view: dt_podcasting_cumulative_downloads {
  sql_table_name: INTELLIGENCE.DBT_PROD.DT_PODCASTING_CUMULATIVE_DOWNLOADS ;;
#   derived_table: {
#     sql: Select episode_id, report_date, to_date(episode_published_date) as episode_published_date,
# IFF(SIGN(DATEDIFF('day', episode_published_date, report_date)) = -1,
#       1, (DATEDIFF('day', episode_published_date, report_date) + 1)) as days_since_release,
# count(distinct(metric_id)) as downloads,
# sum(downloads) OVER (PARTITION BY episode_id order by report_date) as cumulative_downloads
# from facts.prod.staging_raw_megaphone
# where seconds_downloaded > 60
# group by 1,2,3,4
# order by 1,2 asc;;
#   }

  dimension: episode_id {
    type: number
    sql: ${TABLE}.episode_id;;
    hidden: yes
  }

  dimension: report_date {
    type: date
    sql: ${TABLE}.report_date;;
    hidden: yes
  }

  dimension: episode_published_date {
    type: date
    sql: ${TABLE}.episode_published_date;;
    hidden: yes
  }

  dimension: days_since_release {
    label: "Episode Days Since Release"
    type: number
    sql: ${TABLE}.days_since_release;;
    view_label: "Episode"
  }

  measure: downloads {
    type: number
    sql: ${TABLE}.downloads ;;
    hidden: yes
  }

  measure: cumulative_downloads {
    type: max
    sql: ${TABLE}.cumulative_downloads ;;
    label: "Episode Cumulative Downloads"
    view_label: "Episode"
  }

}