Slowly Changing Dimensions

Slowly Changing Dimensions keep business history trustworthy. Job titles shift, addresses change, consent preferences update, and product attributes evolve. Analytics must reflect what was true when an event happened and what is true now. SCDs provide that continuity without turning every report into detective work.

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What SCDs Are and Why They Matter

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Dimensions describe the who, what, and where behind your facts. An SCD defines how changes to those descriptive attributes are captured over time. The goal is simple: answer questions using the value that applied at the time of the fact, while still allowing “current snapshot” views when you need them.

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Common SCD Types

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Pick the lightest mechanism that preserves meaning. Keep the rules explicit and consistent.

• Type 0: Retain the original value forever. Good for “first seen” facts like initial signup date.
• Type 1: Overwrite in place to correct data quality issues, such as a misspelled name.
• Type 2: Add a new row for business-relevant changes and track effective dates plus a “current” flag.
• Type 3: Keep current and previous values in the same row for limited history.
• Hybrids (5/6): Combine the above to expose both fast “current” lookups and full historical analysis.

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Where Hand-Coded SCDs Struggle

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On paper, SCDs are straightforward. In practice, handwritten patterns tend to multiply and drift. A small schema tweak can trigger edits across DDL, ELT, tests, and documentation. Teams end up debugging overlapping date ranges, inconsistent hash logic, and unclear historization rules.

• Change detection pitfalls: null handling, excluded columns, and comparisons by hash vs column.
• Upsert complexity: missed expirations or duplicate history rows when logic varies by table.
• Key alignment: surrogate, business, and hash keys that differ across environments.
• Audit gaps: no single source of truth for which attributes historize and why.

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Agile Teams Need Different SCD Muscle

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Short iterations and frequent releases expose every inconsistency. To move quickly without eroding trust, SCD behavior must be declared once, promoted like code, and applied uniformly. That means versioned metadata, generated SQL or Spark code, impact analysis before promotion, and documentation that reflects what actually shipped.

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Metadata-Driven Automation

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Treat SCD rules as configuration rather than custom code. Define business keys, historized attributes, detection policy, and late-arriving behavior in one place. Let generators create the DDL, ELT, tests, and human-readable documentation. The result is faster delivery and fewer edge-case surprises.

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Benefits at a glance:

• Define once, generate everywhere across dev, test, and prod.
• Flip Type 1 vs Type 2 at the column level without rewriting pipelines.
• Standardize surrogate and hash keys to keep joins stable.
• Ship docs and lineage from the same metadata used to generate code.
• Run impact analysis to see what changes before you promote.

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How BimlFlex Implements SCDs

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BimlFlex operationalizes this approach so teams can focus on intent, not plumbing.

• Per-column SCD settings, including Type 1 vs Type 2, null handling, and late-arriving rules.
• Generated ELT for SQL and Spark with effective-from/to dates, current flags, and soft delete semantics.
• Hash-based comparisons for efficient change detection across many attributes.
• Consistent surrogate key policies aligned with business keys.
• Versioned metadata, promotion gates, and rollback.
• Automatic documentation and lineage tied to the deployed release.

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Design Choices and Good Habits

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Make history useful and easy to query.

• Separate “current” views from full history to keep everyday queries simple.
• Agree with business owners on which attributes historize and why.
• Add column-level assertions to catch unexpected churn in Type 2 attributes.
• Monitor soft deletes and change rates for anomalies.
• Use PIT or bridge tables when very large dimensions need fast point-in-time analysis.

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Example: Core Type 2 Pattern

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This sketch shows the heart of a Type 2 update: expire the current row when a meaningful change is detected, then insert the new version: 

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-- Detect changes by hash, expire the current row
UPDATE d
SET IsCurrent = 0, EffectiveTo = SYSUTCDATETIME()
FROM dbo.DimEmployee d
JOIN stg.Employee s ON d.EmployeeBK = s.EmployeeBK AND d.IsCurrent = 1
WHERE HASHBYTES('SHA2_256', CONCAT_WS('|', d.Title, d.Department, d.ManagerID))
   <> HASHBYTES('SHA2_256', CONCAT_WS('|', s.Title, s.Department, s.ManagerID));

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Platform specifics vary, but automated frameworks generalize this pattern and harden it with tests.

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Quick Answers to Common Objections

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“Type 2 bloats storage.” Storage is cheaper than lost history; use hash diffs and PIT tables to keep queries fast.

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“We can hand-code it faster.” Speed fades as schemas evolve; declarative rules avoid ripple edits across pipelines and docs.

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“Audit only needs current values.” Business questions rarely do; trustworthy trends depend on preserved context.

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Summary & Next Steps

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SCDs sit at the intersection of business truth and technical implementation. Manual patterns slow teams down as change accelerates. Declaring SCD behavior in metadata and generating the rest keeps delivery quick, reviews focused, and history reliable.

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If you want to see column-level SCD settings, generated ELT, and versioned promotion in action, request a BimlFlex demo.