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Merge pull request #28 from Ritik574-coder/dbt_branch
adding the document and generic build in test about product , inventory and employees
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‎models/intermediate/Employees/_int_employees_models.yml‎

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version: 2
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models:
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#=========================================================================================
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#================================ Models : int_employee_profile ==========================
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#=========================================================================================
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- name: int_employee_profile
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description: |
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**Model Purpose:**
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Creates a trusted employee profile dataset by isolating, tokenizing, and standardizing
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core identity and onboarding configurations from raw employee records. The model resolves
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critical data quality issues by programmatically decomposing unified full name blocks into
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discrete name components (`first_name`, `last_name`) and parsing highly fragmented string
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inputs into deterministic, ISO-compliant calendar dates. The resulting dataset establishes
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a high-integrity foundation for workforce demographics, accurate identity tracking,
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headcount timelines, and downstream dimensional modeling.
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columns:
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- name: employee_id
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description: |
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Primary key of the employee profile dimension. Inherited from the staging infrastructure
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and validated for row uniqueness.
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tests:
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- unique
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- not_null
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- name: first_name
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description: |
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The isolated given name of the employee.
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**Transformation Logic:** Calculated using a structural string metric. It counts the whitespace
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delimiters within the raw name block. If exactly one space is present (representing a standard
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two-part name structure), it translates spaces to dots and leverages the system `PARSENAME` object
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metadata parser to pull the second segment from the right.
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tests:
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- not_null
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- name: last_name
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description: |
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The isolated family name or surname of the employee.
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**Transformation Logic:** Programmatically extracts the absolute right-most token from the
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`full_name` field by splitting on whitespace components. This field is guaranteed to populate
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regardless of whether the employee has a middle initial or complex prefix.
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tests:
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- not_null
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- name: hire_date
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description: |
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The canonical calendar date marking the employee's formal onboarding into the enterprise network.
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**Transformation Logic:** Converts multiple incoming text-based date patterns into an immutable
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ISO-standard `DATE` (YYYY-MM-DD) format using an explicit `CASE` branching ladder:
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- Textual month names (e.g., 'January 15, 2021') and standard hyphen/slash formats
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(e.g., 'YYYY-MM-DD') are parsed via native conversion.
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- Regional date ambiguity is resolved dynamically: if the leading numerical segment exceeds 12,
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it is cast via International conventions (Styles 103/105); if the middle segment exceeds 12,
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it is treated as a US convention (Styles 101/110).
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- Any unmapped or non-standard variations safely fallback onto a standard casting function
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to maintain temporal tracking integrity.
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tests:
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- not_null
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#=========================================================================================
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#============================= Models : int_employee_employment ==========================
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#=========================================================================================
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version: 2
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models:
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- name: int_employee_employment
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description: |
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Creates a trusted employee employment dataset by standardizing organizational
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- Sales Manager
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- Sales Representative
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- Senior Sales Associate
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- Store Manager Account Executive
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- Customer Advisor
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- Regional Manager
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- Sales Associate
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- Sales Consultant
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- Sales Manager
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- Sales Representative
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- Senior Sales Associate
