-- COCOON BLOCK START: PLEASE DO NOT MODIFY THIS BLOCK FOR SELF-MAINTENANCE
-- Generated at 2024-07-06 00:03:47.783533+00:00
WITH 
"task_follower_data_projected" AS (
    -- Projection: Selecting 2 out of 3 columns
    -- Columns projected out: ['_fivetran_synced']
    SELECT 
        "task_id",
        "user_id"
    FROM "memory"."main"."task_follower_data"
),

"task_follower_data_projected_casted" AS (
    -- Column Type Casting: 
    -- task_id: from INT to VARCHAR
    -- user_id: from INT to VARCHAR
    SELECT
        CAST("task_id" AS VARCHAR) AS "task_id",
        CAST("user_id" AS VARCHAR) AS "user_id"
    FROM "task_follower_data_projected"
)

-- COCOON BLOCK END
SELECT * FROM "task_follower_data_projected_casted"

version: 2
models:
- name: stg_task_follower_data
  description: 'The table represents the follower relation between tasks and users.
    It contains two columns: task_id and user_id. Each row indicates that a specific
    user is following or associated with a particular task. This structure allows
    tracking which users are following or involved with which tasks in a system.'
  columns:
  - name: task_id
    description: Unique identifier for a task
    tests:
    - not_null
  - name: user_id
    description: Unique identifier for a user
    tests:
    - not_null

-- COCOON BLOCK START: PLEASE DO NOT MODIFY THIS BLOCK FOR SELF-MAINTENANCE
-- Generated at 2024-07-05 23:59:29.269778+00:00
WITH 
"project_task_data_projected" AS (
    -- Projection: Selecting 2 out of 3 columns
    -- Columns projected out: ['_fivetran_synced']
    SELECT 
        "project_id",
        "task_id"
    FROM "memory"."main"."project_task_data"
),

"project_task_data_projected_casted" AS (
    -- Column Type Casting: 
    -- project_id: from INT to VARCHAR
    -- task_id: from INT to VARCHAR
    SELECT
        CAST("project_id" AS VARCHAR) AS "project_id",
        CAST("task_id" AS VARCHAR) AS "task_id"
    FROM "project_task_data_projected"
)

-- COCOON BLOCK END
SELECT * FROM "project_task_data_projected_casted"

version: 2
models:
- name: stg_project_task_data
  description: The table represents a many-to-many relationship between projects and
    tasks. It shows which tasks are associated with which projects. Each row links
    a project (identified by project_id) to a task (identified by task_id). Multiple
    projects can be associated with the same task, and a project can have multiple
    tasks.
  columns:
  - name: project_id
    description: Unique identifier for a project
    tests:
    - not_null
  - name: task_id
    description: Unique identifier for a task
    tests:
    - not_null

-- COCOON BLOCK START: PLEASE DO NOT MODIFY THIS BLOCK FOR SELF-MAINTENANCE
-- Generated at 2024-07-06 00:01:13.008982+00:00
WITH 
"story_data_projected" AS (
    -- Projection: Selecting 9 out of 10 columns
    -- Columns projected out: ['_fivetran_synced']
    SELECT 
        "id",
        "created_at",
        "created_by_id",
        "hearted",
        "num_hearts",
        "source",
        "target_id",
        "text",
        "type"
    FROM "memory"."main"."story_data"
),

"story_data_projected_renamed" AS (
    -- Rename: Renaming columns
    -- id -> entry_id
    -- created_by_id -> author_id
    -- hearted -> is_hearted
    -- num_hearts -> heart_count
    -- source -> source_platform
    -- text -> content
    -- type -> entry_type
    SELECT 
        "id" AS "entry_id",
        "created_at",
        "created_by_id" AS "author_id",
        "hearted" AS "is_hearted",
        "num_hearts" AS "heart_count",
        "source" AS "source_platform",
        "target_id",
        "text" AS "content",
        "type" AS "entry_type"
    FROM "story_data_projected"
),

