Como criar tabelas dinâmicas derivadas de SQL com LookML e Liquid avaliações

Good.

Sharmila B. · Revisado há 20 days

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The viraj D. · Revisado há 20 days

yes

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Gopal J. · Revisado há 20 days

view: user_facts { derived_table: { sql: SELECT order_items.user_id AS user_id ,COUNT(distinct order_items.order_id) AS lifetime_order_count ,SUM(order_items.sale_price) AS lifetime_revenue ,MIN(order_items.created_at) AS first_order_date ,MAX(order_items.created_at) AS latest_order_date FROM cloud-training-demos.looker_ecomm.order_items WHERE {% condition select_date %} order_items.created_at {% endcondition %} GROUP BY user_id;; } filter: select_date { type: date suggest_explore: order_items suggest_dimension: order_items.created_date } measure: count { hidden: yes type: count drill_fields: [detail*] } dimension: user_id { primary_key: yes type: number sql: ${TABLE}.user_id ;; } dimension: lifetime_order_count { type: number sql: ${TABLE}.lifetime_order_count ;; } dimension: lifetime_revenue { type: number sql: ${TABLE}.lifetime_revenue ;; } measure: average_lifetime_revenue { type: average sql: ${TABLE}.lifetime_revenue ;; } measure: average_lifetime_order_count { type: average sql: ${TABLE}.lifetime_order_count ;; } dimension_group: first_order_date { type: time sql: ${TABLE}.first_order_date ;; } dimension_group: latest_order_date { type: time sql: ${TABLE}.latest_order_date ;; } set: detail { fields: [user_id, lifetime_order_count, lifetime_revenue, first_order_date_time, latest_order_date_time] } }

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ANAND G. · Revisado há 20 days

Great

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