Slowly Changing Dimensions (Verbose)

Type 1: Overwrite History

When: Not important (contact info) Implementation:

UPDATE dim_customer 
SET email = 'newemail@example.com'
WHERE customer_id = 123;

Pros: Simple, minimal storage
Cons: Lose history, hard to debug

Type 2: Add New Row (Full History)

When: Important for analysis (customer value, status) Implementation:

-- Deactivate current record
UPDATE dim_customer 
SET end_date = '2026-04-10', is_current = FALSE
WHERE customer_id = 123 AND is_current = TRUE;

-- Insert new record
INSERT INTO dim_customer (
  customer_key, customer_id, name, segment,
  start_date, end_date, is_current
) VALUES (
  999, 123, 'John Smith', 'Premium',
  '2026-04-11', NULL, TRUE
);

Query with SCD Type 2:

-- How much revenue from each segment at that time?
SELECT 
  dc.segment,
  SUM(fs.revenue)
FROM fact_sales fs
JOIN dim_customer dc 
  ON fs.customer_key = dc.customer_key
  AND fs.date_key BETWEEN dc.start_date AND dc.end_date
WHERE fs.date >= '2026-01-01'
GROUP BY dc.segment

Pros: Full history, good for trend analysis
Cons: More rows, complex queries

Type 3: Add Columns (Current + Previous)

When: Need recent history (current + previous)

ALTER TABLE dim_customer ADD previous_segment VARCHAR;
ALTER TABLE dim_customer ADD change_date DATE;

-- Update
UPDATE dim_customer
SET previous_segment = segment,
    segment = 'Premium',
    change_date = '2026-04-11'
WHERE customer_id = 123;

Pros: Simple, tracks previous
Cons: Only 2 versions (current + previous)

Type 4: Slowly Changing Facts

When: Fact attributes change (price changes)

-- Store price at transaction time
CREATE TABLE fact_sales (
  sale_id INT,
  product_id INT,
  price_sold DECIMAL,  -- Price at sale time
  current_price DECIMAL  -- Price today (for comparison)
);

Implementation Patterns

ELT Pattern (Load Then Transform):

-- Load raw customer data
INSERT INTO stg_customer
SELECT * FROM raw_customer_data;

-- Identify changes
SELECT * FROM stg_customer sc
LEFT JOIN dim_customer dc 
  ON sc.customer_id = dc.customer_id AND dc.is_current
WHERE sc.segment != dc.segment
  OR sc.status != dc.status;

-- Apply SCD Type 2
-- Deactivate old, insert new

Decision Matrix

Attribute	Change Frequency	Business Impact	SCD Type
Email	Rare	Low	1
Segment	Few times/year	High	2
Address	Few times/year	Medium	2
Phone	Rare	Low	1
Salary	Annual	High	2
Status	Frequent	High	2

Performance Considerations

Type 1 (Simple):

• No extra rows
• Fast inserts

Type 2 (Complex):

• 2x-10x more rows
• Slower dimension lookups
• Requires SCD join logic

Optimization:

-- Denormalize current values
CREATE TABLE dim_customer_current AS
SELECT * FROM dim_customer WHERE is_current = TRUE;
-- Use this for most queries, fallback to full for historical

Common Mistakes

❌ Type 2 for everything → Bloats dimensions
✅ Use Type 1 for unimportant fields

❌ Missing end_date/is_current flag → Complex queries
✅ Always add status columns

❌ SCD applied to fact table → Breaks grain
✅ Apply to dimensions only