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- Store Manager
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- Store Manager
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- name: department
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description: |
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The standardized corporate department classification (e.g., Sales, Operations).
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The standardized corporate department classification (e.g., Sales, Operations etc..).
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**Transformation Logic:** Implements defensive string trimming and converts missing/null/blank
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values to `'Unknown'`. This column provides a bulletproof aggregation path for departmental
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headcount and cost center distribution analysis.
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- Management
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- Operations
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- Sales
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- Unknown
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- name: store_id
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description: |
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The normalized performance evaluation bucket resulting from the employee's latest review cycle.
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**Transformation Logic:** Resolves massive legacy data fragmentation. It interprets a mixed-type
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input vector containing numbers (`5`, `4`, `3`, `2`), letter grades (`A`, `B`, `C`, `D`), and
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untrimmed text descriptions, mapping them into a clean 4-tier categorical dimension:
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- *'Excellent'* (Maps from: 'excellent', 'a', '5')
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- *'Good'* (Maps from: 'good', 'b', '4')
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- *'Average'* (Maps from: 'average', 'c', '3')
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- *'Below Average'* (Maps from: 'below average', 'd', '2')
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- Any missing, blank, or unmapped value default securely to *'Unknown'*.
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untrimmed text descriptions, mapping them into a clean 4-tier categorical dimension
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(example :'Excellent', 'Good', 'Average', 'Below Average', 'Unknown' )
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tests:
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- accepted_values:
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arguments:
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- not_null
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- accepted_values:
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arguments:
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values: ['True', 'False']
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values: ['True', 'False']
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#=========================================================================================
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#================================ Models : int_employee_contact ==========================
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#=========================================================================================
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- name: int_employee_contact
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description: |
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Creates a trusted employee contact dataset by improving the quality and
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consistency of employee communication attributes. The model validates and
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repairs email addresses and standardizes phone numbers into a common format.
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The resulting dataset provides reliable contact information for internal
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communications, identity matching, corporate directory services, and downstream
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analytical models.
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columns:
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- name: employee_id
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description: |
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Primary key of the employee contact dimension. Inherited from the
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staging layer and used to uniquely identify individual resource records.
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tests:
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- unique
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- not_null
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- name: email
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description: |
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The standardized corporate or personal email address of the employee.
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**Transformation Logic:** Implements programmatic cleansing to resolve formatting anomalies:
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- Null or empty strings, as well as entries completely missing the '@' delimiter, are forced to 'Unknown'.
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- Structural duplication issues (e.g., multiple '@' signs) are resolved by isolating the first instance and stripping duplicate characters from the domain string.
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- All valid entries are trimmed and cast to lowercase to maintain strict grouping consistency.
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tests:
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- not_null
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- name: phone
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description: |
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The employee's phone number standardized into the corporate '+1 (XXX) XXX-XXXX' format.
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**Transformation Logic:** Evaluates incoming string patterns against five distinct structural signatures to re-align components uniformly:
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- Formats starting with a plus sign and an international code (e.g., '+1XXXXXXXXXX') are parsed and injected with standard spaces, brackets, and hyphens.
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- Dotted strings ('XXX.XXX.XXXX') or continuous digit sequences ('XXXXXXXXXX') are sliced into area code, prefix, and line number groups before concatenation.
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- Hyphen-separated values ('XXX-XXX-XXXX') are combined directly by preserving the tail positions.
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- Already formatted parenthesized elements ('(XXX) XXX-XXXX') are prepended with the standard international country indicator '+1 '.
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tests:
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- not_null