"story_data_projected_renamed_casted" AS (
    -- Column Type Casting: 
    -- author_id: from INT to VARCHAR
    -- created_at: from VARCHAR to TIMESTAMP
    -- entry_id: from INT to VARCHAR
    -- is_hearted: from DECIMAL to BOOLEAN
    -- target_id: from INT to VARCHAR
    SELECT
        "heart_count",
        "source_platform",
        "content",
        "entry_type",
        CAST("author_id" AS VARCHAR) AS "author_id",
        CAST("created_at" AS TIMESTAMP) AS "created_at",
        CAST("entry_id" AS VARCHAR) AS "entry_id",
        CAST("is_hearted" AS BOOLEAN) AS "is_hearted",
        CAST("target_id" AS VARCHAR) AS "target_id"
    FROM "story_data_projected_renamed"
)

-- COCOON BLOCK END
SELECT * FROM "story_data_projected_renamed_casted"

version: 2
models:
- name: stg_story_data
  description: The table is about story or message entries. It contains details like
    unique ID, creation timestamp, creator ID, number of hearts, source platform,
    target ID, text content, and message type. Each row represents a single story/message
    item with its associated metadata and engagement metrics.
  columns:
  - name: heart_count
    description: Number of hearts received by the entry
    tests:
    - not_null
  - name: source_platform
    description: Platform or medium where the entry was created
    tests:
    - not_null
    - accepted_values:
        values:
        - web
        - mobile
        - desktop
        - tablet
        - social media
        - email
        - SMS
        - print
        - television
        - radio
        - podcast
        - video
        - blog
        - forum
        - app
        - API
        - IoT device
        - wearable
        - voice assistant
        - kiosk
        - game console
        - VR/AR
  - name: content
    description: Content of the message or story (possibly encrypted)
    tests:
    - not_null
  - name: entry_type
    description: Classification or category of the entry
    tests:
    - not_null
    - accepted_values:
        values:
        - system
        - user
        - application
        - security
        - network
        - hardware
        - software
        - database
        - file
        - process
        - service
        - device
        - driver
        - kernel
        - memory
        - storage
        - backup
        - recovery
        - performance
        - configuration
        - update
        - error
        - event
        - audit
        - monitoring
  - name: author_id
    description: Unique identifier of the user who created the entry
    tests:
    - not_null
  - name: created_at
    description: Timestamp when the entry was created
    tests:
    - not_null
  - name: entry_id
    description: Unique identifier for the entry
    tests:
    - not_null
    - unique
    cocoon_meta:
      uniqueness: This column is described as a unique identifier for the entry. For
        this table, where each row represents a single story/message item, the entry_id
        should be unique across all rows, as it's designed to uniquely identify each
        entry.
  - name: is_hearted
    description: Indicates if the entry was hearted (seems unused)
    cocoon_meta:
      missing_acceptable: Empty when the entry has not been hearted.
  - name: target_id
    description: Identifier of the target or related entity
    tests:
    - not_null

-- COCOON BLOCK START: PLEASE DO NOT MODIFY THIS BLOCK FOR SELF-MAINTENANCE
-- Generated at 2024-07-05 23:59:07.456396+00:00
WITH 
"project_data_projected" AS (
    -- Projection: Selecting 14 out of 15 columns
    -- Columns projected out: ['_fivetran_synced']
    SELECT 
        "id",
        "_fivetran_deleted",
        "archived",
        "color",
        "created_at",
        "current_status",
        "due_date",
        "modified_at",
        "name",
        "notes",
        "owner_id",
        "public_",
        "team_id",
        "workspace_id"
    FROM "memory"."main"."project_data"
),

