sql-queries

To translate SQL queries between dialects like PostgreSQL and Snowflake, syntax adjustments for dates, strings, and semi-structured data are required. This Skill generates dialect-aware SQL, preserving query intent while applying idiomatic patterns and correct syntax for each warehouse.

The best way to optimize slow BigQuery queries is by applying partitioning and clustering to filter data efficiently. This Skill provides performance guidance, recommending specific functions and partitioning strategies to reduce costs and avoid full-table scans.

Writing SQL window functions for cohort retention involves creating CTE-driven pipelines to track user activity over time. This Skill provides ready-to-use analytics templates for cohort retention, funnels, deduplication, and complex window functions across major data warehouses.

Yes, this Skill supports writing queries for both Databricks and Redshift. It targets these specific data warehouses along with Snowflake, BigQuery, and PostgreSQL, generating correct and efficient dialect-aware syntax for each platform.

Query performance on large datasets often degrades due to missing partitioning or inefficient functions. This Skill helps diagnose issues by recommending performance best practices and providing debugging guidance using EXPLAIN or ANALYZE output to optimize slow queries.