Mastering Slowly Changing Dimensions (SCD) in Databricks: A Guide for Data Engineers

In the fast-evolving world of data engineering, managing and tracking changes in dimension data over time is a critical skill. Enter?Slowly Changing Dimensions (SCD)—an essential concept for building reliable data pipelines in data warehousing.

With the power of?Databricks,?PySpark, and?Delta Lake, handling SCD becomes more efficient and scalable. Let’s explore how you can implement SCD types to build robust solutions for your data projects.

What are Slowly Changing Dimensions (SCD)?

SCD refers to the methods used to handle changes in dimension data while ensuring data integrity. These methods help businesses:

• Maintain historical accuracy.
• Adapt to changing records over time.
• Support advanced analytics and reporting.

In Databricks, leveraging?Delta Lake?enhances this process with features like:

• Upserts: Seamlessly merge incoming updates into existing records.
• Time Travel: Retrieve historical versions of data for debugging or reporting.
• Scalability: Handle massive datasets with optimized performance.

SCD Types Explained

Two of the most common types of SCD are?Type 1?and?Type 2:

SCD Type 1: Overwrite Existing Data

• Use Case: When historical data is not required, and only the latest state of the record matters.
• Behavior: Updates overwrite the existing data, making the change immediate and irreversible.

SCD Type 2: Maintain History

• Use Case: When tracking the history of changes is critical for business intelligence.
• Behavior: Changes are stored as new rows, and previous rows are marked inactive. This method keeps a full historical record of all changes.

Why Use Databricks for SCD?

Databricks, paired with?Delta Lake, simplifies SCD implementation:

1. Declarative APIs: Use PySpark for clear, concise transformations.
2. Delta Tables: Handle updates and deletes effortlessly, thanks to its ACID-compliant architecture.
3. Versioning: Delta Lake’s time travel feature lets you query data at specific points in history.

How to Implement SCD in Databricks

Here’s a quick breakdown of how you can start implementing SCD:

SCD Type 1:

1. Create a Delta table for your dimension data.
2. Use the?MERGE?operation to overwrite existing records with incoming updates.
3. Leverage PySpark to efficiently process large datasets.

SCD Type 2:

1. Add metadata columns (start_date,?end_date,?is_current) to track the lifecycle of each record.
2. Use Delta Lake to: Mark existing records as inactive. Insert new records for updated data.
3. Maintain a complete audit trail by leveraging Delta’s versioning features.

Notebook code and output

Key Takeaways

1. Understand your business requirements: Choose SCD Type 1 for simplicity or Type 2 for detailed historical tracking.
2. Leverage Databricks’ ecosystem: Use Delta Lake for streamlined, scalable implementations.
3. Think future-proof: Incorporate metadata and time travel capabilities for long-term data accuracy.
4. For SCD Type 1 it spawned 18 Spark Jobs and for SCD Type 2 it spawned 26 Spark Jobs, consider this point while implementing with respect to the performance

"What challenges have you faced with SCD?”

please share your thoughts or reach out for discussions.

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