"project_data_projected_renamed" AS (
    -- Rename: Renaming columns
    -- id -> project_id
    -- _fivetran_deleted -> is_deleted
    -- archived -> is_archived
    -- color -> project_color
    -- created_at -> creation_date
    -- current_status -> project_status
    -- due_date -> project_deadline
    -- modified_at -> last_modified_date
    -- name -> project_name
    -- notes -> project_notes
    -- public_ -> is_public
    SELECT 
        "id" AS "project_id",
        "_fivetran_deleted" AS "is_deleted",
        "archived" AS "is_archived",
        "color" AS "project_color",
        "created_at" AS "creation_date",
        "current_status" AS "project_status",
        "due_date" AS "project_deadline",
        "modified_at" AS "last_modified_date",
        "name" AS "project_name",
        "notes" AS "project_notes",
        "owner_id",
        "public_" AS "is_public",
        "team_id",
        "workspace_id"
    FROM "project_data_projected"
),

"project_data_projected_renamed_cleaned" AS (
    -- Clean unusual string values: 
    -- project_status: The problem is that the project_status column contains a single value that appears to be a hashed or encoded string ('752e3db7344085cd1bdb19f63d241ca8') rather than a human-readable project status. This value is unusual because it doesn't provide any meaningful information about the status of the project. The correct values for a project status column would typically be descriptive terms like 'In Progress', 'Completed', 'On Hold', etc. Since we don't have any information about what this hash might represent, and there are no other values to compare it to, the safest approach is to map it to an empty string to indicate that the status is unknown or not provided. 
    SELECT
        "project_id",
        "is_deleted",
        "is_archived",
        "project_color",
        "creation_date",
        CASE
            WHEN "project_status" = '''752e3db7344085cd1bdb19f63d241ca8''' THEN ''''
            ELSE "project_status"
        END AS "project_status",
        "project_deadline",
        "last_modified_date",
        "project_name",
        "project_notes",
        "owner_id",
        "is_public",
        "team_id",
        "workspace_id"
    FROM "project_data_projected_renamed"
),

"project_data_projected_renamed_cleaned_null" AS (
    -- NULL Imputation: Impute Null to Disguised Missing Values
    -- project_notes: ['d41d8cd98f00b204e9800998ecf8427e']
    SELECT 
        CASE
            WHEN "project_notes" = 'd41d8cd98f00b204e9800998ecf8427e' THEN NULL
            ELSE "project_notes"
        END AS "project_notes",
        "team_id",
        "project_color",
        "project_id",
        "workspace_id",
        "creation_date",
        "owner_id",
        "last_modified_date",
        "is_archived",
        "is_deleted",
        "project_status",
        "project_name",
        "is_public",
        "project_deadline"
    FROM "project_data_projected_renamed_cleaned"
),

"project_data_projected_renamed_cleaned_null_casted" AS (
    -- Column Type Casting: 
    -- creation_date: from VARCHAR to TIMESTAMP
    -- is_public: from DECIMAL to VARCHAR
    -- last_modified_date: from VARCHAR to TIMESTAMP
    -- owner_id: from DECIMAL to VARCHAR
    -- project_id: from INT to VARCHAR
    -- team_id: from INT to VARCHAR
    -- workspace_id: from INT to VARCHAR
    SELECT
        "project_notes",
        "project_color",
        "is_archived",
        "is_deleted",
        "project_status",
        "project_name",
        "project_deadline",
        CAST("creation_date" AS TIMESTAMP) AS "creation_date",
        CAST("is_public" AS VARCHAR) AS "is_public",
        CAST("last_modified_date" AS TIMESTAMP) AS "last_modified_date",
        CAST("owner_id" AS VARCHAR) AS "owner_id",
        CAST("project_id" AS VARCHAR) AS "project_id",
        CAST("team_id" AS VARCHAR) AS "team_id",
        CAST("workspace_id" AS VARCHAR) AS "workspace_id"
    FROM "project_data_projected_renamed_cleaned_null"
),

"project_data_projected_renamed_cleaned_null_casted_missing_handled" AS (
    -- Handling missing values: There are 5 columns with unacceptable missing values
    -- owner_id has 18.75 percent missing. Strategy: 🔄 Unchanged
    -- project_color has 43.75 percent missing. Strategy: 🔄 Unchanged
    -- project_deadline has 93.75 percent missing. Strategy: 🗑️ Drop Column
    -- project_notes has 87.5 percent missing. Strategy: 🔄 Unchanged
    -- project_status has 93.75 percent missing. Strategy: 🗑️ Drop Column
    SELECT
        "project_notes",
        "project_color",
        "is_archived",
        "is_deleted",
        "project_name",
        "creation_date",
        "is_public",
        "last_modified_date",
        "owner_id",
        "project_id",
        "team_id",
        "workspace_id"
    FROM "project_data_projected_renamed_cleaned_null_casted"
)

