data-explorationlisted

# Data Exploration — DBX Studio ## Exploration Workflow ### Phase 1: Schema Discovery Start with `read_schema` to list all tables, then `describe_table` for each table of interest. ``` 1. read_schema(schema_name: "public") 2. describe_table(table_name: "<each table>") 3. get_table_stats(table_name: "<table>") ``` ### Phase 2: Table Profiling For each table, gather: - Row count - Column names and types - Sample data via `get_table_data` - Null counts and distributions ### Phase 3: Relationship Discovery Look for foreign key patterns: - Columns named `*_id` linking to other tables - Common join patterns: `users.id → orders.user_id` ## Quality Scoring | Score | Completeness | |-------|-------------| | Green | > 95% populated | | Yellow | 80–95% populated | | Orange | 50–80% populated | | Red | < 50% populated | ## Common Exploration Queries ### Row count ```sql SELECT COUNT(*) AS row_count FROM "public"."table_name"; ``` ### Column null rates ```sql SELECT COUNT(*) AS total, COUNT(column_name) AS non_null, ROUND(100.0 * COUNT(column_name) / COUNT(*), 2) AS pct_filled FROM "public"."table_name"; ``` ### Distinct values ```sql SELECT column_name, COUNT(*) AS frequency FROM "public"."table_name" GROUP BY 1 ORDER BY 2 DESC LIMIT 20; ``` ### Date range ```sql SELECT MIN(created_at), MAX(created_at) FROM "public"."table_name"; ``` ## Output Format After exploration, present a structured summary: - **Tables**: list with row counts - **Key relationships**: how tables