view: dt_tiktok_song_tickets_by_week {
  sql_table_name: INTELLIGENCE.DBT_PROD.DT_TIKTOK_SONG_TICKETS_BY_WEEK ;;
#   derived_table: {
#     sql: WITH dt_tiktok_top_song_by_day AS (WITH TIKTOK_TOP_SONG_BY_DAY AS (SELECT
#   TO_CHAR(TO_DATE(D.DISPLAYDATE), 'YYYY-MM-DD') AS DOWNLOAD_DATE,
#   TIKTOK_TOP_SONG.ROW_ID AS ROW_ID
# FROM FACTS.PROD.STAGING_RAW_TIKTOK_TRENDS_TOPSONG AS TIKTOK_TOP_SONG
# LEFT JOIN FACTS.PROD.DIM_DAY D
# ON D.DISPLAYDATE = TIKTOK_TOP_SONG.DOWNLOAD_DATE
# ORDER BY 1)
# SELECT
# (TO_CHAR(TO_DATE(TIKTOK_TOP_SONG_BY_DAY.DOWNLOAD_DATE), 'YYYY-MM-DD')) AS filedate,
#     COUNT(TIKTOK_TOP_SONG_BY_DAY.ROW_ID) AS total_rows
# FROM TIKTOK_TOP_SONG_BY_DAY
# GROUP BY
#     filedate
# ORDER BY
#     1 DESC
# FETCH NEXT 500 ROWS ONLY)
# SELECT
#     (TO_CHAR(TO_DATE(dt_tiktok_top_song_by_day.filedate), 'YYYY-MM-DD')) AS filedate,
#     COALESCE(SUM(dt_tiktok_top_song_by_day.total_rows ), 0) AS totaltickets
# FROM dt_tiktok_top_song_by_day
# WHERE ((( dt_tiktok_top_song_by_day.filedate ) >= ((DATEADD('day', -29, CURRENT_DATE()))) AND ( dt_tiktok_top_song_by_day.filedate ) < ((DATEADD('day', 30, DATEADD('day', -29, CURRENT_DATE()))))))
# GROUP BY
#     (TO_DATE(dt_tiktok_top_song_by_day.filedate))
# ORDER BY
# 1 DESC ;;
#   }

  dimension: filedate {
    type: date
    sql: ${TABLE}.filedate ;;
  }

  measure: tickets {
    type:  sum
    sql: ${TABLE}.totaltickets ;;
  }

}