Handling Slowly Changing Dimensions (SCD) with Delta Lake on Databricks

Dimension attributes in your data warehouse don’t stay constant – customer tiers change; suppliers shift regions, products evolve, and cost centers get reorganized. Handling these changes accurately is essential for reporting, ML features, and auditability. 

Delta Lake makes Slowly Changing Dimensions (SCD) far more manageable with ACID transactions, MERGE operations, schema enforcement, time-travel, and now AUTO CDC. 

This guide covers: 

• SCD Type 1 
• SCD Type 2 
• AUTO CDC 
• AUTO CDC FROM SNAPSHOT 
• Example code for Databricks users 

Why SCD Matters in Modern Data Platforms 

A supply chain, finance, or customer analytics system must maintain both: 

• Current state (e.g., customer’s latest category) 
• Historical state (e.g., what category they were in last year) 

Traditional ETL pipelines require complex logic and heavy orchestration.
Delta Lake simplifies this using: 

• Transactional MERGE 
• Optimized Delta storage 
• Time-travel for validation & rollback 
• Efficient CDC tracking 

SCD Type 1 – Overwrite Changes (No History) 

SCD1 is used when you only care about the latest value. 

Use Case Examples 

• Product name correction 
• Customer phone number update 
• SKU standardization 

Delta Lake Example (SQL) 

PySpark Equivalent 

Why It Works Well 

• Fastest method 
• No date fields needed 
• Perfect when history is irrelevant 

SCD Type 2 – Maintain Full History (Track Changes) 

SCD2 captures every version of a dimension record with start/end dates and active flag. 

Common Uses 

• Customer tiers 
• Supplier status changes 
• Pricing or cost changes 
• Region hierarchy modifications 

Table Structure Example 

Delta Lake SCD2 Merge Logic (SQL) 

PySpark Version 

AUTO CDC – Automated Change Data Capture  

Databricks can automatically detect inserts, updates, and deletes from any Delta table without manually writing MERGE logic. 

How It Works 

• Tracks row-level changes 
• Generates CDC manifests 
• Ideal for Bronze → Silver pipelines 
• Significantly reduces code complexity 

Enable AUTO CDC

Consume CDC in Delta Live Tables

Use Case 

• Real-time dimension sync 
• Continuous SCD1/SCD2 ingestion 
• Lower maintenance compared to MERGE pipelines 

AUTO CDC FROM SNAPSHOT – Generate CDC Without a Source CDC Feed 

Sometimes the upstream system sends only full snapshots, not CDC.
Databricks fills the gap: 

• Automatically computes diffs between snapshots 
• Produces INSERT, UPDATE, DELETE events 
• Ideal for ERP extracts, CRM dumps, legacy systems 

Example 

Benefits 

• No need to write diff logic 
• No MERGE scripting 
• Efficient for large daily snapshots 
• Supports downstream SCD2 tables 

Choosing Between SCD Types & AUTO CDC

Simbus Tech Expertise 

Simbus Tech helps Databricks customers: 

• Build scalable SCD1/SCD2 architectures 
• Implement AUTO CDC & Delta Live Tables 
• Optimize Delta tables for cost & performance 
• Develop Medallion (Bronze → Silver → Gold) pipelines 
• Implement Unity Catalog governance for dimensions & facts 

If you’re modernizing your data engineering workflows, Simbus provides engineering leadership and implementation support tailored for Databricks workloads. 

Need help designing or optimizing SCD pipelines on Databricks? 

Connect with the Simbus Tech Databricks Engineering Team. https://simbustech.com/services/