Infrastructure observability vendor Dynatrace Inc. today is enhancing the Grail data analytics platform to support metrics, distributed traces and multicloud topology and dependencies in addition to the log and business event support that was included in the product’s launch in October.

The company vendor said the additions expand Grail’s ability to store, process and analyze an enormous volume and variety of data from cloud ecosystems in context and without the need for structuring or hydration.

The company is also extending its core platform with the addition of AutomationEngine. That’s a technology that enables users to operate clouds more efficiently by combining a simple interface with a no-code/low-code toolset that leverages causal artificial intelligence, a type of AI that can explain cause and effect. It can be used tasks such as automated remediation and progressive delivery against service level objectives, automated routing of vulnerabilities discovered by Dynatrace Application Security, and forecasting of future cloud infrastructure and compute resource requirements.

Finally, the company has revamped the user interface in its Software Intelligence Platform to incorporate dashboarding capabilities and support for improved collaboration between development and business teams. It’s also adding Dynatrace Notebooks, which are a new interactive document capability that allows information technology, development, security and business users to collaborate using code, text and rich media.

Beyond human scale

“The overarching theme is that cloud complexity is now beyond the human scale to manage; traditional monitoring tools no longer work,” said Bob Wambach, vice president of product marketing at Dynatrace. “A convergence of observability, visibility and business data is needed.”

Grail is a schema-less “lakehouse,” a type of database that combines data warehouse-like performance and scalability with support for a wide variety of data types. It features dependency graphs and the ability to understand dependency maps so that “when you run a query, you get all of that extra context for answers that go way beyond correlation,” Wambach said. The software was built to filter out the false positives and noise that occur when dependencies aren’t known, he said.

The analytics platform can be used to conduct application security forensics to prevent data breaches, detect service outages that may have left e-commerce customers in the lurch and improve multicloud efficiency by predicting cloud performance and utilization over time.

It uses Davis, a deterministic artificial intelligence engine that was built for dependency detection, topology visualization, anomaly detection, root cause analysis and business impact analysis.

“Grail is the data store and Davis is the AI platform that queries and does analytics on Grail,” Wambach said. “We’ve built dashboards and notebooks, but it’s also extensible to let partners build their own custom notebooks on top of that.”

Real-time prediction

The software uses machine learning to analyze historical information and predict future events. “In constantly changing environments like Kubernetes you need something that can work in real time, so the foundation of what we do is to be able to see something we’ve never seen before and act on it based upon all of the involved elements and relationships,” Wambach said.

AutomationEngine is intended to let business users build customizable dashboards out of reusable blocks. “Anything that’s of concern about underlying resources can be root cause analyzed,” Wambach said.

The new Dynatrace user interface, notebook capabilities, Automation Engine and Grail support for metrics and the Smartscape application mapping visualization dashboard will be available within 90 days.

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