```json { "action": "tool_call", "reasoning": "To check for duplicate or near-duplicate rows, I need to understand the grain of the dataset first. The card indicates the grain is 'one row per IPL cricket match played; key = season, match_number, date'. I'll query for exact duplicates based on these key columns, and also check if there are any rows with identical values across all columns.", "tool": "query_sql", "args": { "sql": "WITH key_duplicates AS (SELECT season, match_number, date, COUNT(*) as duplicate_count FROM raw.IPL_Matches_Data_2008_2026 GROUP BY season, match_number, date HAVING COUNT(*) > 1), full_row_check AS (SELECT COUNT(*) as total_rows, COUNT(DISTINCT (season, match_number, date, team1, team2, winner, toss_winner, toss_decision, player_of_match, venue, city, result_type)) as distinct_key_rows FROM raw.IPL_Matches_Data_2008_2026) SELECT (SELECT COUNT(*) FROM key_duplicates) as key_duplicate_groups, (SELECT SUM(duplicate_count) FROM key_duplicates) as total_key_duplicates, (SELECT total_rows FROM full_row_check) as total_rows, (SELECT distinct_key_rows FROM full_row_check) as distinct_extended_key_rows", "purpose": "Check for duplicate rows based on the natural key (season, match_number, date) and also check distinctness across a broader set of identifying columns", "source": "raw" } } ```