those for benchmarking and CPU/memory profiling, to check how efficiently code will run. Engineers can also run unit tests to validate code quality.
With just 30 employees, Sentry Software might be considered a small company, but they’re prioritizing sustainability in a big way. As the makers of Hardware Sentry, an IT monitoring software, a large part of their business relies on maintaining optimal temperature conditions at their data centers — an operation that contributes to the company’s overall carbon footprint.
A family member’s birthday, that concert you’ve waited all year to see, an impromptu weekend getaway with friends — there are a lot of reasons software engineers might want to switch on-call shifts. And rather than have to frantically send Slack messages to your teammates, wouldn’t it be nice to automate the process and quickly find the coverage you need?
PromCon, the annual Prometheus community conference, is around the corner, and this year I’ll have exciting news to share from the Prometheus Java community: The highly anticipated 1.0.0 version of the Prometheus Java client library is here! At Grafana Labs, we’re big proponents of Prometheus. And as a maintainer of the Prometheus Java client library, I highly appreciate the support, as it helps us to drive innovation in the Prometheus community.
In OpenTelemetry metrics, there are two temporalities, Delta and Cumulative and the OpenTelemetry community has a good guide on the different trade-offs of each. However, the guide tackles the problem from the SDK end. It does not cover the complexity that arises from the collection pipeline. This post takes that into account and covers the architecture and considerations that are involved end-to-end for picking the temporality.
In order for fleet managers at Daimler Truck to manage the day-to-day operations of their vast connected vehicles service, they use tb.lx, a digital product studio that delivers near real-time data along with valuable insights for their networks of trucks and buses around the world. Each connected vehicle utilizes the cTP, an installed piece of technology that generates a small mountain of telemetry data, including speed, GPS position, acceleration values, braking force and more.
