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Grafana Labs Releases Grafana 12

Grafana Labs released Grafana 12, the latest version of the company's flagship open source data visualization platform, introducing a comprehensive approach to observability as code that enables a more consistent and stable experience.

"We reimagined what it means to provision dashboards and how APIs and schema are structured," said Torkel Ödegaard, Co-Founder, Grafana Labs. "These are fundamental changes that have become the basis for a range of improvements we've made to how users can interact with Grafana through code. With Grafana 12, we focused on providing everything users need to more easily and efficiently create and manage dashboards."

Key features of Grafana 12 include:

  • App Platform: The backbone of the observability as code strategy, providing consistent, versioned APIs for managing Grafana resources like dashboards, plus a set of tools for building custom applications on top of Grafana.
  • New Dashboard Schema: A new JSON structure that decouples general settings from content, enhancing readability when rendered as code and making it easier to generate dashboards.
  • Dynamic Dashboards: Powered by the new dashboard schema, this feature allows for more flexible dashboard creation with improved customization options.
  • Git Sync: Users can automatically synchronize Grafana dashboards to a GitHub repository and review changes using pull requests, for higher-quality and more portable dashboards.
  • New As Code Tools: A set of new products that can be integrated into pipelines or GitHub Actions, supporting customers who already have observability as code setups in place. These include improvements to the Terraform provider and a new CLI tool, GrafanaCTL.
  • 15 New Data Sources: Explore product analytics data, DB data, and developer tools with new data sources like DynamoDB, CosmosDB, Cloudflare, Atlassian Statuspage, and Pagerduty.
  • SQL Expressions: Use SQL to combine and transform data from different sources and merge disparate data sets in real time.
     

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Grafana Labs Releases Grafana 12

Grafana Labs released Grafana 12, the latest version of the company's flagship open source data visualization platform, introducing a comprehensive approach to observability as code that enables a more consistent and stable experience.

"We reimagined what it means to provision dashboards and how APIs and schema are structured," said Torkel Ödegaard, Co-Founder, Grafana Labs. "These are fundamental changes that have become the basis for a range of improvements we've made to how users can interact with Grafana through code. With Grafana 12, we focused on providing everything users need to more easily and efficiently create and manage dashboards."

Key features of Grafana 12 include:

  • App Platform: The backbone of the observability as code strategy, providing consistent, versioned APIs for managing Grafana resources like dashboards, plus a set of tools for building custom applications on top of Grafana.
  • New Dashboard Schema: A new JSON structure that decouples general settings from content, enhancing readability when rendered as code and making it easier to generate dashboards.
  • Dynamic Dashboards: Powered by the new dashboard schema, this feature allows for more flexible dashboard creation with improved customization options.
  • Git Sync: Users can automatically synchronize Grafana dashboards to a GitHub repository and review changes using pull requests, for higher-quality and more portable dashboards.
  • New As Code Tools: A set of new products that can be integrated into pipelines or GitHub Actions, supporting customers who already have observability as code setups in place. These include improvements to the Terraform provider and a new CLI tool, GrafanaCTL.
  • 15 New Data Sources: Explore product analytics data, DB data, and developer tools with new data sources like DynamoDB, CosmosDB, Cloudflare, Atlassian Statuspage, and Pagerduty.
  • SQL Expressions: Use SQL to combine and transform data from different sources and merge disparate data sets in real time.
     

The Latest

Payment system failures are putting $44.4 billion in US retail and hospitality sales at risk each year, underscoring how quickly disruption can derail day-to-day trading, according to research conducted by Dynatrace ... The findings show that payment failures are no longer isolated incidents, but part of a recurring operational challenge that disrupts service, damages customer trust, and negatively impacts revenue ...

For years, the success of DevOps has been measured by how much manual work teams can automate ... I believe that in 2026, the definition of DevOps success is going to expand significantly. The era of automation is giving way to the era of intelligent delivery, in which AI doesn't just accelerate pipelines, it understands them. With open observability connecting signals end-to-end across those tools, teams can build closed-loop systems that don't just move faster, but learn, adapt, and take action autonomously with confidence ...

The conversation around AI in the enterprise has officially shifted from "if" to "how fast." But according to the State of Network Operations 2026 report from Broadcom, most organizations are unknowingly building their AI strategies on sand. The data is clear: CIOs and network teams are putting the cart before the horse. AI cannot improve what the network cannot see, predict issues without historical context, automate processes that aren't standardized, or recommend fixes when the underlying telemetry is incomplete. If AI is the brain, then network observability is the nervous system that makes intelligent action possible ...

SolarWinds data shows that one in three DBAs are contemplating leaving their positions — a striking indicator of workforce pressure in this role. This is likely due to the technical and interpersonal frustrations plaguing today's DBAs. Hybrid IT environments provide widespread organizational benefits but also present growing complexity. Simultaneously, AI presents a paradox of benefits and pain points ...

Over the last year, we've seen enterprises stop treating AI as “special projects.” It is no longer confined to pilots or side experiments. AI is now embedded in production, shaping decisions, powering new business models, and changing how employees and customers experience work every day. So, the debate of "should we adopt AI" is settled. The real question is how quickly and how deeply it can be applied ...

In MEAN TIME TO INSIGHT Episode 20, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA presents his 2026 NetOps predictions ... 

Today, technology buyers don't suffer from a lack of information but an abundance of it. They need a trusted partner to help them navigate this information environment ...

My latest title for O'Reilly, The Rise of Logical Data Management, was an eye-opener for me. I'd never heard of "logical data management," even though it's been around for several years, but it makes some extraordinary promises, like the ability to manage data without having to first move it into a consolidated repository, which changes everything. Now, with the demands of AI and other modern use cases, logical data management is on the rise, so it's "new" to many. Here, I'd like to introduce you to it and explain how it works ...

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