-- COCOON BLOCK END
SELECT * FROM "project_data_projected_renamed_cleaned_null_casted_missing_handled"

version: 2
models:
- name: stg_project_data
  description: The table is about project data. It contains details such as project
    ID, name, creation date, modification date, color, status, due date, notes, owner
    ID, team ID, and workspace ID. Each row represents a unique project with its associated
    attributes. The table tracks project information including metadata like archival
    status and public visibility.
  columns:
  - name: project_notes
    description: Additional notes or comments about the project
    tests:
    - not_null
  - name: project_color
    description: Color assigned to the project for visual identification
    tests:
    - not_null
    - accepted_values:
        values:
        - light-blue
        - light-purple
        - dark-green
        - dark-pink
        - light-warm-gray
        - dark-blue
        - dark-purple
        - light-green
        - light-pink
        - dark-warm-gray
        - light-red
        - dark-red
        - light-orange
        - dark-orange
        - light-yellow
        - dark-yellow
        - light-gray
        - dark-gray
        - light-cyan
        - dark-cyan
  - name: is_archived
    description: Indicates if the project is archived
    tests:
    - not_null
  - name: is_deleted
    description: Indicates if the record has been deleted
    tests:
    - not_null
  - name: project_name
    description: Name or title of the project
    tests:
    - not_null
  - name: creation_date
    description: Timestamp of project creation
    tests:
    - not_null
  - name: is_public
    description: Indicates if the project is publicly visible
    cocoon_meta:
      missing_acceptable: Boolean flag; absence likely means project is not public.
  - name: last_modified_date
    description: Timestamp of last modification
    tests:
    - not_null
  - name: owner_id
    description: Identifier of the project owner
    tests:
    - not_null
  - name: project_id
    description: Unique identifier for the project
    tests:
    - not_null
    - unique
    cocoon_meta:
      uniqueness: This column contains a unique identifier for each project. For this
        table, each row represents a unique project. project_id is designed to be
        unique across all projects, ensuring that no two projects have the same ID.
  - name: team_id
    description: Identifier of the team associated with the project
    tests:
    - not_null
  - name: workspace_id
    description: Identifier of the workspace containing the project
    tests:
    - not_null

-- COCOON BLOCK START: PLEASE DO NOT MODIFY THIS BLOCK FOR SELF-MAINTENANCE
-- Generated at 2024-07-06 00:02:09.098470+00:00
WITH 
"tag_data_projected" AS (
    -- Projection: Selecting 8 out of 9 columns
    -- Columns projected out: ['_fivetran_synced']
    SELECT 
        "id",
        "_fivetran_deleted",
        "color",
        "created_at",
        "message",
        "name",
        "notes",
        "workspace_id"
    FROM "memory"."main"."tag_data"
),

"tag_data_projected_renamed" AS (
    -- Rename: Renaming columns
    -- id -> tag_id
    -- _fivetran_deleted -> is_deleted
    -- color -> tag_color
    -- created_at -> creation_timestamp
    -- message -> tag_message
    -- name -> tag_hash
    -- notes -> tag_notes
    SELECT 
        "id" AS "tag_id",
        "_fivetran_deleted" AS "is_deleted",
        "color" AS "tag_color",
        "created_at" AS "creation_timestamp",
        "message" AS "tag_message",
        "name" AS "tag_hash",
        "notes" AS "tag_notes",
        "workspace_id"
    FROM "tag_data_projected"
),

