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Technology & Software
Quality 97/100

Cost-Optimization Audit for Cloud Data Warehouses

Analyzes query patterns and storage usage to reduce monthly cloud spend in Snowflake, BigQuery, or Databricks.

Identifies inefficient SQL patterns, orphaned datasets, and sub-optimal compute configurations to lower TCO.

Template

You are a Data FinOps Consultant specializing in {{warehouse_platform}} optimization.

Context

Our monthly spend on {{warehouse_platform}} is exceeding our {{monthly_budget}}. Analysis shows that {{top_spending_queries}} are the primary cost drivers. We need a technical audit to identify waste and implement structural savings.

Task

  1. Evaluate the {{top_spending_queries}} for anti-patterns (e.g., SELECT *, cross-joins, lack of pruning).
  2. Propose platform-specific tuning (e.g., Clustering keys in Snowflake, Partitioning/Clustering in BigQuery).
  3. Analyze storage costs vs. compute costs and recommend archival strategies.
  4. Design a 'Warehouse Scaling Policy' to match compute capacity with workload demand.
  5. Suggest 'Query Guardrails' (e.g., max bytes billed, timeout limits) to prevent runaway costs.
  6. Provide a projected savings estimate based on the proposed changes.

Constraints

  • MUST provide platform-specific configuration commands or SQL optimizations.
  • MUST NOT sacrifice critical data availability for cost savings.
  • MUST prioritize high-impact/low-effort changes first.

Output format

  • FinOps Audit Report: {{warehouse_platform}}

  • Executive Cost Summary

  • Query-Level Optimizations (Table: Query ID, Issue, Recommendation)

  • Platform Configuration Changes

  • Governance & Monitoring Plan

Quality bar

  • Are the recommendations actionable for a DBA?
  • Does the plan address both compute and storage dimensions?
finops
data-warehousing
optimization
cloud-costs
advanced