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Dynatrace Announces Early Access for Joint Google Cloud Customers

Dynatrace announced early access for joint Google Cloud customers to its most recent platform innovations. 

These innovations are powered by the Dynatrace Grail™ data lakehouse that retains context across all data types – including logs, metrics, traces, events, and more – to provide customers with precise, actionable answers.

Dynatrace Grail enables organizations to extract real-time, actionable intelligence from their data. It brings together observability, security, and business data, allowing businesses to swiftly derive insights, boost operational performance and stay ahead in Google Cloud environments.

With real-time data processing and advanced automation, Grail also helps organizations improve efficiencies to make smarter, data-driven decisions that directly contribute to business growth and competitive advantage. Other key benefits include:

  • AI-Driven Precision: The combination of Davis® AI and Grail enables accurate, real-time insights for informed decision-making and faster issue resolution.
  • Scalable Data Processing: Grail’s cloud-native architecture delivers the access and speed of hot storage for all data with the cost efficiency of cold-tier storage. It eliminates the time-consuming and costly re-indexing and rehydration operations that are inherent to competitive observability solutions.
  • Seamless Integration with Google Cloud: Organizations can unify and analyze data within their existing cloud ecosystems, enhancing performance and security.
  • Easy Access to Dynatrace Data for Developers: With Google’s Gemini Code Assist, an AI-powered coding assistant that helps developers with various tasks – including code generation, completion, and debugging, directly within their IDEs – developers can access critical Dynatrace data, including data related to potential issues, without disrupting their flow state. This enables faster issue resolution and continuous innovation.
  • Observability for End-to-End Multimodal AI Models: Users can track and monitor the consumption, cost, and performance of AI services and models provided by Google’s Gemini models – a family of multimodal AI models designed to understand and generate text, images, audio, videos, and code – at scale.

“AI-powered observability has the power to transform how organizations manage their cloud-native environments,” said Ritika Suri, Managing Director of AI & Data ISV Partnerships at Google Cloud. “Solutions like Dynatrace Grail, integrated with Google Cloud's leading AI and infrastructure, enable customers to streamline operations, enhance efficiency, and confidently drive innovation.”

“Grail has been a game changer for Dynatrace, setting us apart by delivering real-time insights at an unprecedented scale,” said Jay Snyder, SVP of Partners and Alliances, Dynatrace. “By combining the capabilities of Grail with the flexibility and power of Google Cloud, Dynatrace empowers customers to innovate faster, operate more efficiently, and achieve their digital transformation goals with confidence.”

The early access program presents an opportunity for Google Cloud customers to adopt next-generation observability technology, positioning them at the forefront of cloud-native transformation. Through this integration, enterprises can modernize operations, improve system reliability, and unlock new opportunities for growth and efficiency.

Dynatrace expects the Grail data lakehouse on Google Cloud to be generally available by June 30. 

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Dynatrace Announces Early Access for Joint Google Cloud Customers

Dynatrace announced early access for joint Google Cloud customers to its most recent platform innovations. 

These innovations are powered by the Dynatrace Grail™ data lakehouse that retains context across all data types – including logs, metrics, traces, events, and more – to provide customers with precise, actionable answers.

Dynatrace Grail enables organizations to extract real-time, actionable intelligence from their data. It brings together observability, security, and business data, allowing businesses to swiftly derive insights, boost operational performance and stay ahead in Google Cloud environments.

With real-time data processing and advanced automation, Grail also helps organizations improve efficiencies to make smarter, data-driven decisions that directly contribute to business growth and competitive advantage. Other key benefits include:

  • AI-Driven Precision: The combination of Davis® AI and Grail enables accurate, real-time insights for informed decision-making and faster issue resolution.
  • Scalable Data Processing: Grail’s cloud-native architecture delivers the access and speed of hot storage for all data with the cost efficiency of cold-tier storage. It eliminates the time-consuming and costly re-indexing and rehydration operations that are inherent to competitive observability solutions.
  • Seamless Integration with Google Cloud: Organizations can unify and analyze data within their existing cloud ecosystems, enhancing performance and security.
  • Easy Access to Dynatrace Data for Developers: With Google’s Gemini Code Assist, an AI-powered coding assistant that helps developers with various tasks – including code generation, completion, and debugging, directly within their IDEs – developers can access critical Dynatrace data, including data related to potential issues, without disrupting their flow state. This enables faster issue resolution and continuous innovation.
  • Observability for End-to-End Multimodal AI Models: Users can track and monitor the consumption, cost, and performance of AI services and models provided by Google’s Gemini models – a family of multimodal AI models designed to understand and generate text, images, audio, videos, and code – at scale.

“AI-powered observability has the power to transform how organizations manage their cloud-native environments,” said Ritika Suri, Managing Director of AI & Data ISV Partnerships at Google Cloud. “Solutions like Dynatrace Grail, integrated with Google Cloud's leading AI and infrastructure, enable customers to streamline operations, enhance efficiency, and confidently drive innovation.”

“Grail has been a game changer for Dynatrace, setting us apart by delivering real-time insights at an unprecedented scale,” said Jay Snyder, SVP of Partners and Alliances, Dynatrace. “By combining the capabilities of Grail with the flexibility and power of Google Cloud, Dynatrace empowers customers to innovate faster, operate more efficiently, and achieve their digital transformation goals with confidence.”

The early access program presents an opportunity for Google Cloud customers to adopt next-generation observability technology, positioning them at the forefront of cloud-native transformation. Through this integration, enterprises can modernize operations, improve system reliability, and unlock new opportunities for growth and efficiency.

Dynatrace expects the Grail data lakehouse on Google Cloud to be generally available by June 30. 

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Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...