models/intermediate/Employees/int_employees_contact.sql renamed to models/intermediate/Employees/int_employee_contact.sql

File renamed without changes.

‎models/intermediate/Inventory/_int_inventory_models.yml‎

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version: 2
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models:
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####################################################################################
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####################### Model: int_inventory_product ###############################
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####################################################################################
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- name: int_inventory_product
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description: |
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Creates a trusted product tracking dataset by standardizing snapshot logging dates,
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cleaning product identifiers, and resolving textual categorization anomalies across
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inventory snapshots. The model handles regional variations in string-based date configurations
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and maps product categories into a standardized, clean taxonomy. The resulting dataset
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provides a consistent product grain for operational inventory control and downstream reporting.
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columns:
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- name: inventory_snapshot_sk
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description: Surrogate primary key uniquely identifying a specific product item log during a snapshot period.
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tests:
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- unique
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- not_null
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- name: snapshot_date
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description: |
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The standardized calendar logging date for inventory item records.
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**Transformation Logic:** Implements an explicit CASE validation ladder to parse text-based dates into an ISO-standard DATE format:
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- Textual or abbreviated months and standard hyphen/slash paths (YYYY-MM-DD or YYYY/MM/DD) are natively converted.
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- Multi-locale string structures are evaluated dynamically: if the middle token is greater than 12, it handles it as a US format (Style 101 or 110);
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if the leading token is greater than 12, it is routed via International styles (Style 103 or 105).
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tests:
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- not_null
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- name: product_id
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description: |
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The validated operational identifier for the product. Un-castable alphanumeric strings
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or missing values are safely coerced to NULL to prevent downstream join failures.
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tests:
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- not_null
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- name: product_name
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description: |
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Official product nomenclature. Leading and trailing whitespaces are removed via trimming,
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with missing or blank values defaulting to 'Unknown'.
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tests:
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- not_null
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- name: sku
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description: |
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Stock Keeping Unit alpha-numeric tracking string. Whitespace components are trimmed
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and missing or empty records resolve to 'Unknown'.
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tests:
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- not_null
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- name: category
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description: |
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Standardized product line categorization. Maps varied lowercase or messy textual entries into
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10 business-defined Title Case operational paths: 'Electronics', 'Clothing', 'Kitchen', 'Office',
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'Sports', 'Health', 'Beauty', 'Footwear', 'Toys', or 'Bags'. Unmatched configurations resolve to 'Unknown'.
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tests:
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- not_null
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- accepted_values:
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arguments:
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values: ['Electronics', 'Clothing', 'Kitchen', 'Office', 'Sports', 'Health', 'Beauty', 'Footwear', 'Toys', 'Bags', 'Unknown']
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####################################################################################
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####################### Model: int_inventory_stock #################################
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####################################################################################
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- name: int_inventory_stock
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description: |
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Calculates key physical unit availability metrics across inventory snapshots by enforcing
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numeric boundaries. The model validates system counts, filters out impossible negative numbers,
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and computes real-time pickable availability balances to provide an accurate picture of net warehouse stock.
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columns:
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- name: inventory_snapshot_sk
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description: Surrogate primary key identifying the unique item snapshot sequence.
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tests:
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- unique
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- not_null
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- name: stock_on_hand
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description: |
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The total recorded physical item count present inside the warehouse facility.
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Alphanumeric parsing anomalies or negative value errors are explicitly set to NULL.
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- name: stock_reserved
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description: |
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The count of units allocated or committed to active customer orders.
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Negative entries or invalid numeric data strings are coerced to NULL.
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- name: stock_available
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description: |
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The net quantity of items currently pickable, sellable, and uncommitted.
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**Transformation Logic:** Dynamically calculated inline as `stock_on_hand` minus `stock_reserved`.
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- name: reorder_level
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description: |
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The replenishment baseline safety milestone count. Values failing integer conversion
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or registering below zero are converted to NULL.
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####################################################################################
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####################### Model: int_inventory_valuation #############################
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####################################################################################
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- name: int_inventory_valuation
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description: |
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Enriches snapshot files with high-precision asset valuation and pricing metrics. The model
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strips formatting noise (such as currency symbols and comma grouping separators) from text fields,
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standardizes locations, and calculates extended inventory holding values for financial and margin analysis.
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columns:
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- name: inventory_snapshot_sk
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description: Surrogate primary key identifying the evaluated warehouse ledger item.
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tests:
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- unique
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- not_null
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- name: unit_cost
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description: |
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The acquisition cost per unit cast to a decimal(10,2) format for exact precision.
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**Transformation Logic:** Programmatically checks for and strips leading '$' markers from text strings before conversion.
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- name: unit_price
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description: |
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The Manufacturer's Suggested Retail Price (MSRP) for the item, cast to a decimal(10,2) format.
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**Transformation Logic:** Cleanses text strings by stripping out both currency '$' characters
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and embedded thousands comma groupings (',') to allow safe mathematical execution.
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- name: stock_on_hand
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description: |
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The physical count of units present in the warehouse. Non-numeric inputs or negative counts
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resolve to NULL to protect downstream multiplication.
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- name: inventory_value
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description: |
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The extended total dollar value of the stock on hand.
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**Transformation Logic:** Evaluated dynamically by multiplying the cleaned `unit_price`
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by the validated `stock_on_hand`.
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- name: warehouse_location
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description: |
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The tracking designation code for the storage facility. Values are standardized to UPPERCASE
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with missing or blank fields defaulting to 'Unknown'.
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tests:
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- not_null
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- name: store_id
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description: |
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The associated brick-and-mortar retail location identifier. Converted to an integer data type,
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with missing or blank spaces coerced to NULL.

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