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Quest Integrates with Snowflake

Quest Software announced the integration of its governance and cost optimization capabilities with Snowflake AI Data Cloud. 

This enables organizations to accelerate insight delivery and monitor cloud spend—all from a single console.

“Snowflake delivers performance, and Quest supports Snowflake’s data-driven innovations,” said Bharath Vasudevan, Vice-President of Product Management at Quest. “We give data teams the visibility and confidence they need to trust their data, certify their models, and keep budgets in check—so AI projects can launch in days, not months.”

Quest empowers customers to transform raw cloud data into trusted, compliant, and cost-aware assets. Key capabilities now available to joint customers include:

  • Foglight for Cloud Cost Optimization: Within our database observability tool, we surface warehouse utilization and analyze credit consumption across warehouses, users, and queries down to the hour, which can surface idle or misconfigured resources and anomalous workloads.  By leveraging Snowflake’s Organization Usage Schema, Foglight delivers actionable insights that have demonstrated 15–30% cost savings in pilot deployments.
  • erwin 15 AI-Ready Governance Enhancements: Within our data intelligence suite, we offer native support for Snowflake environments—certifying AI models built on Snowflake data, scoring data trustworthiness using Snowflake-native metrics, and seamlessly integrating governance metadata. This includes cataloging Snowflake datasets, capturing lineage across Snowflake objects, linking business glossary terms to Snowflake tables and views, and assessing data quality in real-time. Trusted and explainable data is the foundation of trusted AI.

“With these integrations, Quest is helping joint users get trusted data faster and optimize spend—a critical step for successful AI initiatives,” said Kieran Kennedy, VP, Data Cloud Product Partners, Snowflake.

Joint customers can now trace lineage end-to-end, auto-document impacts, enforce cross-platform policies, and monitor usage spikes. By combining trusted data governance with real-time cost insights, Quest helps teams move from raw data to AI-ready outcomes—faster and with more control.

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The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

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Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...

Quest Integrates with Snowflake

Quest Software announced the integration of its governance and cost optimization capabilities with Snowflake AI Data Cloud. 

This enables organizations to accelerate insight delivery and monitor cloud spend—all from a single console.

“Snowflake delivers performance, and Quest supports Snowflake’s data-driven innovations,” said Bharath Vasudevan, Vice-President of Product Management at Quest. “We give data teams the visibility and confidence they need to trust their data, certify their models, and keep budgets in check—so AI projects can launch in days, not months.”

Quest empowers customers to transform raw cloud data into trusted, compliant, and cost-aware assets. Key capabilities now available to joint customers include:

  • Foglight for Cloud Cost Optimization: Within our database observability tool, we surface warehouse utilization and analyze credit consumption across warehouses, users, and queries down to the hour, which can surface idle or misconfigured resources and anomalous workloads.  By leveraging Snowflake’s Organization Usage Schema, Foglight delivers actionable insights that have demonstrated 15–30% cost savings in pilot deployments.
  • erwin 15 AI-Ready Governance Enhancements: Within our data intelligence suite, we offer native support for Snowflake environments—certifying AI models built on Snowflake data, scoring data trustworthiness using Snowflake-native metrics, and seamlessly integrating governance metadata. This includes cataloging Snowflake datasets, capturing lineage across Snowflake objects, linking business glossary terms to Snowflake tables and views, and assessing data quality in real-time. Trusted and explainable data is the foundation of trusted AI.

“With these integrations, Quest is helping joint users get trusted data faster and optimize spend—a critical step for successful AI initiatives,” said Kieran Kennedy, VP, Data Cloud Product Partners, Snowflake.

Joint customers can now trace lineage end-to-end, auto-document impacts, enforce cross-platform policies, and monitor usage spikes. By combining trusted data governance with real-time cost insights, Quest helps teams move from raw data to AI-ready outcomes—faster and with more control.

The Latest

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...