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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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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

For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...

Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...

While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...

A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...