June 2026 Summaries

Investigating issues across modern applications can be challenging due to the complexity of connecting scattered information across services and infrastructure layers. Grafana Cloud offers a solution by providing full-stack observability that integrates application, infrastructure, and Kubernetes monitoring into a single platform. This unified view is powered by a knowledge graph that maps telemetry data to various entities like services, pods, nodes, and databases, enabling users to visualize relationships and streamline the process of identifying root causes. Grafana's features, such as the RCA workbench and entity graph, facilitate efficient incident investigation by consolidating insights, dependencies, and telemetry in one place, allowing users to jump directly into logs and traces to pinpoint issues. The platform allows customization of knowledge graph configurations to suit different environments, providing flexibility in handling diverse labeling strategies and telemetry setups. Additionally, users can manage configurations via Grafana's Terraform provider, ensuring that observability data is correlated effectively to speed up root cause analysis and maintain system reliability.

Grafana 13.1 builds upon the groundwork of Grafana 13 by enhancing observability as code, expanding Grafana Assistant's capabilities to more data sources like Snowflake, Oracle, and Jira, and refining the user experience for dashboard management and visualization. The release introduces improvements such as Git Sync enhancements for managing dashboards as code, section-level variables for better organization, and a revamped query editor for streamlined query management. Additionally, Grafana 13.1 supports Private Data Source Connect for new sources like MQTT and IBM Db2, facilitating secure connections between Grafana Cloud and private networks. These updates aim to provide a faster, more flexible, and integrated approach to data visualization and analysis while encouraging community engagement and innovation.

In response to a supply chain ransom incident involving TanStack, Grafana Labs conducted an extensive investigation and remediation effort, confirming that there was no unauthorized access to customer production systems or alteration of their codebase. The incident, which began on May 11 with the Mini Shai-Hulud campaign, involved the cloning of repositories after an overlooked credential allowed a bad actor access. Despite a ransom demand, Grafana Labs, adhering to its principles and FBI guidance, opted not to pay. The company engaged cybersecurity firm Mandiant to conduct an independent audit, which corroborated Grafana's findings of no code tampering or compromised customer systems. Following the incident, Grafana Labs implemented a robust security strategy, including global code freezes, comprehensive audits, and enhanced security operations, while maintaining transparency with their community. The company continues to improve its security posture by employing token brokers, fine-grained access controls, and more restrictive GitHub actions, committing to ongoing efforts to strengthen their security infrastructure.

ObservabilityCON 2026 is set to take place in San Francisco at Pier 27 from October 19–21, bringing together the observability community to explore advancements in AI and observability technologies. The event will feature an opening keynote on emerging trends, live demos of Grafana Cloud's latest tools, and workshops on AI observability and digital experience monitoring. Attendees will have the chance to network, engage in one-on-one sessions with engineers, and participate in hands-on technical deep dives. Registration for discounted tickets is available through early access sign-ups, and the event also offers speaking and sponsorship opportunities for those wishing to share their observability success stories. For those unable to attend in San Francisco, ObservabilityCON will also tour cities including São Paulo, London, Madrid, and Bengaluru, where participants can deepen their expertise through AI demos and expert interactions.

Grafana Assistant Investigations is an AI-powered feature within Grafana Cloud designed to automatically identify and address the root causes of incidents, providing enhanced support for incident response and auto-remediation workflows. This upgraded tool, currently in public preview, allows users to explore various aspects of the observability space and developer lifecycle by examining metrics, logs, traces, and more to find and validate problems. Users can define degradation criteria, schedule AI-assisted investigations, and receive daily reports to ensure operational parameters are maintained. The system supports full customization, integrating with various tools and platforms like GitHub, GitLab, Snowflake, and Salesforce, allowing users to tailor investigations to their specific needs. Grafana Assistant's collaborative features enable multiple users to manage incidents through a shared workspace, while seamless integration with Grafana Alerting and Cloud IRM helps initiate investigations in response to alerts or incidents. Additionally, the tool's automation capabilities allow for structured investigations and proactive issue detection before users notice problems, making it a valuable asset in enhancing observability practices and maintaining system reliability.

The article outlines a method for generating realistic load tests using Grafana Cloud k6 by leveraging production telemetry data. Traditional load tests often rely on assumptions, such as arbitrary virtual user counts or latency thresholds, which can result in inaccurate representations of real-world user interactions. By utilizing data from Grafana Cloud dashboards, teams can create load tests that reflect actual user behavior, request rates, latency distributions, and traffic patterns. This approach ensures more reliable baselines and thresholds, reducing surprises in production environments. The article describes how to derive critical metrics like request rates and latency baselines from production data and incorporate them into k6 testing scenarios, including different traffic modeling patterns such as constant load and stress tests. Additionally, it discusses the benefits of Grafana Assistant, an AI-powered tool that automates the creation of k6 scripts based on real-world metrics, enhancing testing accuracy and efficiency. This strategy aligns with shift-left testing principles, ensuring that pre-production tests accurately simulate production pressures to catch issues early in the development pipeline.

Tempo 3.0 introduces a significant architectural overhaul aimed at enhancing scalability and reducing the total cost of ownership for distributed tracing, alongside the general availability of TraceQL metrics for deriving insights directly from trace data. The new Kafka-compatible architecture for microservices deployments allows Tempo to efficiently handle trace ingestion, storage, and querying by decoupling the read and write paths, which improves performance and reliability. Key improvements include enhanced query performance, trace redaction capabilities, and the ability to run TraceQL metrics without relying on the metrics-generator component. Additionally, the release introduces a per-label cardinality limiter and a DRAIN-based span name sanitizer to manage cardinality and ensure system stability, as well as flexible filtering options and sampling compensation to improve metric accuracy. Users are advised to refer to the migration guide for transitioning from previous versions due to these extensive architectural changes.