SCD Type 0: Retain Original – Immutable Dimensions in Data Warehousing

In the nuanced world of data warehouse design, effectively managing changing dimension attributes is a fundamental challenge that data engineers face daily. Among the various Slowly Changing Dimension (SCD) methodologies, Type 0 stands apart with its uncompromising simplicity: never change the original values, regardless of real-world changes. This “retain original” approach might seem basic at first glance, but its applications and implications are surprisingly sophisticated and far-reaching.

SCD Type 0 is defined by a single, unwavering principle: once a dimensional attribute value is recorded, it remains unchanged for the entire lifecycle of the dimension record. This steadfast immutability distinguishes Type 0 from other SCD types that accommodate change in various ways.

The defining characteristics that make Type 0 unique in the SCD taxonomy include:

• Complete Immutability: Values are written once and never modified
• No Historical Tracking: No mechanism for recording changes, as changes simply aren’t allowed
• Original Values Preserved: The first recorded value is maintained permanently
• Change Rejection: Updates to the source system are intentionally ignored
• Static Truth Definition: The dimension represents a specific point-in-time reality that never evolves

To illustrate the concept, consider a customer dimension where certain attributes are implemented as Type 0:

CustomerDimension {
    CustomerKey (PK)
    CustomerID
    FirstName                  // Type 0: Never changes
    LastName                   // Type 1: Can be updated
    DateOfBirth                // Type 0: Never changes
    OriginalCreditScore        // Type 0: Score at first application
    CurrentCreditScore         // Type 1: Updated score
    CustomerAddress            // Type 2: Full history tracked
    RegistrationDate           // Type 0: Never changes
    CustomerSegment            // Type 1: Can be updated
}

In this example, FirstName, DateOfBirth, OriginalCreditScore, and RegistrationDate are Type 0 attributes—they will never change regardless of updates in source systems.

While the concept is straightforward, the strategic application of Type 0 requires careful consideration. Here are the primary scenarios where Type 0 attributes deliver significant value:

Some attributes are immutable by their very nature:

• Birth Date: A person’s date of birth doesn’t change
• Creation Timestamps: When an entity was first created
• Original Documentation: Initial application forms or registration details
• Physical Constants: Properties that cannot change (manufacturing date, serial number)

Retaining initial values provides critical baseline reference points:

• Initial Credit Scores: Original creditworthiness at account opening
• Starting Measurements: Initial health metrics for medical patients
• Baseline Configurations: Original settings of equipment or systems
• Initial Classifications: Original risk categories or segments

Some attributes should remain constant for analytical consistency:

• Cohort Assignments: Customer acquisition channels or cohort groupings
• Geographical Origins: Original store where customer first purchased
• Initial Categorizations: Original product category assignments
• Entry Point Tracking: Original touchpoint in the customer journey

Regulatory needs often require preservation of original values:

• KYC Information: Original identity verification data
• Legal Agreements: Terms accepted at registration
• Regulatory Filings: Original submitted information
• Contractual Terms: Original agreement parameters

Implementing Type 0 attributes requires specific technical approaches to ensure their immutability is maintained throughout the data pipeline.

The following patterns ensure Type 0 attributes maintain their immutability:

During the first load of a dimension record:

1. Extract the attribute from the source system
2. Perform any necessary cleansing or standardization
3. Load the value into the dimension table

When processing updates to existing dimension records:

1. Load all non-Type 0 attributes normally
2. For Type 0 attributes, use one of these approaches:
   • Explicitly exclude Type 0 columns from update statements
   • Read existing values and write them back unchanged
   • Use conditional logic to only update non-Type 0 attributes

-- Initial dimension load
INSERT INTO DimCustomer (
    CustomerID,
    FirstName,  -- Type 0
    LastName,   -- Type 1
    DateOfBirth,  -- Type 0
    CustomerAddress,  -- Type 2
    RegistrationDate  -- Type 0
)
SELECT
    CustomerID,
    FirstName,
    LastName,
    DateOfBirth,
    Address,
    RegistrationDate
FROM StageCustomer;

