FinOps, DataOps and Observability for Snowflake in minutes

Gain complete FinOps, DataOps and Observability with actionable insights across spend, usage, data quality, and performance, supporting data teams in managing resources effectively, optimizing operational costs, and ensuring reliable data delivery.
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Key Benefits:

Real-Time Cost Management
  • Visibility into spend and resource utilization empowers teams to identify and quickly address spending inefficiencies
Enhanced Operational Efficiency
  • Actionable insights on warehouse utilization and query activity facilitate immediate efficiency improvements
Automated Data Quality
  • Real-time monitoring and identification of data quality incidents strengthens overall trust in organizational data, reducing risk

Instant 360-degree Observability

  • Tenant-Level Usage Analytics: Gain deep tenant usage, crucial for precise resource management with active users and Tables Data Size Management, Query Activity, Apps Usage & User Segmentation
  • Tenant Segmentation & Warehouse Usage: Segmented view of warehouse usage with warehouse Distribution, Active vs. Idle Warehouses and User Dynamics
  • Data & Infrastructure Health and Operations Management: Proactive data governance and reliability metrics with automatic Data Observability monitor identify Operational Performance and Failures to troubleshoot and reduce downtime
Observability
Performance and Optimization

Performance and Optimization Insights

  • Warehouse Performance Report: Automatically know annualized cost savings achieved through dynamic warehouse optimization strategies with query performance and detailed performance metrics
  • Usage and Efficiency Metrics: Use detailed insights into Snowflake resource usage, query distribution, and user-level credit consumption with warehouse efficiency, credit trends, and idle resources, enabling precise cost optimization and planning
  • Comprehensive Warehouse Management: Details sizing strategies, annual savings, and scaling policies, helping with strategic resource allocation.

Automatic Data Quality

  • Reduced Downtime: Quick identification and remediation of anomalies mitigate potential disruptions in analytics or operations and  preserves the accuracy and trustworthiness of data assets
  • Enhanced Operational Productivity: Automating monitoring frees data teams to focus on strategic projects rather than manual quality checks
  • End-to-End Visibility: Insight into both upstream causes and downstream impacts provides holistic data quality management
Automatic Data Quality

Questions & Answers

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