CDC vs SCD vs Incremental Refresh in Power BI - Daily Diary vs Updated Textbook vs Quick Notes

📊 CDC vs SCD vs Incremental Refresh in Power BI

Daily Diary vs Updated Textbook vs Quick Notes - The Ultimate Guide!

💡The Big Idea

Imagine you're keeping track of your friends' information...

🗓️ Daily Diary (CDC): You write down every single change as it happens - "Sarah got a new phone number today!"

📚 Updated Textbook (SCD): You keep both old and new information - "Sarah's old number was X, now it's Y"

📝 Quick Notes (Incremental Refresh): You only update the parts that changed since yesterday!

These three approaches help Power BI handle data changes efficiently. Let's explore how each one works and when to use them!

🔍What Are These Refresh Strategies?

🗓️ Change Data Capture (CDC)

What it does: Tracks every single change in real-time, like a security camera that never stops recording!

Best for: When you need to know exactly what changed and when

📚 Slowly Changing Dimensions (SCD)

What it does: Keeps historical versions of data, like keeping all your old school yearbooks!

Best for: When you need to compare how things looked before vs. now

📝 Incremental Refresh

What it does: Only updates the new or changed parts, like adding new pages to a book instead of rewriting the whole thing!

Best for: Large datasets where full refresh takes too long

🏫Real-World Analogy: The School Records System

🏫 Imagine Your School's Student Records System...

🗓️ CDC Approach - The Daily Log Book:

The school secretary writes down EVERY change in a special log book:

  • "9:15 AM - John Smith changed his address"
  • "10:30 AM - Sarah Johnson got a new phone number"
  • "2:45 PM - Mike Davis updated his emergency contact"

📚 SCD Approach - The Archive System:

They keep ALL versions of student records:

  • John's address from 2020, 2021, 2022, and current 2025
  • Sarah's phone numbers: old one, new one, and when each was valid
  • Mike's emergency contacts: mom (2020-2023), dad (2023-present)

📝 Incremental Refresh - The Smart Update System:

Instead of checking every student's record daily, they only:

  • Check records that were modified this week
  • Add new student enrollments
  • Skip unchanged records from previous years

⚙️Core Concepts Breakdown

1

🗓️ Change Data Capture (CDC)

How it works: Database automatically logs every insert, update, and delete operation

Key benefit: Real-time change tracking with minimal performance impact

2

📚 Slowly Changing Dimensions (SCD)

Type 1: Overwrite old data (like updating your profile picture)

Type 2: Keep history (like keeping all your old profile pictures with dates)

Type 3: Keep current + previous (like showing "Current Address" and "Previous Address")

3

📝 Incremental Refresh

Archive Period: Old data that never changes (like last year's test scores)

Refresh Period: Recent data that might change (like this month's attendance)

💻Practical Power BI Implementation

🗓️ CDC in Power BI:

-- Enable Change Tracking (CDC) at the database level ALTER DATABASE YourDatabase SET CHANGE_TRACKING = ON (CHANGE_RETENTION = 2 DAYS, AUTO_CLEANUP = ON); -- Enable Change Tracking on the Students table ALTER TABLE Students ENABLE CHANGE_TRACKING WITH (TRACK_COLUMNS_UPDATED = ON);

📚 SCD Type 2 in Power Query:

let // Step 1: Connect to SQL Server database Source = Sql.Database("server", "database"), // Step 2: Add a ValidFrom column with the current date and time AddValidFrom = Table.AddColumn( Source, "ValidFrom", each DateTime.LocalNow() ), // Step 3: Add a ValidTo column with a far-future date AddValidTo = Table.AddColumn( AddValidFrom, "ValidTo", each #datetime(9999, 12, 31, 0, 0, 0) ), // Step 4: Add an IsCurrent column set to true AddIsCurrent = Table.AddColumn( AddValidTo, "IsCurrent", each true ) in AddIsCurrent

📝 Incremental Refresh Setup:

Pro Tip: In Power BI Dataset Settings:

  1. Add RangeStart and RangeEnd parameters
  2. Filter your date column using these parameters
  3. Configure refresh policy (e.g., "Archive 2 years, Refresh 7 days")

🌟Real-World Example: E-commerce Order System

📦 Scenario: Online Store Order Tracking

🗓️ Using CDC:

Track every order status change: "Order #12345 changed from 'Processing' to 'Shipped' at 2:30 PM"

Perfect for: Real-time dashboards showing live order status

📚 Using SCD Type 2:

Keep full history: Order #12345 was "Processing" (Jan 1-3), then "Shipped" (Jan 3-5), then "Delivered" (Jan 5-present)

Perfect for: Analyzing how long orders spend in each status

📝 Using Incremental Refresh:

Archive orders older than 1 year, refresh only last 30 days of orders

Perfect for: Large order datasets where full refresh takes hours

🚀Why Are These Strategies So Powerful?

Strategy Speed ⚡ Storage 💾 History 📊 Complexity 🧩
CDC Very Fast Minimal Change Log Only Medium
SCD Type 2 Medium High Complete History High
Incremental Refresh Fast Optimized All Data Low

🎯 Key Benefits:

  • Performance: Faster refreshes mean happier users!
  • Storage Efficiency: Only update what's needed
  • Data Accuracy: Track changes without losing information
  • Historical Analysis: Compare past vs. present trends

🎓Learning Path: Start Your Journey!

1

🚶‍♂️ Beginner Level

Start with Incremental Refresh - it's the easiest to implement and gives immediate benefits!

Practice: Set up incremental refresh on a date table

2

🚴‍♂️ Intermediate Level

Learn SCD Type 1 (simple overwrites) before moving to Type 2

Practice: Create a customer dimension with address changes

3

🏃‍♂️ Advanced Level

Master CDC and SCD Type 2 for complex scenarios

Practice: Build a complete audit trail system

4

🧙‍♂️ Expert Level

Combine strategies! Use CDC + Incremental Refresh for optimal performance

Practice: Design hybrid solutions for enterprise scenarios

📋Quick Reference Cheat Sheet

🗓️ Choose CDC When:

  • Real-time tracking needed
  • Minimal storage impact
  • Change log is sufficient
  • Database supports CDC

📚 Choose SCD When:

  • Historical analysis required
  • Point-in-time reporting needed
  • Compliance demands audit trail
  • Dimensional modeling approach

📝 Choose Incremental When:

  • Large datasets (millions+ rows)
  • Long refresh times
  • Time-based partitioning possible
  • Simple implementation preferred

🚀 Ready to Master Power BI Data Refresh?

You've learned the core concepts! Now it's time to practice and implement these strategies in your own projects.

🎯 Your Next Steps:

  1. Start Simple: Implement incremental refresh on your largest dataset
  2. Practice SCD: Create a customer dimension with historical tracking
  3. Explore CDC: Set up change tracking on a transactional table
  4. Combine Strategies: Use different approaches for different tables

📝 Article by: Nishant Chandravanshi

Making complex Power BI concepts simple and fun to learn!