-- Subsequent Type 1 updates (preserving Type 0 attributes)
UPDATE DimCustomer
SET
    LastName = S.LastName
FROM StageCustomer S
WHERE DimCustomer.CustomerID = S.CustomerID
AND (DimCustomer.LastName <> S.LastName);

-- Type 2 updates handled separately with new rows

When incorporating Type 0 attributes into your dimensional model:

• Clearly Document Type 0 Attributes: Ensure all team members know which attributes should never change
• Consider Column Grouping: Group Type 0 attributes together for clarity
• Use Naming Conventions: Prefixes like “Original_” can signal Type 0 attributes
• Apply Database Constraints: Consider using triggers or constraints to prevent updates

Despite the “never change” rule, real-world scenarios sometimes require strategies for addressing source system modifications:

• Source Error Corrections: Have a governed exception process for correcting genuine data entry errors
• Default Value Handling: Establish policies for handling initially null values that later receive data
• Data Quality Monitoring: Implement alerts for attempted changes to Type 0 attributes
• Change Logging: Consider logging attempted changes for audit purposes even if not applied

To fully appreciate the role of Type 0, it’s valuable to contrast it with other SCD methodologies:

Aspect	Type 0 (Retain Original)	Type 1 (Overwrite)
Value Changes	Never change	Overwrite with new values
Historical Data	Original values only	Current values only
Query Complexity	Simple	Simple
Storage Impact	Minimal	Minimal
Implementation Complexity	Very low	Low
Use Case	Immutable properties	Current state only matters

Aspect	Type 0 (Retain Original)	Type 2 (Add New Row)
Value Changes	Never change	New row with new values
Historical Data	Original values only	Complete history
Query Complexity	Simple	Moderate
Storage Impact	Minimal	Significant
Implementation Complexity	Very low	Moderate to high
Use Case	Immutable properties	Full historical tracking needed

Aspect	Type 0 (Retain Original)	Type 6 (Hybrid)
Value Changes	Never change	Complex change tracking
Historical Data	Original values only	Complete + current flags
Query Complexity	Simple	Complex
Storage Impact	Minimal	Significant
Implementation Complexity	Very low	High
Use Case	Immutable properties	Maximum flexibility needed

In practice, most dimensions contain attributes handled with different SCD types. Type 0 often complements other SCD strategies:

A common pattern preserving original values while maintaining current state:

CustomerDimension {
    CustomerKey (PK)
    CustomerID
    Original_CreditScore      // Type 0: Never changes
    Current_CreditScore       // Type 1: Updated with current value
    Original_Address          // Type 0: First address
    Current_Address           // Type 1: Current address
    Registration_Date         // Type 0: Never changes
}

This approach enables both “as of now” and “as of registration” analysis without the complexity of Type 2.

When full history is needed for some attributes but original values must be preserved:

CustomerDimension {
    CustomerKey (PK)
    CustomerID
    Original_Segment          // Type 0: Original segment
    Customer_Segment          // Type 2: Full history of segments
    EffectiveDate
    ExpirationDate
    Current_Flag
}

This combination is powerful for cohort analysis that needs to compare current state against original classification.

Type 6 (the “hybrid” approach combining Types 1, 2, and 3) often incorporates Type 0 attributes as reference points:

ProductDimension {
    ProductKey (PK)
    ProductID
    LaunchDate               // Type 0: Never changes
    CurrentProductName       // Type 1: Current name
    ProductName              // Type 2: Historical names
    OriginalCategory         // Type 0: Original category
    PreviousCategory         // Type 3: Previous category
    CurrentCategory          // Type 1: Current category
    EffectiveDate
    ExpirationDate
    Current_Flag
}

The principle of immutability in Type 0 dimensions aligns well with modern data architecture patterns:

In Data Vault modeling, Type 0 attributes align with:

