Describe a table and what it feeds. Get a prioritized list of data quality checks — uniqueness, nulls, ranges, referential integrity, freshness, distribution drift — as runnable SQL assertions plus dbt-test / Great Expectations equivalents, so bad data fails loud before it hits a dashboard.
Open it in Prompt Dock — free prompts run instantly; premium prompts are included in Explorer and Master.
Describe what you need to know in plain English and paste your schema. Get a correct, readable SQL query with CTEs, aliases, a plain-English explanation, and an index note — no guessing at column names.
Describe a user interaction you want to track. Get a complete, engineering-ready tracking plan: canonical event name, exact trigger, a property table, what NOT to track, and a QA checklist that includes the error case.
Paste your slow query and its EXPLAIN ANALYZE plan. Get a root-cause diagnosis, exact CREATE INDEX statements, a query rewrite if the structure is the problem, and a clear 'what NOT to do' warning.
Paste in a slow or failing PySpark/SQL data pipeline and get root-cause diagnosis, an optimized rewrite, and a plain-English explanation of every change. Built for data engineers working with Spark, Delta Lake, and cloud warehouses (Databricks, Snowflake, BigQuery).