本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

在 Amazon Connect 分析資料湖中排程資料

本主題詳細說明 Amazon Connect 分析資料湖排程資料表中的內容。資料表列出內容的欄、類型和描述。

有兩種方式可存取分析資料湖並設定要共用的資料：

如果您無法使用「選項 1」存取排程資料表，請嘗試使用「選項 2」。

員工排程設定檔

資料表名稱：staff_scheduling_profile

複合主鍵：{instance_id, agent_arn, staff_scheduling_profile_version}

輪班活動

資料表名稱：shift_activities

複合主鍵：{instance_id, shift_activity_arn, shift_activity_version}

輪班設定檔

資料表名稱：shift_profiles

複合主鍵：{instance_id, shift_profile_arn, shift_profile_version}

人員配置群組

資料表名稱：staffing_groups

複合主鍵：{instance_id, staffing_group_arn, staffing_group_version}

人員配置群組 - 預測群組

資料表名稱：staffing_group_forecast_groups

複合主鍵：{instance_id, staffing_group_arn, staffing_group_version, forecast_group_arn}

應透過在 staffing_group_arn 和 staffing_group_version 上聯結 staffing_groups 資料表來查詢此資料表。

人員配置群組 - 主管

資料表名稱：staffing_group_supervisors

複合主鍵：{instance_id, staffing_group_arn, staffing_group_version, supervisor_arn}

應透過在 staffing_group_arn 和 staffing_group_version 上聯結 staffing_groups 資料表來查詢此資料表。

員工輪班

資料表名稱：staff_shifts

複合主鍵：{instance_id, shift_id, shift_version}

員工輪班活動

資料表名稱：staff_shift_activities

複合主鍵：{instance_id, shift_id, shift_version, activity_id}

應透過在 shift_id 和 shift_version 上聯結 staff_shifts 資料表來查詢此資料表。

員工休假餘額變更

資料表名稱：staff_timeoff_balance_changes

複合主鍵：{instance_id, agent_arn, shift_activity_arn, timeoff_balance_version}

員工休假

資料表名稱：staff_timeoffs

複合主鍵：{instance_id, timeoff_id, agent_arn, timeoff_version}

員工休假間隔

資料表名稱：staff_timeoff_intervals

複合主鍵：{instance_id, timeoff_id, timeoff_version, interval_id}

應透過在 timeoff_id 和 timeoff_version 上聯結 staff_timeoffs 資料表來查詢此資料表。

員工需求群組

資料表名稱： staff_demand_group

複合主鍵：{instance_id, agent_arn, demand_group_arn, staff_demand_group_version}

人員配置群組需求群組

資料表名稱： staffing_group_demand_group

複合主鍵：{instance_id, staffing_group_arn, demand_group_arn, staffing_group_demand_group_version}

員工輪班活動分配

資料表名稱： staff_shift_activity_allocations

複合主鍵：{instance_id, shift_id, shift_version, activity_id, demand_group_arn}

資料結構描述

以下是實體關係圖，顯示 Amazon Connect 分析資料湖中排程資料表之間的結構和關係。

每個資料表會顯示其主索引鍵和屬性及其資料類型。圖表說明這些資料表如何透過外部索引鍵關係相互關聯，提供排程資料模型的全面檢視。

範例查詢

1. 查詢以取得在特定預測群組上工作之客服人員的所有排程輪班活動

SELECT * FROM agent_scheduled_shift_activities_view where forecast_group_name = 'AnyDepartmentForecastGroup'

完成下列步驟以建立上述的 agent_scheduled_shift_activities_view。

步驟 1：建立檢視以取得主管名稱

CREATE OR REPLACE VIEW "latest_supervisor_names_view" AS
SELECT
  staffing_group_arn
, array_agg(supervisor_name ORDER BY supervisor_name ASC) supervisor_names
FROM
  (
   SELECT
     s.staffing_group_arn
   , CONCAT(u.first_name, ' ', u.last_name) supervisor_name
   FROM
     ((
      SELECT
        staffing_group_arn
      , supervisor_arn
      FROM
        (
         SELECT
           *
         , RANK() OVER (PARTITION BY staffing_group_arn ORDER BY staffing_group_version DESC) recency
         FROM
           staffing_group_supervisors
         WHERE (instance_id = 'YourAmazonConnectInstanceId')
      )  t
      WHERE (recency = 1)
   )  s
   INNER JOIN USERS u ON (s.supervisor_arn = u.user_arn))
)
GROUP BY staffing_group_arn