"tag_data_projected_renamed_casted" AS (
    -- Column Type Casting: 
    -- creation_timestamp: from VARCHAR to TIMESTAMP
    -- tag_color: from DECIMAL to VARCHAR
    -- tag_id: from INT to VARCHAR
    -- tag_message: from DECIMAL to VARCHAR
    -- tag_notes: from DECIMAL to VARCHAR
    -- workspace_id: from INT to VARCHAR
    SELECT
        "is_deleted",
        "tag_hash",
        CAST("creation_timestamp" AS TIMESTAMP) AS "creation_timestamp",
        CAST("tag_color" AS VARCHAR) AS "tag_color",
        CAST("tag_id" AS VARCHAR) AS "tag_id",
        CAST("tag_message" AS VARCHAR) AS "tag_message",
        CAST("tag_notes" AS VARCHAR) AS "tag_notes",
        CAST("workspace_id" AS VARCHAR) AS "workspace_id"
    FROM "tag_data_projected_renamed"
),

"tag_data_projected_renamed_casted_missing_handled" AS (
    -- Handling missing values: There are 3 columns with unacceptable missing values
    -- tag_color has 100.0 percent missing. Strategy: 🗑️ Drop Column
    -- tag_message has 100.0 percent missing. Strategy: 🗑️ Drop Column
    -- tag_notes has 100.0 percent missing. Strategy: 🗑️ Drop Column
    SELECT
        "is_deleted",
        "tag_hash",
        "creation_timestamp",
        "tag_id",
        "workspace_id"
    FROM "tag_data_projected_renamed_casted"
)

-- COCOON BLOCK END
SELECT * FROM "tag_data_projected_renamed_casted_missing_handled"

version: 2
models:
- name: stg_tag_data
  description: The table is about tags in a workspace system. It contains tag details
    like id, name, color, creation date, and associated workspace id. Each row represents
    a unique tag. The 'name' field contains hash-like values, possibly for identification
    or security purposes. The table tracks if tags are deleted and allows for optional
    messages and notes.
  columns:
  - name: is_deleted
    description: Indicates if the tag has been deleted
    tests:
    - not_null
  - name: tag_hash
    description: Hashed name or identifier for the tag
    tests:
    - not_null
    - unique
    cocoon_meta:
      uniqueness: This column represents a hashed name or identifier for the tag.
        For this table, each row represents a unique tag. The tag_hash appears to
        be unique across rows in the given sample, and it's likely designed to be
        unique for each tag.
  - name: creation_timestamp
    description: Timestamp when the tag was created
    tests:
    - not_null
  - name: tag_id
    description: Unique identifier for the tag
    tests:
    - not_null
    - unique
    cocoon_meta:
      uniqueness: This column is explicitly described as a unique identifier for the
        tag. For this table, each row represents a unique tag. The tag_id is designed
        to be unique across all tags in the system.
  - name: workspace_id
    description: Identifier of the workspace the tag belongs to
    tests:
    - not_null

-- COCOON BLOCK START: PLEASE DO NOT MODIFY THIS BLOCK FOR SELF-MAINTENANCE
-- Generated at 2024-07-06 00:00:06.130210+00:00
WITH 
"section_data_projected" AS (
    -- Projection: Selecting 4 out of 5 columns
    -- Columns projected out: ['_fivetran_synced']
    SELECT 
        "id",
        "created_at",
        "name",
        "project_id"
    FROM "memory"."main"."section_data"
),

"section_data_projected_renamed" AS (
    -- Rename: Renaming columns
    -- id -> section_id
    -- name -> section_hash
    SELECT 
        "id" AS "section_id",
        "created_at",
        "name" AS "section_hash",
        "project_id"
    FROM "section_data_projected"
),

"section_data_projected_renamed_casted" AS (
    -- Column Type Casting: 
    -- created_at: from VARCHAR to TIMESTAMP
    -- project_id: from INT to VARCHAR
    -- section_id: from INT to VARCHAR
    SELECT
        "section_hash",
        CAST("created_at" AS TIMESTAMP) AS "created_at",
        CAST("project_id" AS VARCHAR) AS "project_id",
        CAST("section_id" AS VARCHAR) AS "section_id"
    FROM "section_data_projected_renamed"
)

