Overview

Looker is a modern data platform in Google Cloud that lets you analyze and visualize your data interactively. You can use Looker to do in-depth data analysis, integrate insights across different data sources, build actionable data-driven workflows, and create custom data applications.

In Looker, dimensions are unique attributes of the data that help you to describe data. For example, the city and elevation of an airport may be different dimensions within an airport dataset. Measures are aggregations of one or more dimensions (or unique attributes of the data) such as a count or average. Measures let you calculate your Key Performance Indicators (KPIs) and help your business users analyze data using different aggregated attributes.

In this lab, you learn how to create different types of dimensions and measures in LookML as a Looker developer. You also learn how to modify models of Explores, which are data views that serve as the foundation for self-service exploration by the business users in Looker.

For this lab, a project called qwiklabs-ecommerce has been created for you in LookML. This project is based on a mock e-commerce dataset that will enable you to create business KPIs using dimensions and measures created using LookML. You can learn more about LookML modeling in the Looker documentation.

---

users.view

view: users {
  sql_table_name: `cloud-training-demos.looker_ecomm.users`
    ;;
  drill_fields: [id]

  dimension: id {
    primary_key: yes
    type: number
    sql: ${TABLE}.id ;;
  }

  dimension: age {
    type: number
    sql: ${TABLE}.age ;;
  }

  dimension: age_tier {
    type: tier
    tiers: [18, 25, 35, 45, 55, 65, 75, 90]
    style: integer
    sql: ${age} ;;
  }

  dimension: city {
    type: string
    sql: ${TABLE}.city ;;
  }

  dimension: country {
    type: string
    map_layer_name: countries
    sql: ${TABLE}.country ;;
  }

  dimension_group: created {
    type: time
    timeframes: [
      raw,
      time,
      date,
      week,
      month,
      quarter,
      year
    ]
    sql: ${TABLE}.created_at ;;
  }

  dimension: email {
    type: string
    sql: ${TABLE}.email ;;
  }

  dimension: first_name {
    type: string
    sql: ${TABLE}.first_name ;;
  }

  dimension: gender {
    type: string
    sql: ${TABLE}.gender ;;
  }

  dimension: last_name {
    type: string
    sql: ${TABLE}.last_name ;;
  }

  dimension: latitude {
    type: number
    sql: ${TABLE}.latitude ;;
  }

  dimension: longitude {
    type: number
    sql: ${TABLE}.longitude ;;
  }

  dimension: state {
    type: string
    sql: ${TABLE}.state ;;
    map_layer_name: us_states
  }

  dimension: traffic_source {
    type: string
    sql: ${TABLE}.traffic_source ;;
  }

  dimension: is_email_source {
    type: yesno
    sql: ${traffic_source} = "Email" ;;
  }

  dimension: zip {
    type: zipcode
    sql: ${TABLE}.zip ;;
  }

  measure: count {
    type: count
    drill_fields: [id, last_name, first_name, events.count, order_items.count]
  }
}

order_items.view

view: order_items {
  sql_table_name: `cloud-training-demos.looker_ecomm.order_items`
    ;;
  drill_fields: [order_item_id]

  dimension: order_item_id {
    primary_key: yes
    type: number
    sql: ${TABLE}.id ;;
  }

  dimension_group: created {
    type: time
    timeframes: [
      raw,
      time,
      date,
      week,
      month,
      quarter,
      year
    ]
    sql: ${TABLE}.created_at ;;
  }

  dimension_group: delivered {
    type: time
    timeframes: [
      raw,
      date,
      week,
      month,
      quarter,
      year
    ]
    convert_tz: no
    datatype: date
    sql: ${TABLE}.delivered_at ;;
  }

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

  dimension: order_id {
    type: number
    sql: ${TABLE}.order_id ;;
  }

  dimension_group: returned {
    type: time
    timeframes: [
      raw,
      time,
      date,
      week,
      month,
      quarter,
      year
    ]
    sql: ${TABLE}.returned_at ;;
  }

  dimension: sale_price {
    type: number
    sql: ${TABLE}.sale_price ;;
  }

  dimension_group: shipped {
    type: time
    timeframes: [
      raw,
      date,
      week,
      month,
      quarter,
      year
    ]
    convert_tz: no
    datatype: date
    sql: ${TABLE}.shipped_at ;;
  }

  dimension: shipping_days {
    type: number
    sql: DATE_DIFF(${shipped_date}, ${created_date}, DAY);;
  }

  dimension: status {
    type: string
    sql: ${TABLE}.status ;;
  }

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


  measure: average_sale_price {
    type: average
    sql: ${sale_price} ;;
    drill_fields: [detail*]
    value_format_name: usd_0
  }

  measure: count_distinct_orders {
    type: count_distinct
    sql: ${order_id} ;;
  }

  measure: order_item_count {
    type: count
    drill_fields: [detail*]
  }

  measure: percentage_sales_email_source {
    type: number
    value_format_name: percent_2
    sql: 1.0*${total_sales_email_users}
      / NULLIF(${total_sales}, 0) ;;
  }

  measure: total_sales_email_users {
    type: sum
    sql: ${sale_price} ;;
    filters: [users.traffic_source: "Email"]
  }

  measure: total_sales {
    type: sum
    sql: ${sale_price} ;;
    value_format_name: usd_0
  }

  measure: order_count {
    type: count_distinct
    sql: ${order_id} ;;
  }

  measure: total_revenue {
    type: sum
    sql: ${sale_price} ;;
    value_format_name: usd
  }

  measure: total_revenue_from_completed_orders {
    type: sum
    sql: ${sale_price} ;;
    filters: [status: "Complete"]
    value_format_name: usd
  }


  # ----- Sets of fields for drilling ------
  set: detail {
    fields: [
      order_item_id,
      users.last_name,
      users.id,
      users.first_name,
      inventory_items.id,
      inventory_items.product_name
    ]
  }
}