• Hub Records: Business keys in hubs are inherently Type 0
• Historical Satellites: Original state satellites preserve first values
• Reference Satellites: Static classification satellites

Type 0 complements event sourcing by:

• Preserving original event properties
• Maintaining immutable event records
• Supporting event replay with original values

In data lake architectures, Type 0 principles manifest as:

• Immutable raw data zones
• Original record preservation
• Bronze layer immutability

Type 0 concepts apply to streaming scenarios through:

• Original event retention
• Immutable event logs (like Kafka)
• First-seen attribute extraction

While Type 0 is conceptually simple, its implementation has notable performance and storage implications:

Type 0 attributes are storage-efficient because:

• No historical versions needed
• No additional tracking columns required
• No temporal overlap considerations

Queries involving Type 0 attributes benefit from:

• No temporal filtering required
• Simpler join conditions
• No current_flag or date range predicates
• Better index utilization

Type 0 simplifies data lineage tracking by:

• Eliminating change tracking complexity
• Providing consistent reference points
• Simplifying historical reconstructions

To illustrate a practical implementation of Type 0, consider a financial services customer dimension:

DimAccount {
    AccountKey (PK)
    AccountID (Natural Key)
    
    // Type 0 Attributes - Never Change
    OpenDate
    OriginalProduct
    OriginalBranch
    OriginalCreditLimit
    OriginalInterestRate
    SourceSystem
    
    // Type 1 Attributes - Current Values Only
    CurrentCreditLimit
    CurrentInterestRate
    AccountStatus
    
    // Type 2 Attributes - Full History
    OwnershipType
    AccountManager
    RiskCategory
    EffectiveDate
    ExpirationDate
    Current_Flag
}

This design enables crucial financial analytics:

• Compare original vs. current interest rates across cohorts
• Analyze account performance based on original branch
• Track risk category changes while retaining original classifications
• Measure credit limit increases from original baselines

To successfully implement Type 0 attributes, consider these best practices:

Clearly identify Type 0 attributes in:

• Data dictionaries
• ETL specifications
• Data governance policies
• Data warehouse documentation

Confirm immutability requirements through:

• Business stakeholder validation
• Regulatory requirement review
• Use case confirmation
• Change scenario analysis

Establish processes for the rare cases when Type 0 attributes might need correction:

• Data quality remediation procedures
• Executive approval processes
• Change audit logging
• Updated value annotations

Implement safeguards to protect Type 0 integrity:

• Data quality monitoring
• Change attempt alerting
• ETL validation rules
• Database triggers or constraints

Adopt naming standards that signal immutability:

• Prefix: “Original_” or “Initial_”
• Suffix: “_At_Registration”
• Documentation tags in metadata repositories
• Comments in DDL scripts

In the complex landscape of data warehouse design, SCD Type 0 represents a powerful reminder that sometimes the simplest approach delivers the most value. By permanently retaining original values for selected attributes, organizations gain multiple benefits:

• Analytical Consistency: Fixed reference points that never change
• Implementation Simplicity: The easiest SCD type to implement
• Query Performance: Efficient retrieval without temporal complexities
• Business Clarity: Clear representation of original states

While not suitable for all attributes, Type 0 plays a crucial role in a comprehensive dimensional modeling strategy. The “retain original” approach provides the foundation upon which other SCD types can build, enabling both simplified current-state reporting and sophisticated historical analysis.

For data engineers and architects designing data warehouses, mastering the strategic application of Type 0 attributes is an essential skill that enhances both model integrity and analytical capabilities. In a data landscape constantly navigating the challenges of change, sometimes the most powerful approach is to anchor certain elements in their original, unchanging state.

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Keywords: SCD Type 0, Slowly Changing Dimensions, retain original, immutable dimensions, data warehouse design, dimensional modeling, original values, reference data, data warehousing, ETL processing, immutability, Kimball methodology, customer dimension, data integration, historical data

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