-- COCOON BLOCK END
SELECT * FROM "section_data_projected_renamed_casted"

version: 2
models:
- name: stg_section_data
  description: The table is about project sections. It contains details of each section,
    including a unique ID, creation date, name (likely a hash), and the project ID
    it belongs to. The table allows tracking of different sections within various
    projects, with each section having a specific creation timestamp and identifier.
  columns:
  - name: section_hash
    description: Hash-like name or identifier for the section
    tests:
    - not_null
  - name: created_at
    description: Timestamp when the section was created
    tests:
    - not_null
  - name: project_id
    description: Identifier of the project the section belongs to
    tests:
    - not_null
  - name: section_id
    description: Unique identifier for the section
    tests:
    - not_null
    - unique
    cocoon_meta:
      uniqueness: This column is a unique identifier for each section. For this table,
        each row represents a unique section, and the section_id is designed to be
        unique across all rows. It serves as the primary identifier for each section
        entry.

-- COCOON BLOCK START: PLEASE DO NOT MODIFY THIS BLOCK FOR SELF-MAINTENANCE
-- Generated at 2024-07-06 00:03:32.255648+00:00
WITH 
"task_data_renamed" AS (
    -- Rename: Renaming columns
    -- id -> task_id
    -- completed -> is_completed
    -- completed_at -> completion_timestamp
    -- created_at -> creation_timestamp
    -- due_on -> due_date
    -- due_at -> due_timestamp
    -- modified_at -> last_modified_timestamp
    -- name -> task_name
    -- parent_id -> parent_task_id
    -- start_on -> start_date
    -- notes -> task_description
    SELECT 
        "id" AS "task_id",
        "assignee_id",
        "completed" AS "is_completed",
        "completed_at" AS "completion_timestamp",
        "completed_by_id",
        "created_at" AS "creation_timestamp",
        "due_on" AS "due_date",
        "due_at" AS "due_timestamp",
        "modified_at" AS "last_modified_timestamp",
        "name" AS "task_name",
        "parent_id" AS "parent_task_id",
        "start_on" AS "start_date",
        "notes" AS "task_description",
        "workspace_id"
    FROM "memory"."main"."task_data"
),

"task_data_renamed_casted" AS (
    -- Column Type Casting: 
    -- assignee_id: from INT to VARCHAR
    -- completed_by_id: from DECIMAL to VARCHAR
    -- completion_timestamp: from VARCHAR to TIMESTAMP
    -- creation_timestamp: from VARCHAR to TIMESTAMP
    -- due_date: from VARCHAR to TIMESTAMP
    -- due_timestamp: from DECIMAL to TIMESTAMP
    -- last_modified_timestamp: from VARCHAR to TIMESTAMP
    -- parent_task_id: from DECIMAL to VARCHAR
    -- start_date: from DECIMAL to TIMESTAMP
    -- task_id: from INT to VARCHAR
    -- workspace_id: from INT to VARCHAR
    SELECT
        "is_completed",
        "task_name",
        "task_description",
        CAST("assignee_id" AS VARCHAR) AS "assignee_id",
        CAST("completed_by_id" AS VARCHAR) AS "completed_by_id",
        CAST("completion_timestamp" AS TIMESTAMP) AS "completion_timestamp",
        CAST("creation_timestamp" AS TIMESTAMP) AS "creation_timestamp",
        CAST("due_date" AS TIMESTAMP) AS "due_date",
        CAST("due_timestamp" AS TIMESTAMP) AS "due_timestamp",
        CAST("last_modified_timestamp" AS TIMESTAMP) AS "last_modified_timestamp",
        CAST("parent_task_id" AS VARCHAR) AS "parent_task_id",
        CAST("start_date" AS TIMESTAMP) AS "start_date",
        CAST("task_id" AS VARCHAR) AS "task_id",
        CAST("workspace_id" AS VARCHAR) AS "workspace_id"
    FROM "task_data_renamed"
),