步驟 2：建立檢視以取得與客服人員相關聯的人員配置群組和預測群組

CREATE OR REPLACE VIEW "latest_agent_staffing_group_forecast_group_view" AS
WITH
  latest_staff_scheduling_profile AS (
   SELECT
     agent_arn
   , staffing_group_arn
   , last_updated_timestamp
   FROM
     (
      SELECT
        *
      , RANK() OVER (PARTITION BY agent_arn ORDER BY staff_scheduling_profile_version DESC) recency
      FROM
        staff_scheduling_profile
      WHERE ((instance_id = 'YourAmazonConnectInstanceId') AND (is_deleted = false))
   )  t
   WHERE (recency = 1)
)
, latest_staffing_groups AS (
   SELECT
     staffing_group_name
   , staffing_group_arn
   FROM
     (
      SELECT
        *
      , RANK() OVER (PARTITION BY staffing_group_arn ORDER BY staffing_group_version DESC) recency
      FROM
        staffing_groups
      WHERE (instance_id = 'YourAmazonConnectInstanceId')
   )  t
   WHERE (recency = 1)
)
, latest_forecast_groups AS (
   SELECT
     forecast_group_arn
   , forecast_group_name
   FROM
     (
      SELECT
        *
      , RANK() OVER (PARTITION BY forecast_group_arn ORDER BY forecast_group_version DESC) recency
      FROM
        forecast_groups
      WHERE (instance_id = 'YourAmazonConnectInstanceId')
   )  t
   WHERE (recency = 1)
)
, latest_staffing_group_forecast_groups AS (
   SELECT
     staffing_group_arn
   , forecast_group_arn
   FROM
     (
      SELECT
        *
      , RANK() OVER (PARTITION BY staffing_group_arn ORDER BY staffing_group_version DESC) recency
      FROM
        staffing_group_forecast_groups
      WHERE (instance_id = 'YourAmazonConnectInstanceId')
   )  t
   WHERE (recency = 1)
)
SELECT
  ssp.agent_arn
, U.agent_username AS username
, U.agent_routing_profile_id AS routing_profile_id
, CONCAT(u.first_name, ' ', u.last_name) agent_name
, fg.forecast_group_arn
, fg.forecast_group_name
, sg.staffing_group_arn
, sg.staffing_group_name
FROM
 latest_staff_scheduling_profile ssp
INNER JOIN latest_staffing_groups sg ON ssp.staffing_group_arn = sg.staffing_group_arn
INNER JOIN latest_staffing_group_forecast_groups sgfg ON ssp.staffing_group_arn = sgfg.staffing_group_arn
INNER JOIN latest_forecast_groups fg ON fg.forecast_group_arn = sgfg.forecast_group_arn
INNER JOIN USERS u ON ssp.agent_arn = u.user_arn

步驟 3：取得最新的輪班活動

CREATE OR REPLACE VIEW "latest_shift_activities_view" AS
SELECT
  shift_activity_arn
, shift_activity_name
, shift_activity_version
, type
, sub_type
, is_adherence_tracked
, is_paid
, last_updated_timestamp
FROM
  (
   SELECT
     *
   , RANK() OVER (PARTITION BY shift_activity_arn ORDER BY shift_activity_version DESC) recency
   FROM
     shift_activities
   WHERE (instance_id = 'YourAmazonConnectInstanceId')
)  t
WHERE (recency = 1)

