monte-carlo-storage-cost-analysis

Data warehouse storage cost analysis identifies stale and unused tables by classifying waste patterns, assigning safety tiers, and generating formatted cleanup recommendations. This process surfaces wasteful data assets and guides safe removal opportunities.

Yes, storage waste analysis works with Snowflake, BigQuery, Redshift, and Databricks. It supports these common warehouse platforms to fetch waste candidates, categorize stale tables, and guide cost optimization cleanup across your data infrastructure.

You verify if a stale table is safe to delete by performing downstream lineage checks to validate safe removals. The analysis will not suggest deleting tables with downstream consumers without verification, ensuring downstream pipelines remain intact.

Categories of storage waste to look for when optimizing data warehouse costs include temporary, archive, and snapshot tables. Analyzing these categories helps classify waste patterns and assign safety tiers to prioritize safe cleanup opportunities.

Yes, you need access to the Monte Carlo MCP server to analyze data warehouse storage costs. The analysis delegates classification to the analyze_storage_costs MCP pipeline to fetch candidates, categorize waste, and generate presentation-ready output.