"task_data_renamed_casted_missing_handled" AS (
    -- Handling missing values: There are 3 columns with unacceptable missing values
    -- due_timestamp has 100.0 percent missing. Strategy: 🗑️ Drop Column
    -- parent_task_id has 100.0 percent missing. Strategy: 🗑️ Drop Column
    -- start_date has 100.0 percent missing. Strategy: 🗑️ Drop Column
    SELECT
        "is_completed",
        "task_name",
        "task_description",
        "assignee_id",
        "completed_by_id",
        "completion_timestamp",
        "creation_timestamp",
        "due_date",
        "last_modified_timestamp",
        "task_id",
        "workspace_id"
    FROM "task_data_renamed_casted"
)

-- COCOON BLOCK END
SELECT * FROM "task_data_renamed_casted_missing_handled"

version: 2
models:
- name: stg_task_data
  description: The table is about tasks. It contains details like task ID, assignee,
    completion status, due dates, and workspace. Each row represents a single task
    with its properties. The table tracks task creation, modification, and completion
    dates. It also includes task names, notes, and parent task relationships. This
    structure allows for managing and tracking tasks within a workspace.
  columns:
  - name: is_completed
    description: Indicates if the task is completed
    tests:
    - not_null
  - name: task_name
    description: Name or title of the task
    tests:
    - not_null
  - name: task_description
    description: Additional information or details about the task
    tests:
    - not_null
  - name: assignee_id
    description: ID of the person assigned to the task
    tests:
    - not_null
  - name: completed_by_id
    description: ID of the person who completed the task
    cocoon_meta:
      missing_acceptable: Task not completed by anyone if is_completed is False.
  - name: completion_timestamp
    description: Timestamp when the task was completed
    tests:
    - not_null
  - name: creation_timestamp
    description: Timestamp when the task was created
    tests:
    - not_null
  - name: due_date
    description: Date when the task is due
    tests:
    - not_null
  - name: last_modified_timestamp
    description: Timestamp when the task was last modified
    tests:
    - not_null
  - name: task_id
    description: Unique identifier for the task
    tests:
    - not_null
    - unique
    cocoon_meta:
      uniqueness: This column contains a unique identifier for each task. In a well-designed
        task management system, each task should have a unique ID to distinguish it
        from all other tasks.
  - name: workspace_id
    description: ID of the workspace the task belongs to
    tests:
    - not_null

-- COCOON BLOCK START: PLEASE DO NOT MODIFY THIS BLOCK FOR SELF-MAINTENANCE
-- Generated at 2024-07-06 00:04:09.011709+00:00
WITH 
"task_section_data_projected" AS (
    -- Projection: Selecting 2 out of 3 columns
    -- Columns projected out: ['_fivetran_synced']
    SELECT 
        "section_id",
        "task_id"
    FROM "memory"."main"."task_section_data"
),

"task_section_data_projected_renamed" AS (
    -- Rename: Renaming columns
    -- task_id -> parent_task_id
    SELECT 
        "section_id",
        "task_id" AS "parent_task_id"
    FROM "task_section_data_projected"
),

"task_section_data_projected_renamed_casted" AS (
    -- Column Type Casting: 
    -- parent_task_id: from INT to VARCHAR
    -- section_id: from INT to VARCHAR
    SELECT
        CAST("parent_task_id" AS VARCHAR) AS "parent_task_id",
        CAST("section_id" AS VARCHAR) AS "section_id"
    FROM "task_section_data_projected_renamed"
)

-- COCOON BLOCK END
SELECT * FROM "task_section_data_projected_renamed_casted"

version: 2
models:
- name: stg_task_section_data
  description: The table represents the relationship between task sections and tasks.
    Each row links a section to a task. A task can have multiple sections. The section_id
    is unique for each section. The task_id connects sections to their parent task.
    This structure allows for organizing tasks into multiple sections or subtasks.
  columns:
  - name: parent_task_id
    description: Identifier linking sections to their parent task
    tests:
    - not_null
  - name: section_id
    description: Unique identifier for each task section
    tests:
    - not_null
    - unique
    cocoon_meta:
      uniqueness: This column represents a unique identifier for each task section.
        For this table, each row represents a link between a section and its parent
        task. The section_id is described as unique for each section, which means
        it is unique across rows.

