Cloud Canaries Announces Snowflake Support for Cloud Intelligence

CAMBRIDGE, MASSACHUSETTS, 2 April 2024 – Cloud Canaries, a leading cloud intelligence company, today announced support for the cloud data platform Snowflake. With the latest version of its Aviary Platform, Cloud Canaries customers can store critical workload data directly in Snowflake and use Snowflake Cortex for forecasting enabling teams to analyze and proactively prevent harmful events.

Cloud intelligence is quickly surpassing traditional cloud monitoring and observability approaches, enabling faster, more informed decision-making. Cloud Canaries simulate workloads and analyze the behavior of systems, applications, and networks to detect anomalies and potential threats. With Cloud Canaries, businesses can quickly detect, uncover, and recover from critical events, including hardware failures, software bugs, vulnerabilities, brand loyalty fluctuations, and supply chain or distribution network challenges. 

The combination of cloud workload data and AI guides automated canaries to investigate, identify, mitigate, and prevent the events now and in the future through the platform itself or connected external solutions. The Aviary Platform is the central hub where users can manage their Cloud Canaries and associated artificial intelligence models.

Snowflake with Cloud Canaries offers seamless access to data for machine learning and analysis while enhancing data security and availability. The latest version includes forecast capabilities using Snowflake Cortex, as the integration of Cortex forecasts with Cloud Canaries’ Aviary Platform includes alarms and notifications within the platform’s health, compliance, and forecast dashboards.

“The addition of Snowflake support advances our cloud intelligence platform by merging AI and data from diverse cloud workloads to enable predictive incident management,” said Mark Callahan, CEO and Founder of Cloud Canaries. We now offer a better approach for teams to monitor and address performance and customer experience issues than traditional monitoring and observability platforms—and at a fraction of the cost.”

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