warehouse-optimization

What problem does it solve?

This skill helps analytics teams reduce query latency and cloud spend by applying targeted clustering, partitioning, materialization strategies, and query tuning to data warehouses.

Core Features & Use Cases

• Diagnostics-first optimization: profile expensive queries using baseline metrics and identify bottlenecks.
• Cross-platform optimization guidance: clustering, partitioning, and warehouse sizing tailored to Snowflake, BigQuery, Redshift, and Databricks.
• Workflow guidance: provide checks and steps to baseline, validate improvements with tooling, and update dbt configurations.
• Use cases: optimize production workloads, tune large dashboards, and cut run costs on scheduled jobs.

Quick Start

Run the baseline tooling to profile expensive queries and then apply clustering, partitioning, materialization, and sizing changes to optimize performance and reduce costs.