-- COCOON BLOCK START: PLEASE DO NOT MODIFY THIS BLOCK FOR SELF-MAINTENANCE
-- Generated at 2024-07-06 00:05:01.424219+00:00
WITH 
"team_data_projected" AS (
    -- Projection: Selecting 4 out of 5 columns
    -- Columns projected out: ['_fivetran_synced']
    SELECT 
        "id",
        "_fivetran_deleted",
        "name",
        "organization_id"
    FROM "memory"."main"."team_data"
),

"team_data_projected_renamed" AS (
    -- Rename: Renaming columns
    -- id -> team_id
    -- _fivetran_deleted -> is_deleted
    -- name -> encrypted_team_name
    SELECT 
        "id" AS "team_id",
        "_fivetran_deleted" AS "is_deleted",
        "name" AS "encrypted_team_name",
        "organization_id"
    FROM "team_data_projected"
),

"team_data_projected_renamed_casted" AS (
    -- Column Type Casting: 
    -- organization_id: from INT to VARCHAR
    -- team_id: from INT to VARCHAR
    SELECT
        "is_deleted",
        "encrypted_team_name",
        CAST("organization_id" AS VARCHAR) AS "organization_id",
        CAST("team_id" AS VARCHAR) AS "team_id"
    FROM "team_data_projected_renamed"
)

-- COCOON BLOCK END
SELECT * FROM "team_data_projected_renamed_casted"

version: 2
models:
- name: stg_team_data
  description: The table is about teams within an organization. Each row represents
    a team with a unique ID. The table includes the team's name (encrypted), whether
    it's deleted, and the organization ID it belongs to. All teams shown belong to
    the same organization (2104505001950). The table likely tracks teams across different
    organizations for management or analytics purposes.
  columns:
  - name: is_deleted
    description: Indicates if the team has been deleted
    tests:
    - not_null
  - name: encrypted_team_name
    description: Encrypted name of the team
    tests:
    - not_null
  - name: organization_id
    description: Identifier of the organization the team belongs to
    tests:
    - not_null
  - name: team_id
    description: Unique identifier for the team
    tests:
    - not_null
    - unique
    cocoon_meta:
      uniqueness: This column represents a unique identifier for each team. For this
        table, each row represents a distinct team within the organization. The team_id
        appears to be unique across rows, as each entry has a different value.

-- COCOON BLOCK START: PLEASE DO NOT MODIFY THIS BLOCK FOR SELF-MAINTENANCE
-- Generated at 2024-07-06 00:04:25.812042+00:00
WITH 
"task_tag_data_projected" AS (
    -- Projection: Selecting 2 out of 3 columns
    -- Columns projected out: ['_fivetran_synced']
    SELECT 
        "tag_id",
        "task_id"
    FROM "memory"."main"."task_tag_data"
),

"task_tag_data_projected_casted" AS (
    -- Column Type Casting: 
    -- tag_id: from INT to VARCHAR
    -- task_id: from INT to VARCHAR
    SELECT
        CAST("tag_id" AS VARCHAR) AS "tag_id",
        CAST("task_id" AS VARCHAR) AS "task_id"
    FROM "task_tag_data_projected"
)

-- COCOON BLOCK END
SELECT * FROM "task_tag_data_projected_casted"

version: 2
models:
- name: stg_task_tag_data
  description: The table represents a many-to-many relationship between tags and tasks.
    Each row links a tag (identified by tag_id) to a task (identified by task_id).
    Multiple tags can be associated with a single task, and a single tag can be applied
    to multiple tasks. This structure allows for flexible categorization and organization
    of tasks using tags.
  columns:
  - name: tag_id
    description: Unique identifier for a tag
    tests:
    - not_null
  - name: task_id
    description: Unique identifier for a task
    tests:
    - not_null