步驟 4：建立檢視以取得客服人員排程輪班活動

CREATE OR REPLACE VIEW "agent_scheduled_shift_activities_view" AS
WITH
  latest_staff_shifts AS (
   SELECT
     agent_arn
   , shift_id
   , shift_version
   , shift_start_timestamp
   , shift_end_timestamp
   , created_timestamp
   , last_updated_timestamp
   , data_lake_last_processed_timestamp
   , recency
   FROM
     (
      SELECT
        RANK() OVER (PARTITION BY shift_id ORDER BY shift_version DESC) recency
      , *
      FROM
        staff_shifts sa
      WHERE (instance_id = 'YourAmazonConnectInstanceId')
   )  t
   WHERE ((recency = 1) AND (is_deleted = false))
)
SELECT
  asgfg.forecast_group_name
, array_join(sn.supervisor_names, ',') supervisor_names
, s.agent_arn
, u.first_name
, u.last_name
, asgfg.staffing_group_name
, ssa.activity_id
, (CASE WHEN (ssa.shift_activity_arn IS NULL) THEN COALESCE(sa.shift_activity_name, 'Work') ELSE sa.shift_activity_name END) shift_activity_name
, s.shift_start_timestamp
, s.shift_end_timestamp
, (CASE WHEN (ssa.shift_activity_arn IS NULL) THEN COALESCE(sa.type, 'PRODUCTIVE') ELSE sa.type END) type
, (CASE WHEN (ssa.shift_activity_arn IS NULL) THEN COALESCE(sa.is_paid, true) ELSE sa.is_paid END) is_paid
, ssa.activity_start_timestamp
, ssa.activity_end_timestamp
, ssa.last_updated_timestamp
, ssa.data_lake_last_processed_timestamp
, u.agent_username as username
, u.agent_routing_profile_id as routing_profile_id
FROM
  staff_shift_activities ssa
INNER JOIN latest_staff_shifts s ON s.shift_id = ssa.shift_id AND s.shift_version = ssa.shift_version
INNER JOIN USERS u ON s.agent_arn = u.user_arn
INNER JOIN latest_agent_staffing_group_forecast_group_view asgfg ON s.agent_arn = asgfg.agent_arn
LEFT JOIN latest_shift_activities_view sa ON sa.shift_activity_arn = ssa.shift_activity_arn
INNER JOIN latest_supervisor_names_view sn ON sn.staffing_group_arn = asgfg.staffing_group_arn
WHERE (ssa.is_deleted = false) AND (COALESCE(ssa.activity_status, ' ') <> 'INACTIVE') AND (ssa.instance_id = 'YourAmazonConnectInstanceId')

2. 查詢以取得特定預測群組中客服人員的所有休假請求

SELECT * FROM agent_timeoff_report_view where forecast_group_name = 'AnyDepartmentForecastGroup'

使用下列查詢來建立上述的 agent_timeoff_report_view。

CREATE OR REPLACE VIEW "agent_timeoff_report_view" AS
WITH latest_staff_timeoffs AS (
        SELECT t1.*,
            CAST((t1.effective_timeoff_hours * 60) AS INT) total_effective_timeoff_minutes
        FROM (
                SELECT RANK() OVER (
                        PARTITION BY timeoff_id
                        ORDER BY timeoff_version DESC
                    ) recency,
                    agent_arn,
                    timeoff_id,
                    shift_activity_arn,
                    timeoff_status,
                    timeoff_version,
                    effective_timeoff_hours,
                    timeoff_start_timestamp,
                    timeoff_end_timestamp,
                    last_updated_timestamp,
                    data_lake_last_processed_timestamp
                FROM staff_timeoffs
                WHERE (
                        instance_id = 'YourAmazonConnectInstanceId'
                    )
            ) t1
        WHERE (recency = 1)
    )
SELECT asgfg.forecast_group_name,
    to.agent_arn,
    asgfg.agent_name,
    asgfg.staffing_group_name,
    asgfg.username,
    sa.shift_activity_name,
    to.timeoff_start_timestamp,
    to.timeoff_end_timestamp,
    to.timeoff_status,
    array_join(sn.supervisor_names, ',') AS supervisor_names,
    sa.is_paid,
    to.last_updated_timestamp,
    to.data_lake_last_processed_timestamp,
    u.agent_routing_profile_id AS routing_profile_id,
    to.timeoff_id,

    to.shift_activity_arn,
    to.total_effective_timeoff_minutes
FROM latest_staff_timeoffs to
    INNER JOIN latest_agent_staffing_group_forecast_group_view asgfg ON asgfg.agent_arn = to.agent_arn
    INNER JOIN latest_shift_activities_view sa ON sa.shift_activity_arn = to.shift_activity_arn
    INNER JOIN latest_supervisor_names_view sn ON sn.staffing_group_arn = asgfg.staffing_group_arn
    INNER JOIN users u ON u.user_arn = to.agent_arn